Reviewerly/exHarmony huggingface dataset:
Towards a correct by construction design of complex systems: The MBSS approach

We present in this paper the Model Based System Synthesis (MBSS) approach for the design of complex systems that are correct by construction. Where the usual Model Based System Engineering (MBSE) approach offers formalisms and tools to represent a candidate system, to analyze it, to simulate it and even to optimize it, MBSS proposes to represent the global design problem using a problem representation language and then to solve it by using adapted synthesis tools producing one or several solutions necessarily satisfying the expressed requirements. The two approaches are therefore complementary; the MBSS being more adapted to the preliminary design and system integration stages. After presenting the different categories of problems encountered in system design (sizing, configuration, allocation, architecture generation), MBSS and MBSE will be positioned in relation to each other. The main concepts of MBSS will be detailed in order to understand the specific representation needs of the approach. The structural and behavioral notions related to the sub-definite systems will be explained as well as the links to be established with the functional and non-functional requirements. The approach is illustrated using the DEPS design problem specification language and the DEPS Studio modeling and solving tool on a system design case study. The DEPS language combines structural modeling features specific to object-oriented principles and ontology definition capabilities for engineers with problem specification features from constraint programming. DEPS Studio is an integrated modeling and solving environment designed to model and resolve system synthesis problems. It allows the engineer to edit, compile, debug and solve problems expressed in DEPS. It integrates a mixed constraint programming solver. The approach can be applied on physical systems, software intensive or mixed systems (embedded or cyber-physical).
Digital Authentication and Dyslexia: A Survey of the Problems and Needs of Dyslexia People

It might be expected that people with dyslexia would have difficulties with password creation and maintenance, given their difficulties with language, often with spelling and correctly ordering elements. However, very little research has investigated this issue. Therefore, an online survey was conducted with 69 individuals with dyslexia and a matched sample of 90 non-dyslexic individuals. The survey found that the dyslexic individuals reported significantly more difficulty in creating and remembering passwords than the non-dyslexic individuals, and significantly more difficulties with CAPTCHAs and pattern authentication systems, but not with biometric authentication, including face recognition. The results show there is a great need to address the problems which individuals with dyslexia face with passwords and other authentication systems.
An Improved Chicken Swarm Optimization Algorithm for Feature Selection

Abstract In recent years, feature selection is becoming more and more important in data mining. Its target is that reduce the dimensionality of the datasets while at least maintaining the classification accuracy. There are some researches about chicken swarm optimization algorithm (CSO) applied to feature selection, the effect is extraordinary compared with traditional swarm intelligence algorithms. However, there is a complex search space in the challenging task feature selection, the CSO algorithm still has a default that quickly gets stuck in the local minimum problem. An improved chicken swarm optimization algorithm (ICSO) is proposed in this paper, which introduces the Levy flight strategy in the hen location update strategy and the nonlinear strategy of decreasing inertial weight in the chick location update strategy to increase the global search ability and avoid getting stuck in the local minimum problem. Compared with the other three algorithms on eighteen UCI datasets shows that the ICSO algorithm can greatly reduce the redundant features while ensuring classification accuracy.
Causal Probabilistic Based Variational Autoencoders Capable of Handling Noisy Inputs Using Fuzzy Logic Rules

Researchers and engineers may use inferential logic and/or fuzzy logic to solve real-world causal problems. Inferential logic uses probability theories, while fuzzy logic uses its membership functions and set theories to process uncertainty and fuzziness of the events. To benefit from both logics, some researchers in the past tried to create probabilistic fuzzy logic (PFL). Deep Learning algorithms (DLs) with their incredible achievements such as very high precision results in some specific tasks are at the center of the weak AI. However, DLs fail when it comes to causal reasoning. In order to equip Deep Learning algorithms (DLs) with reasoning capabilities, one solution would be to integrate non-classical logics such as PFL with DLs. In this paper, we will demonstrate the first step toward creating a deep causal probabilistic fuzzy logic architecture capable of reasoning with missing or noisy datasets. To do so, the architecture uses fuzzy theories, probabilistic theories, and deep learning algorithms such as causal effect variational autoencoders.
Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking

