Reviewerly/exHarmony huggingface dataset:
Enhancing Long-Distance Dialogue History Modeling for Better Dialogue Ellipsis and Coreference Resolution

Previous work on dialogue-specific ellipsis and coreference resolution usually concatenates all dialogue history utterances into a single sequence. It may mislead the model to attend to inappropriate parts and to copy from wrong utterances when the dialogue history is long. In this paper, we aim to model dialogue history from multiple granularities and take a deep look into the semantic connection between the dialogue history and the omitted or coreferred expressions. To achieve this, we propose a speaker highlight dialogue history encoder and a top-down hierarchical copy mechanism to generate the complete utterances. We conduct dozens of experiments on the CamRest676 dataset, and the experimental results show that our methods are expert in long-distance dialogue history modeling and can significantly improve the performance of ellipsis and coreference resolution in the dialogue task.
Zipping Strategies and Attribute Grammars

Strategic term rewriting and attribute grammars are two powerful programming techniques widely used in language engineering. The former relies on strategies (recursion schemes) to apply term rewrite rules in defining transformations, while the latter is suitable for expressing context-dependent language processing algorithms. Each of these techniques, however, is usually implemented by its own powerful and large processor system. As a result, it makes such systems harder to extend and to combine. We present the embedding of both strategic tree rewriting and attribute grammars in a zipper-based, purely functional setting. The embedding of the two techniques in the same setting has several advantages: First, we easily combine/zip attribute grammars and strategies, thus providing language engineers the best of the two worlds. Second, the combined embedding is easier to maintain and extend since it is written in a concise and uniform setting. We show the expressive power of our library in optimizing Haskell let expressions, expressing several Haskell refactorings and solving several language processing tasks for an Oberon-0 compiler.
An Efficient Proxy Blind Signcryption Scheme for IoT

Recent years have witnessed growing scientific research interest in the Internet of Things (IoT) technologies, which supports the development of a variety of applications such as health care, Industry 4.0, agriculture, ecological data management, and other various domains. IoT utilizes the Internet as a prime medium of communication for both single documents as well as multi-digital messages. However, due to the wide-open nature of the Internet, it is important to ensure the anonymity, untraceably, confidentiality, and unforgeability of communication with efficient computational complexity and low bandwidth. We designed a light weight and secure proxy blind signcryption for multi-digital messages based on a hyperelliptic curve (HEC). Our results outperform the available schemes in terms of computational cost and communication bandwidth. The designed scheme also has the desired authentication, unforgeability of warrants and/or plaintext, confidentiality, integrity, and blindness, respectively. Further, our scheme is more suitable for devices with low computation power such as mobiles and tablets.
Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming

Evolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Lévy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP variant was proposed using a step-size-based self-adaptive mutation operator. In SSEP, the mutation type with its parameters is selected based on the step size, which differs from generation to generation. Several principles for choosing proper parameters have been proposed; however, SSEP still has limitations and does not display outstanding performance on some benchmark functions. In this work, we proposed a novel mutation strategy based on both the “step size” and “survival rate” for EP (SSMSEP). SSMSEP-1 and SSMSEP-2 are two variants of SSMSEP, which use “survival rate” or “step size” separately. Our proposed method can select appropriate mutation operators and update parameters for mutation operators according to diverse landscapes during the evolutionary process. Compared with SSMSEP-1, SSMSEP-2, SSEP, and other EP variants, the SSMSEP demonstrates its robustness and stable performance on most benchmark functions tested.
Blockchain-Based Mutual Authentication Protocol with Privacy Protection in Telemedicine

Telemedicine builds a patient-centered remote diagnosis and continuous monitoring service system, which can effectively alleviate the current situation of difficult and expensive medical treatment. This paper focuses on mutual authentication with privacy protection in telemedicine. First, we propose a decentralized multi-authority attribute-based signature scheme. The signature scheme can be used to verify the signer's attributes without leaking the identity information, thereby achieving privacy protection while authenticating. Combined with the proposed signature scheme, a blockchain-based mutual authentication protocol with privacy protection in telemedicine is designed. By this protocol, patients and telemedicine terminal can encrypt and store the medical data off-chain, and store the data digest and decentralized attribute-based signature on the blockchain. As a result, the confidentiality of the data and the authenticity of the source can be guaranteed. In addition, the nature of the blockchain and signature ensure that accurate liability determination can be made when medical disputes occur. Theoretical analysis shows that our protocol is secure and practical.
A Syscall-Level Binary-Compatible Unikernel

