Reviewerly/exHarmony huggingface dataset:
Context Aware Joint Modeling of Domain Classification, Intent Detection and Slot Filling with Zero-Shot Intent Detection Approach

Natural language understanding (NLU) aims to extract schematic information contained in user utterances, which allows down streaming module of dialogue system, i.e., Dialogue Manager (DM) to process user queries and serve users in accomplishing their goal. If NLU component detects information improperly, it will cause error propagation and failure of all subsequent modules. Although the development of an adequate conversation system is challenging because of its periodic and contextual nature, its efficacy, applicability, and positive impact continue to fuel its recent surge and attention in the research community. The proposed work is the first of its kind, which attempts to develop a unified, multitasking, and context-aware BERT-based model for all NLU tasks, i.e., Domain classification (DC), Intent detection (ID), Slot filling (SF). Additionally, we have also incorporated a zero-shot intent detection technique in our proposed model for dealing with new and emerging intents effectively. The experimental results, as well as comparisons to the present state-of-the-art model and other several baselines on a benchmark dataset, firmly establish the efficacy and necessity of the proposed model.
Reachability-Based Approach for Search and Detection of Maneuvering Cislunar Objects

With recent and planned missions to cislunar space, there exists a developing need for expanding space domain awareness to include cislunar space. Detection, tracking, and maneuver reconstruction in Earth orbits is already a difficult task and is exponentially more difficult in the cislunar domain given the shear volume and the chaotic nature of the three-body problem. Additionally, many of these methods assume that measurements of the target spacecraft are readily available. The focus of this work is to examine the utility of the recently developed reachability set search (RSS) algorithm in regaining custody of a cislunar target lost due to an unknown, bounded maneuver. The objective of in this work is to numerically define a Taylor series approximation to the reachability set of a cislunar spacecraft given a priori knowledge on only the maximum delta-v and a window for the time-of-maneuver, optimally search the reachability set, and perform state estimate and maneuver reconstruction following detection of the target. This algorithm is tested two scenarios: a L1 halo orbit and a L2 Lyapunov to L1 Lyapunov transfer with 10 m/s and 100 m/s maneuver capabilities, respectively. The RSS algorithm proves effective assuming accurate reachability set approximations, which is a function of the order of the polynomial approximation, volume of the true reachable space, and proximity to the lunar singularity.
Eventmd: High-Speed Moving Object Detection Based on Event-Based Video Frames

The event camera is a novel bio-inspired sensor that has attracted the interest of many researchers. As a neuromorphic vision, the sensor is different from the traditional frame-based cameras. It has such advantages for high-speed object detection that conventional frame-based cameras can’t match, e.g., high temporal resolution, high dynamic range (HDR), sparse and minimal motion blur. In this paper, we propose EventMD an adaptive framework for high-speed moving object detection using event cameras and an event-based dataset collected from real traffic scenes using event-camera Celex-V. The key novelties of our framework are our method overcomes the inability of conventional cameras to detect high-speed moving objects and also provides a framework for event vision that can adapt to any detector. Since event cameras have unique advantages for capturing high-speed moving objects in different scenes, our framework combines bio-inspired sensors and conventional detection algorithms to detect high-speed moving objects and to reduce motion blur and data redundancy. The experiments show that our method can detect high-speed moving objects but some state-of-the-art detection algorithms with the conventional camera are not capable of it and our proposed approach is very competitive and extensible. In addition, our approach can be extended to other scenarios of high-speed moving objects.
Abnormal Activity Recognition using Deep Learning in Streaming Video for Indoor Application

Human activity recognition has emerged as a challenging research domain for video analysis. The major issue for abnormal activity recognition in a streaming video is the presence of the large spatio-temporal data along with the constraints of communication networks affecting the quality of received data for analysis. In this paper, we propose a deep learning-based system to identify abnormal human activities using a combination of Skeleton Activity Forecasting (SAF) and a Bi-LSTM network. The generated skeleton joint points of a human subject are used for the pose estimation. The skeleton tracking and regions of interest points are estimated on a streaming video from an IP networked camera. The extracted interest points and their corresponding features are optimized and used to classify them as normal, abnormal or suspicious actions. The proposed system complies with Recommendation ITU-T H.627 “Signalling and protocols for a video surveillance system” and has been experimented and evaluated over benchmarked data sets for the recognition of human actions. The system performance attains a precision of 85.6% and an accuracy of 97.2% in recognizing different actions.
Four-way Bidirectional Attention for Multiple-choice Reading Comprehension