Relation extraction (RE) is an important natural language processing task that predicts the relation between two given entities, where a good understanding of the contextual information is essential to achieve an outstanding model performance.Among different types of contextual information, the auto-generated syntactic information (namely, word dependencies) has shown its effectiveness for the task.However, most existing studies require modifications to the existing baseline architectures (e.g., adding new components, such as GCN, on the top of an encoder) to leverage the syntactic information.To offer an alternative solution, we propose to leverage syntactic information to improve RE by training a syntax-induced encoder on auto-parsed data through dependency masking.Specifically, the syntax-induced encoder is trained by recovering the masked dependency connections and types in first, second, and third orders, which significantly differs from existing studies that train language models or word embeddings by predicting the context words along the dependency paths.Experimental results on two English benchmark datasets, namely, ACE2005EN and SemEval 2010 Task 8 datasets, demonstrate the effectiveness of our approach for RE, where our approach outperforms strong baselines and achieve state-ofthe-art results on both datasets.
Simulation and Stabilization of a Custom-Made Quadcopter in Gazebo Using ArduPilot and QGroundControl

Simulation has been an integral part of the research and development domain in the current era. Especially in the field of aerial robotics, the introduction of simulation techniques and several simulation platforms has been a great breakthrough and has given a lot of options for the researcher to reiterate the designing and functioning of the final model without building it in real life which completely strikes off the disadvantage of financial incur of building the model mainly for testing purposes. In this paper, we propose a systematic approach through which one can design and simulate their own custom quadcopter in a platform like Gazebo with the help of ArduPilot flight stack and QGroundControl software and then stabilize it in the simulation with the help of the LiftDragPlugin. An experimental set-up was designed and carried out to prove that the quadcopter was stable before continuing to build the physical model. The main objective of this paper is to demonstrate the idea of building a personalized quadcopter and integrating it with the major simulation platforms like Gazebo and ArduPilot to make the vehicle work and fly stably. This work is aimed to inspire the creative aerial robotics researchers to think outside the box and to motivate them to come up with their own vehicle for their applications rather than depending on the already established group of simulated vehicles available.
CML: A Contrastive Meta Learning Method to Estimate Human Label Confidence Scores and Reduce Data Collection Cost

Deep neural network models are especially susceptible to noise in annotated labels. In the real world, annotated data typically contains noise caused by a variety of factors such as task difficulty, annotator experience, and annotator bias. Label quality is critical for label validation tasks; however, correcting for noise by collecting more data is often costly. In this paper, we propose a contrastive meta-learning framework (CML) to address the challenges introduced by noisy annotated data, specifically in the context of natural language processing. CML combines contrastive and meta learning to improve the quality of text feature representations. Meta-learning is also used to generate confidence scores to assess label quality. We demonstrate that a model built on CML-filtered data outperforms a model built on clean data. Furthermore, we perform experiments on deidentified commercial voice assistant datasets and demonstrate that our model outperforms several SOTA approaches.
A Methodical Study on Blockchain Technology

In today’s world with the advancement of technology people are able to avail various needed services like: communicating with other people, banking, bill payments etc. through Internet with great deal of ease. Today people don’t have go physically to afford various services, rather they can afford these services from their homes using Internet. Thus, Internet is providing tremendous benefits to the people. Affording of various services through Internet give rise to a humongous amount of confidential data related to the people. Unintended illegal access to these data and mishandling of these data will create serious problems in peoples’ lives. So, it is immensely essential in today’s time to have a very secure mechanism equipped with the capability of storing the highly confidential data with great deal of protection to safeguard these data from unintended illegal access and thus providing a strong resistance against malicious mishandling of these data. Along with the protection feature the mechanism should also provide a convenient and easy way to the people of accessing these data legally. Blockchain technology is the most suitable mechanism for achieving the aforesaid objectives. Blockchain provides highly secured data storage facility with the blend of access convenience. Today, the flourishing growth of this technology in various application areas are being witnessed. This technology is booming today and providing immense benefits to the people. This paper presents a detailed review of various blockchain technologies to enrich the researchers with the knowledge about these technologies for future development in this arena.
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders

While variational autoencoders (VAEs) have been widely applied in text generation tasks, they are troubled by two challenges: insufficient representation capacity and poor controllability.The former results from the posterior collapse and restrictive assumption, which impede better representation learning.The latter arises as continuous latent variables in traditional formulations hinder VAEs from interpretability and controllability.In this paper, we propose Dictionary Prior (DPrior), a new data-driven prior that enjoys the merits of expressivity and controllability.To facilitate controlled text generation with DPrior, we propose to employ contrastive learning to separate the latent space into several parts.Extensive experiments on both language modeling and controlled text generation demonstrate the effectiveness of the proposed approach.
Ontological Representation of Healthcare Application Security Using Blockchain Technology