Unikernels are minimal single-purpose virtual machines. They are highly popular in the research domain due to the benefits they provide. A barrier to their widespread adoption is the difficulty/impossibility to port existing applications to current unikernels. HermiTux is the first unikernel providing system call-level binary compatibility with Linux applications. It is composed of a hypervisor and a lightweight kernel layer emulating the load- and runtime Linux ABI. HermiTux relieves application developers from the burden of porting software, while providing unikernel benefits such as security through hardware-assisted virtualized isolation, swift boot time, and low disk/memory footprint. Fast system calls and kernel modularity are enabled through binary rewriting and analysis techniques, as well as shared library substitution. HermiTuxs design principles are architecture-independent and we present a prototype on both the x86-64 and ARM aarch64 ISAs, targeting various cloud as well as edge/embedded deployments. We demonstrate HermiTuxs compatibility over a range of native C/C++/Fortran/Python Linux applications. We also show that it offers a similar degree of lightweightness compared to other unikernels, and that it performs similarly to Linux in many cases: its performance overhead averages 3% in memory- and compute-bound scenarios, and its I/O performance is acceptable.
Augmented Reality for an Efficient Inspection of Outfitting Process in Shipbuilding

This paper proposes the system for ship outfitting support based on the augmented reality. Considering the practical applicability of the elaborated procedure as the highest priority, the intention was to develop a sufficiently robust method for model registration that could be applied in extremely demanding environments such as the interior of the ship during construction. The approach presented in this paper is based on QR markers consisting the code value of the model from the database thus enabling the interactive mapping between the CAD 3D model, reality and digitized technical documentation The application has been developed in Unity 3D game engine and shows promising potential to support interactive ship outfitting procedures during construction in the shipyard.
Using Partial Differential Equation Face Recognition Model to Evaluate Students’ Attention in a College Chinese Classroom

The partial differential equation learning model is applied to another high-level visual-processing problem: face recognition. A novel feature selection method based on partial differential equation learning model is proposed. The extracted features are invariant to rotation and translation and more robust to illumination changes. In the evaluation of students’ concentration in class, this paper firstly uses the face detection algorithm in face recognition technology to detect the face and intercept the expression data, and calculates the rise rate. Then, the improved model of concentration analysis and evaluation of a college Chinese class is used to recognize facial expression, and the corresponding weight is given to calculate the expression score. Finally, the head-up rate calculated at the same time is multiplied by the expression score as the final concentration score. Through the experiment and analysis of the experimental results in the actual classroom, the corresponding conclusions are drawn and teaching suggestions are provided for teachers. For each face, a large neighborhood set is firstly selected by the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> -nearest neighbor method, and then, the sparse representation of sample points in the neighborhood is obtained, which effectively combines the locality of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> -nearest neighbor and the robustness of sparse representation. In the sparse preserving nonnegative block alignment algorithm, a discriminant partial optimization model is constructed by using sparse reconstruction coefficients to describe local geometry and weighted distance to describe class separability. The two algorithms obtain good clustering and recognition results in various cases of real and simulated occlusion, which shows the effectiveness and robustness of the algorithm. In order to verify the reliability of the model, this paper verified the model through in-class practice tests, teachers’ questions, and interviews with students and teachers. The results show that the proposed joint evaluation method based on expression and head-up rate has high accuracy and reliability.
From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data

Advances in neuroimaging have yielded extensive variety in the scale and type of data available. Effective integration of such data promises deeper understanding of anatomy and disease–with consequences for both diagnosis and treatment. Often catered to particular datatypes or scales, current computational tools and mathematical frameworks remain inadequate for simultaneously registering these multiple modes of “images” and statistically analyzing the ensuing menagerie of data. Here, we present (1) a registration algorithm using a “scattering transform” to align high and low resolution images and (2) a varifold-based modeling framework to compute 3D spatial statistics of multiscale data. We use our methods to quantify microscopic tau pathology across macroscopic 3D regions of the medial temporal lobe to address a major challenge in the diagnosis of Alzheimer’s Disease–the reliance on invasive methods to detect microscopic pathology.
Anomaly detection for data accountability of Mars telemetry data