As one of the crucial tasks of natural language processing, machine reading comprehension has gained increased attention in recent years. In this paper, we propose a four-way bidirectional attention network for a multiple-choice reading comprehension task, where every question comes with a set of candidate options and only one correct answer. Current methods on such tasks usually judge options independently and ignore their relations. Thus, this work designs a four-way bidirectional attention strategy to formulate the interactions among the passage, questions and candidate options. In particular, the relations among options are well represented. This enables the model to leverage the option correlation information for inferring the final answer accurately. The experimental evaluations on the CosmosQA dataset demonstrate the competitive performance of our model, and confirm the effectiveness of the option comparison strategy.
Web Development and performance comparison of Web Development Technologies in Node.js and Python

From the first website being created in 1991 by Tim Berners-Lee to this day in 2021, the world of websites and their development has seen a rapid evolution. Every business nowadays has a website to make its online presence in this digital age. Website use is not only limited to this, but many people use them to build their online portfolio, apart from that there are many web-based applications/software, etc. This research paper discusses the process involved in developing a website in past and present, development of content delivery over the years, the website uses, a website for mobile devices, and performance comparison between two of the most used web backend development technologies, i. e, Node.js and Python. For comparing performance, we have used Locust – an open-source software and Autocannon and tested both of them under similar conditions.
Research on data sharing mechanism of power material supply chain based on blockchain

Aiming at the common problems of data centralized storage, poor security and weak tamper resistance in the existing power material supply chain management, a data sharing mechanism based on block chain is proposed: the power limit setting and identity authentication of power material supply chains at all levels are realized through joint consensus algorithm and digital signature; Through the interstellar file system (IPFS) technology in the blockchain, data protection, verification, recovery and sharing are realized. Finally, through experimental comparison, this method has the characteristics of decentralization, security, credibility and non-tampering. It not only reduces the overall inventory cost of the supply chain, but also ensures the security of data and improves the overall response speed of the supply chain.
On Commitment over General Compound Channels

As computation and communication become increasingly ubiquitous, data security remains a key consideration. Hence, cryptographic primitives lie at the heart of several modern digital technologies. In this work, we study one such important cryptographic primitive called commitment. Here two parties, say Alice and Bob, aim to realize a protocol with the following functionality: Alice seeks to share or commit to a bit string securely with Bob with the requirement that the string is hidden from Bob until she chooses to reveal it to him. Bob wants to ensure that the protocol binds Alice to her original choice of bit string by allowing him to successfully detect when she cheats at the time of revealing her string. This seemingly elementary cryptographic functionality is extremely powerful and finds wide use in technologies like blockchains, digital contracts, cryptocurrencies, etc.. More generally, commitment has also been widely studied for applications like sealed-bid auctions, distributed coin tossing, etc.. Building on Wyner's classic work on wiretap channels, unconditionally-secure or information-theoretically secure protocols have been realized for a wide variety of problems, including commitment, using noisy channels as a resource. Crucially, however, the security guarantees are intimately tied to the knowledge of the noise statistics; these could be compromised when noise statistics are imprecisely known. In this work, we seek to study information-theoretically secure commitment over a class of unreliable noisy channels called compound channels. In particular, we study commitment over compound discrete memoryless channels (compound-DMCs) where the instantiated channel is known to be a DMC but its realization or channel state is not known exactly and can only be localized to a potential set of possible states. We precisely characterize the optimal throughput or commitment capacity over general compound DMCs. We also study the effect of state-awareness at the two parties, when the parties are honest and when they are malicious, on the commitment capacity of such compound DMCs. Interestingly, we show that committer-side state-awareness can non-trivially increase the commitment throughput over the compound DMC which is not the case when one has only receiver-side state awareness. In particular, we completely characterize the commitment capacity for all state-aware configurations for compound DMCs.
A multiturn complementary generative framework for conversational emotion recognition