Blockchain is gaining traction for improving the security of healthcare applications, however, it does not become a silver bullet as various security threats are observed in blockchain-based applications. Moreover, when performing the security risk management (SRM) of blockchain-based applications, there are conceptual ambiguities and semantic gaps that hinder from treating the security threats effectively. To address these issues, we present a blockchain-based healthcare security ontology (HealthOnt) that offers coherent and formal information models to treat security threats of traditional and blockchain-based applications. We evaluate the ontology by performing the SRM of a back-pain patient’s healthcare application case. The results show that HealthOnt can support the iterative process of SRM and can be continually updated when new security threats, vulnerabilities, or countermeasures emerge. In addition, the HealthOnt may assist in the modelling and analysis of real-world situations while addressing important security concerns from the perspective of stakeholders. This work can help blockchain developers, practitioners, and other associated stakeholders to develop secure blockchain-based healthcare applications in the early stages.
Grounded Dialogue Generation with Cross-encoding Re-ranker, Grounding Span Prediction, and Passage Dropout

MultiDoc2Dial presents an important challenge on modeling dialogues grounded with multiple documents.This paper proposes a pipeline system of "retrieve, re-rank, and generate", where each component is individually optimized.This enables the passage re-ranker and response generator to fully exploit training with groundtruth data.Furthermore, we use a deep crossencoder trained with localized hard negative passages from the retriever.For the response generator, we use grounding span prediction as an auxiliary task to be jointly trained with the main task of response generation.We also adopt a passage dropout and regularization technique to improve response generation performance.Experimental results indicate that the system clearly surpasses the competitive baseline and our team CPII-NLP ranked 1st among the public submissions on ALL four leaderboards based on the sum of F1, SacreBLEU, METEOR and RougeL scores.
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction

Fine-grained Entity Typing (FET) has made great progress based on distant supervision but still suffers from label noise.Existing FET noise learning methods rely on prediction distributions in an instance-independent manner, which causes the problem of confirmation bias.In this work, we propose a clustering-based loss correction framework named Feature Cluster Loss Correction (FCLC), to address these two problems.FCLC first train a coarse backbone model as a feature extractor and noise estimator.Loss correction is then applied to each feature cluster, learning directly from the noisy labels.Experimental results on three public datasets show that FCLC achieves the best performance over existing competitive systems.Auxiliary experiments further demonstrate that FCLC is stable to hyperparameters and it does help mitigate confirmation bias.We also find that in the extreme case of no clean data, the FCLC framework still achieves competitive performance.
Unsupervised Dependency Graph Network

Recent work has identified properties of pretrained self-attention models that mirror those of dependency parse structures.In particular, some self-attention heads correspond well to individual dependency types.Inspired by these developments, we propose a new competitive mechanism that encourages these attention heads to model different dependency relations.We introduce a new model, the Unsupervised Dependency Graph Network (UDGN), that can induce dependency structures from raw corpora and the masked language modeling task.Experiment results show that UDGN achieves very strong unsupervised dependency parsing performance without gold POS tags and any other external information.The competitive gated heads show a strong correlation with human-annotated dependency types.Furthermore, the UDGN can also achieve competitive performance on masked language modeling and sentence textual similarity tasks 1 .
DoTAT: A Domain-oriented Text Annotation Tool

We propose DoTAT, a domain-oriented text annotation tool.The tool designs and implements functions heavily in need in domain-oriented information extraction.Firstly, the tool supports a multi-person collaborative process with automatically merging and review, which can greatly improve the annotation accuracy.Secondly, the tool provides annotation of events, nested event and nested entity, which are frequently required in domain-related text structuring tasks.Finally, DoTAT provides visual annotation specification definition, automatic batch annotation and iterative annotation to improve annotation efficiency.Experiments on the ACE2005 dataset show that DoTAT can reduce the event annotation time by 19.7% compared with existing annotation tools.The accuracy without review is 84.09%, 1.35% higher than Brat and 2.59% higher than Webanno.The accuracy of DoTAT even reaches 93.76% with review.
S$$^2$$QL: Retrieval Augmented Zero-Shot Question Answering over Knowledge Graph