The Mars Curiosity rover is frequently sending engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data to request a re-transmission when necessary. So far, this task relied primarily on the expertise of GDSA analysts who, especially with new missions launching, require the assistance of an autonomous and effective detection tool. Variational autoencoders are powerful deep neural networks that can learn to isolate such anomalies yet, as any deep network, they require an architectural search and fine-tuning to yield exploitable performance. Furthermore, this process needs to be repeated periodically to adjust to the changing flow of transmissions. This work presents Δ-MADS, a hybrid derivative-free optimization method designed to quickly produce efficient variational autoencoders in order to assist the GDSA team in their mission.
Ground 3D Object Reconstruction Based on Multi-View 3D Occupancy Network using Satellite Remote Sensing Image

It is challenging to achieve high accuracy and rich details three-dimensional (3D) reconstruction of ground object using satellite remote sensing image (RSI). A 3D reconstruction method is proposed to address this problem based on multi-view 3D occupancy network. The domain adaptive multi-view image obtained by pretreatment is input into the depth residual network to extract perceptual features at first. The occupancy probability of objects at different coordinates in 3D space is obtained by combining spatial probability network with definition balance strategy. Then, the isosurface is extracted to obtain the 3D mesh of the object, and the reconstruction result is given scale by combining the attitude estimation and the original RSI parameter information. Experiments are carried out on a testing dataset composed of hundreds of object image pairs. The experimental results show that the proposed method for satellite RSI has better accuracy than the typical single-view and multi-view reconstruction methods.
Scalable POMDP Decision-Making Using Circulant Controllers

This paper presents a novel policy representation for partially observable Markov decision processes (POMDPs) called circulant controllers and a provably efficient gradient-based algorithm for them. A formal mathematical description is provided that leverages circulant matrices for the controller’s stochastic node transitions. This structure is particularly effective for capturing decision-making patterns found in real-world domains with repeated periodic behaviors that adapt their cycles based on observation. This includes domains such as bipedal walking over varied terrain, pick-and-place tasks in warehouses, and home healthcare monitoring and medicine delivery in household environments. A performant gradient-based algorithm is presented with a detailed theoretical analysis, formally proving the algorithm’s improved performance, as well as circulant controllers’ structural properties. Experiments on these domains demonstrate that the proposed controller algorithm outperforms other state-of-the-art POMDP controller algorithms. The proposed novel controller approach is demonstrated on an actual robot performing a navigation task in a real household environment.
DEC: A deep‐learning based edge‐cloud orchestrated system for recyclable garbage detection

Summary To identify recyclable garbage via the garbage classification is an effective countermeasure for protecting the environment. An automatic classification system supported by image recognition technologies is able to significantly reduce huge human labors of recycling tasks. However, performing the real‐time and accurate garbage detection is not a trivial task. In this article, we present an edge‐cloud framework equipped with deep learning model for recyclable garbage detection. Specifically, we propose to use the deep convolutional neural network for garbage images classification, and thus design the collaborative mechanism between edge devices and the cloud server. As a result, we design and develop a novel recyclable garbage detection system, where scanning garbage images and thus detecting recyclable ones can be completed in real‐time. We validate the performances of the proposed recyclable garbage detection system on 1000 real‐life household garbage images. Experimental results show the overall accuracy of our system reaches nearly 90%, and the time for detection is less than 500 ms.
Conducting and Publishing Research in Developing Countries: Challenges and Solutions

Abstract Most of the knowledge in the library and information science (LIS) discipline is created and published by researchers in developed countries. Two roundtable sessions of ASIS&amp;T SIG‐III with the ASIS&amp;T Africa Chapter and ASIS&amp;T South Asia Chapter, in early 2021, confirmed “the lack of a conducive research environment” as the primary reason for this inequality in the discipline and called for systematic efforts, like this panel, to (a) create awareness about this inequality and (b) start building a global support system for LIS researchers in developing countries. In the first 30 minutes of this panel, six LIS scholars with cultural roots, academic training, and research experience in developing countries will illustrate common challenges to conducting and publishing research in developing countries. In the next 30 minutes, attendees will be divided into groups, with each group facilitated by a panelist, to brainstorm solutions for addressing the challenges related to academic training for conducting and publishing research, accessing resources, institutional support, opportunities for research collaboration and funding, fieldwork, analyzing data, composing manuscripts, and finding mentors, among others. In the last 30 minutes, each group will present its findings.
Explainable neural computation via stack neural module networks