Conversational emotion recognition (CER) is a significant task due to its application in human–computer interaction. Existing work treats CER as an utterance-level classification task without considering that empathic response also reflects contextual emotion understanding. Previous work has proven that accurate recognition of emotions in the dialogue history is helpful to generate high-fit responses. In this paper, we investigate whether this conclusion is a sufficient and necessary condition. Specifically, we define an auxiliary empathic multiturn dialogue generation (MDG) task to enhance emotion understanding. Correspondingly, we present a Sequence-to-Sequence oriented framework that combines CER and MDG in a multitask learning manner to verify the complementarity between the two tasks. First, we use alternate recurrent neural networks to encode the content of historical utterances and represent the states of multiparty emotions, which are used for emotion classification. Second, since most MDG methods ignore the emotional coherence of the dialogue context itself, we use affine transformation to fuse hidden states of content and emotions to initialize the decoder. Finally, at each step of generation, an attention mechanism is used to fuse information from the dialogue history to ensure emotional coherence. The CER results of our models outperform the state-of-the-art on three prevalent emotional dialogue data sets. Further analysis demonstrates the mutual promotion and empathy interpretability between MDG and CER. Furthermore, our framework is scalable for different coding strategies and multimodal fusion. To the best of our knowledge, this is the first work to explore CER from the perspective of empathy through multitask learning with dialogue generation.
Quality Management in Pedagogical Education Based on the Diagnostics of Teachers’ Professional Deficits: Theoretical and Methodological Aspect

The paper presents the methodology of education quality management based on the identication of teachers’ professional deficits. The methodology for evaluating the formation of subject and methodological competencies is described. Assessment materials are developed in accordance with the requirements of the professional standard of the teacher and the Federal state educational standards of basic general and secondary general education. It is supposed to implement an automated identification of professional deficits within teaching staff. The article presents empirical data on the results of the approbation of this technique. 624 teachers from rural and urban educational organizations of the Republic of Bashkortostan and 90 students of pedagogical specialties of the university participated in the testing. The analysis of the approbation results of the methodology and assessment materials showed that the developed methodology is applicable to identify professional deficits of teachers and students of teacher-training institutions for further construction of individual educational trajectories of the approbation participants.
FineDIFT: Fine-Grained Dynamic Information Flow Tracking for Data-Flow Integrity Using Coprocessor

Dynamic Information Flow Tracking (DIFT) is a technique that facilitates run-time data-flow analysis on a running process, allowing a system to overcome the limitations of finding data dependencies statically at compilation time. DIFT serves as the backbone for applications including data-flow integrity (DFI). However, previous uses of DIFT towards DFI often have large overhead in terms of hardware, software or both, and often cannot provide fine-granularity tracking for software object, such as variables. To address these limitations, we present FineDIFT as a DFI framework which utilizes DIFT to generate a live data-flow graph of a running process and perform hardware-based assisted analysis at fine-granularity, thus being able to enforce the application’s Data-Flow Graph (DFG). We provide a sample implementation on a RISC-V core with a performance overhead of 5.03% for BEEBS benchmarks and hardware overhead of 6% LUTs and 8% Flip-Flops in the FPGA implementation, if excluding the Content-Addressable Memory (CAM) like structure used for metadata storage. With CAM-like structure being synthesized using FPGA logic, the total hardware overhead is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 2 \times $ </tex-math></inline-formula> LUTs and 33% Flip-Flops compared to the original RISC-V core. We also use the real-world application and customized vulnerable application to demonstrate the effectiveness of the proposed framework in protecting computing systems.
An ASP-based solver for parametrized-difference revision

Abstract In the present article, a solver for the well-behaved concrete revision operators, named parametrized-difference (PD) revision operators, is described. The solver is developed by means of the powerful framework of answer set programming, which constitutes a contemporary modelling tool, oriented towards difficult search problems. Several useful functionalities are supported by the system, namely, dynamic PD revision, query-answering capabilities, integrity-constraints handling and revision under the closed-world assumption. The solver exhibits high performance in a plethora of revision instances, including hard ones of the SATLIB library; the fact that PD revision respects an intuitive relevance-sensitive principle, identified herein, contributes to this high performance. A graphical user interface provides easy interaction with the implemented system, making it a high-end standalone revision-tool for potential artificial intelligence applications.
Improving Text Classifiers Through Controlled Text Generation Using Transformer Wasserstein Autoencoder

Training good classifiers on imbalanced dataset has always been a challenge, especially if the classifier has to work with textual data. Natural language is one such area where there are abundant imbalanced datasets such as spam filtering, fake news detection, and toxic comment classification. Techniques for generating synthetic data like synthetic minority over-sampling technique fail to train effective classifiers. This paper proposes a technique for generating controlled text using the transformer-based Wasserstein autoencoder which helps in improving the classifiers. The paper compares the results with classifiers trained on data generated by other synthetic data generators. Furthermore, the potential issues of the proposed model for training classifiers are discussed.
Towards Nonintrusive and Secure Mobile Two-Factor Authentication on Wearables