Knowledge Graph Question Answering (KGQA) is a challenging task that aims to obtain the entities from the given Knowledge Graph (KG) to answer the user’s natural language questions. Most existing studies are focused on the traditional KGQA task, where the test distribution is the same as the training distribution over questions. In contrast, few efforts have been made to explore the zero-shot KGQA task. Logically, the existing models for the traditional KGQA task naturally show poor performance on the zero-shot setting. It is a non-trivial task to migrate the off-the-shelf zero-shot solutions in other common tasks to KGQA since an intrinsical gap exists between other common tasks and the KGQA task under the zero-shot settings. Furthermore, we observed that Similar Questions tend to have Similar Logic forms. Motivated by this, we propose a simple yet effective framework S $$^2$$ QL. In detail, we first elaborately devise three similarity measurement units to category the user’s questions. Then based on the Similarity Relation Graph (SRG) constructed by the above similarity measurement units, we devise a retrieval augmented strategy to further answer arduous zero-shot questions with its retrieved similar questions. Extensive experiments on the GrailQA and WebQSP benchmarks demonstrate that our approach is more effective than a number of competitive KGQA baselines on the zero-shot setting.
Dynamic Classification of Bank Clients by the Predictability of Their Transactional Behavior

We propose a method for dynamic classification of bank clients by the predictability of their transactional behavior (with respect to the chosen prediction model, quality metric, and predictability measure). The method adopts incremental learning to perform client segmentation based on their predictability profiles and can be used by banks not only for determining predictable (and thus profitable, in a sense) clients currently but also for analyzing their dynamics during economical periods of different types. Our experiments show that (1) bank clients can be effectively divided into predictability classes dynamically, (2) the quality of prediction and classification models is significantly higher with the proposed incremental approach than without it, (3) clients have different transactional behavior in terms of predictability before and during the COVID-19 pandemics.
Practical Event Location Estimation Algorithm for Power Transmission System Based on Triangulation and Oscillation Intensity

Event location in power systems is quite essential information for system operators to enhance control-room situational awareness capability. Therefore, it is of great importance to develop an event location estimation algorithm for transmission systems with high accuracy. With the development of wide-area measurement system (WAMS) such as FNET/GridEye, and the synchrophasor measurement devices (SMDs) such as frequency disturbance recorders (FDRs), the synchronous measurement data including frequency, voltage amplitude and phase angle can be collected and used for event location estimation. First, the phase angle and rate of change of frequency (RoCoF) trajectories are respectively used for determining two sets of wave arrival time associated with each FDR. Then, a convolutional neural network (CNN) is utilized to determine the wave arrival order to select the more suitable set of wave arrival times for a given case and to perform corresponding modifications. Next, the oscillation intensity associated with each FDR is determined based on phase angle trajectories in the center of inertia (COI) coordinate system. Finally, the multiple criteria for event location estimation are represented. Case studies and comparisons between the proposed and previous algorithms using actual and confirmed cases in U.S. power systems are performed to demonstrate the effectiveness and improvement of the proposed algorithm in practical applications.
Blockchain-based Transparency Framework for Privacy Preserving Third-party Services

Increasingly, information systems rely on computational, storage, and network resources deployed in third-party facilities such as cloud centers and edge nodes. Such an approach further exacerbates cybersecurity concerns constantly raised by numerous incidents of security and privacy attacks resulting in data leakage and identity theft, among others. These have, in turn, forced the creation of stricter security and privacy-related regulations and have eroded the trust in cyberspace. In particular, security-related services and infrastructures, such as Certificate Authorities (CAs) that provide digital certificate services and Third-Party Authorities (TPAs) that provide cryptographic key services, are critical components for establishing trust in crypto-based privacy-preserving applications and services. To address such trust issues, various transparency frameworks and approaches have been recently proposed in the literature. This paper proposes TAB framework that provides transparency and trustworthiness of third-party authority and third-party facilities using blockchain techniques for emerging crypto-based privacy-preserving applications. TAB employs the Ethereum blockchain as the underlying public ledger and also includes a novel smart contract to automate accountability with an incentive mechanism that motivates users to participate in auditing, and punishes unintentional or malicious behaviors. We implement TAB and show through experimental evaluation in the Ethereum official test network, Rinkeby, that the framework is efficient. We also formally show the security guarantee provided by TAB, and analyze the privacy guarantee and trustworthiness it provides.