Abstract In complex inferential tasks like question answering, machine learning models must confront two challenges: the need to implement a compositional reasoning process, and, in many applications, the need for this reasoning process to be interpretable to assist users in both development and prediction. Existing models designed to produce interpretable traces of their decision‐making process typically require these traces to be supervised at training time. In this paper, we present a novel neural modular approach that performs compositional reasoning by automatically inducing a desired subtask decomposition without relying on strong supervision. Our model allows linking different reasoning tasks through shared modules that handle common routines across tasks. Experiments show that the model is more interpretable to human evaluators compared to other state‐of‐the‐art models: users can better understand the model's underlying reasoning procedure and predict when it will succeed or fail based on observing its intermediate outputs.
Amharic Character Recognition Based on Features Extracted by CNN and Auto-Encoder Models

Amharic is an ancient Semitic language that serves as the official language of the Federal Republic of Ethiopia. Due to the large number of historical and literary documents written in this language, an automated OCR system is highly demanded. However, previous approaches have been based on traditional machine learning algorithms that focus on hand-crafted feature extraction, and the performance of these methods is greatly affected by the presence of a large set of structurally similar characters. Therefore, according to various studies on Amharic character, this problem can be solved by examining robust feature extraction techniques. In this study, we proposed a hybrid method that uses deep learning models Convolutional Neural Network (CNN) and Convolutional Auto-Encoder (CAE) for feature extraction, Random Forest (RF) and Mutual Information (MI) feature selection methods for selecting top features and a traditional machine learning algorithm Support Vector Machine (SVM) for classification. First, the features extracted by the two deep models were combined to form hybrid features, and then top features were selected by applying feature selection. The common features selected by the two feature selection methods were later used for recognition by SVM. Experimental results using CNN extracted features achieved an accuracy of 96.03% while using CAE extracted features achieved an accuracy of 92.52%. On the other hand, the proposed method based on the intersection features selected by the RF and MI feature selection methods achieved an accuracy of 97.06%.
Risk Group Prediction of Software Projects Using Machine Learning Algorithm

In our study, we predicted software projects’ risk group by using machine learning algorithms. We conducted ID3 and Naïve Bayes algorithms using ‘development source as count’, ‘software development lifecycle model’ and ‘project size’ parameters. We obtained different accuracy ratios by implementing holdout model.
HERTI: A Reinforcement Learning-Augmented System for Efficient Real-Time Inference on Heterogeneous Embedded Systems

Real-time inference is the key technology that enables a variety of latency-critical intelligent services such as autonomous driving and augmented reality. Heterogeneous embedded systems that consist of various computing devices with widely-different architectural and system-level characteristics are emerging as a promising solution for real-time inference. Despite extensive prior works, it still remains unexplored to design and implement a practical system that enables efficient real-time inference on heterogeneous embedded systems. To bridge this gap, we propose HERTI, a reinforcement learning-augmented system for efficient real-time inference on heterogeneous embedded systems. HERTI efficiently explores the state space and robustly finds an efficient state that significantly improves the efficiency of the target inference workload while satisfying its deadline constraint through reinforcement learning. Our quantitative evaluation conducted on a real heterogeneous embedded system demonstrates the effectiveness of HERTI in that HERTI achieves high inference efficiency in multiple metrics (i.e., energy and energy-delay product) with a strong deadline guarantee in contrast to the state-of-the-art techniques, delivers larger gains as the inference deadline and the system heterogeneity increase, provides strong generality for hyper-parameter tuning, and significantly reduces the training time through its estimation-based approach across all the evaluated inference workloads and scenarios.
A New Variational Bayesian-Based Kalman Filter with Unknown Time-Varying Measurement Loss Probability and Non-Stationary Heavy-Tailed Measurement Noise

In this paper, a new variational Bayesian-based Kalman filter (KF) is presented to solve the filtering problem for a linear system with unknown time-varying measurement loss probability (UTVMLP) and non-stationary heavy-tailed measurement noise (NSHTMN). Firstly, the NSHTMN was modelled as a Gaussian-Student's t-mixture distribution via employing a Bernoulli random variable (BM). Secondly, by utilizing another Bernoulli random variable (BL), the form of the likelihood function consisting of two mixture distributions was converted from a weight sum to an exponential product and a new hierarchical Gaussian state-space model was therefore established. Finally, the system state vector, BM, BL, the intermediate random variables, the mixing probability, and the UTVMLP were jointly inferred by employing the variational Bayesian technique. Simulation results revealed that in the scenario of NSHTMN, the proposed filter had a better performance than current algorithms and further improved the estimation accuracy of UTVMLP.