Mobile devices are promising to apply two-factor authentication to improve system security. Existing solutions have certain limits of requiring extra user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors available in most wrist-worn wearables. PPGPass simultaneously performs a password/pattern/signature authentication and a physiological-based authentication. To realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts so that users do not have to deliberately keep their bodies still. In addition, to deal with noncancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials. We leverage the great power of Random Forest and Support Vector Data Description to detect adversaries and verify a user's identity. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors. Extensive experiments demonstrate that PPGPass can achieve the false acceptance rate of 3.11% and the false recognition rate of 3.71%, which confirms its high effectiveness, security, and usability.
GlassNet: Label Decoupling‐based Three‐stream Neural Network for Robust Image Glass Detection

Abstract Most of the existing object detection methods generate poor glass detection results, due to the fact that the transparent glass shares the same appearance with arbitrary objects behind it in an image. Different from traditional deep learning‐based wisdoms that simply use the object boundary as an auxiliary supervision, we exploit label decoupling to decompose the original labelled ground‐truth (GT) map into an interior‐diffusion map and a boundary‐diffusion map. The GT map in collaboration with the two newly generated maps breaks the imbalanced distribution of the object boundary, leading to improved glass detection quality. We have three key contributions to solve the transparent glass detection problem: (1) We propose a three‐stream neural network (call GlassNet for short) to fully absorb beneficial features in the three maps. (2) We design a multi‐scale interactive dilation module to explore a wider range of contextual information. (3) We develop an attention‐based boundary‐aware feature Mosaic module to integrate multi‐modal information. Extensive experiments on the benchmark dataset exhibit clear improvements of our method over SOTAs, in terms of both the overall glass detection accuracy and boundary clearness.
Domain adaptation and continual learning in semantic segmentation

Deep networks produce outstanding results in many computer vision tasks including semantic segmentation. On the other hand, they are plagued by the need of a huge amount of labeled data for training, which is not always available or may not be accessible all together as typically required in the standard supervised machine learning setting. These problems raise the demand for knowledge transfer techniques able to adapt the learning performed on one domain to a related one and to allow the training of the network in multiple stages. This chapter will start by introducing the domain adaptation task for semantic segmentation and the different levels at which the adaptation can be performed. Then, the different families of strategies will be discussed in detail presenting the most successful approaches for each of them. In the second part, we will present the task of continual learning in semantic segmentation. Although being a relatively new research field, its interest is rapidly growing, and many different scenarios have been introduced, which will be described in detail along with the approaches to tackle them.
Improved Decision Tree Classification (IDT) Algorithm for Social Media Data

In this paper we used classification algorithms on social networking. We are proposing, a new classification algorithm called the improved Decision Tree (IDT). Our model provides better classification accuracy than the existing systems for classifying the social network data. Here we examined the performance of some familiar classification algorithms regarding their accuracy with our proposed algorithm. We used Naïve Bayes ,SVM, , KNN, decision tree in our research and performed analyses on social media dataset. Matlab is used for performing experiments. The result shows that the proposed algorithm achieves the best results with an accuracy of 84.66%.
Mesh-Based Reconstruction of Dynamic Foam Images Using X-Ray CT

Due to the rapid nucleation and bubble deformation processes that occur during a foam formation process, 4D-CT scans can only record a small number of X-ray projections per rotation. Current dynamic CT imaging methods compute a series of 3D images, which requires a high number of projections per rotation and thus limits the temporal resolution. In this work, we present a new reconstruction method that directly reconstructs a 4D dynamic image, by adapting a set of spherical meshes, representing the foam bubbles, such that the projection distance to the measured projections is minimized. Our method requires little memory, is robust against noise, and yields accurate reconstructions even when only few projections are available, as demonstrated by several experiments on simulated and real data.
A Comprehensive Formalization of AADL with Behavior Annex

In safety-critical fields, architectural languages such as AADL (Architecture Analysis and Design Language) have been playing an important role, and the analysis of the languages and systems designed by them is a challenging research topic. At present, a formal method has become one of the main practices in software engineering for strict analysis, and it has been applied on the tools of formalization and analysis. The formal method can be used to find and resolve the problems early by describing the system with precise semantics and validating the system model. This article studies the comprehensive formal specification and verification of AADL with Behavior annex by the formal method. The presentation of this specification and semantics is the aim of this article, and the work is illustrated with an ARINC653 model case study in Isabelle/HOL.