Reviewerly/exHarmony huggingface dataset:
Effective FAQ Retrieval and Question Matching Tasks with Unsupervised Knowledge Injection

Frequently asked question (FAQ) retrieval, with the purpose of providing information on frequent questions or concerns, has far-reaching applications in many areas like e-commerce services, online forums and many others, where a collection of question-answer (Q-A) pairs compiled a priori can be employed to retrieve an appropriate answer in response to a user’s query that is likely to reoccur frequently. To this end, predominant approaches to FAQ retrieval typically rank question-answer pairs by considering either the similarity between the query and a question (q-Q), the relevance between the query and the associated answer of a question (q-A), or combining the clues gathered from the q-Q similarity measure and the q-A relevance measure. In this paper, we extend this line of research by combining the clues gathered from the q-Q similarity measure and the q-A relevance measure, and meanwhile injecting extra word interaction information, distilled from a generic (open-domain) knowledge base, into a contextual language model for inferring the q-A relevance. Furthermore, we also explore to capitalize on domain-specific topically-relevant relations between words in an unsupervised manner, acting as a surrogate to the supervised domain-specific knowledge base information. As such, it enables the model to equip sentence representations with the knowledge about domain-specific and topically-relevant relations among words, thereby providing a better q-A relevance measure. We evaluate variants of our approach on a publicly-available Chinese FAQ dataset (viz. TaipeiQA), and further apply and contextualize it to a large-scale question-matching task (viz. LCQMC), which aims to search questions from a QA dataset that have a similar intent as an input query. Extensive experimental results on these two datasets confirm the promising performance of the proposed approach in relation to some state-of-the-art ones.
Relation Artefacts Type I

This chapter touches on the perhaps most crucial and difficult part of HWID, of finding psychological needs from both an interaction design and a work analysis point of view. The chapter presents three sub-types of relation artefacts Type I: organizational problems, worker needs, and contextual personas. It further develops a terminology that acknowledges that computer algorithms (e.g., robots, work automation) are themselves socio-technical systems that embed designers, vendors, suppliers, and managers, even when their primary users and collaborators are workers. Finding human and non-human actors’ psychological needs is not a well-defined procedure, but an interpretative act that requires courage and will, and the chapter ends with a summary of how this is done.
Prediction of Adverse Effects of Medical Treatment using Electronic Health Record and Machine Learning

In the past few years, deaths due to adverse effects of medical treatment have become a major concern and early detection and diagnosis can lead to a timely cure. There is a significant need to develop automated systems for adverse event detection. The complete blood count (CBC) test is a common broad screening test used to determine patients' overall health status. CBC data can be used to screen for a variety of conditions and diseases and also to monitor the efficacy of the treatment. Considering the facts, in this study authors have proposed to use ensemble machine learning methods to develop an automated system for the detection of patients with adverse effects of medical treatment (AEMT) symptoms. The proposed approach has been evaluated on an electronic health record dataset. The data consists of 10 parameters with instances labelled as in-care and out-care. The study results indicate that the extremely randomized ensemble learning methods show better performance as compared to other methods with a reduction in execution time. The developed automated system can be used for real-time medical assistance.
Research on image enhancement algorithm based on deep neural network algorithm

Image denoising and enhancement technology plays an important role in remote sensor image processing. Due to the low brightness, signal-to-noise ratio and contrast of image processing, the traditional remote sensor is limited in obtaining object information. Based on the principle of biological segmentation algorithm, neural network algorithm technology can carry the advantages of image evaluation algorithms such as PSNR, IFC and SSIM, through the end-to-end training of nonlinear infinite approximation, and carry out the research of image fitting low light level image enhancement and denoising. On this basis, the effectiveness of the research results is evaluated. The results show that compared with the traditional remote sensor imaging technology, the PSNR value of the image constructed by the depth neural network algorithm is significantly improved by about 15.2401, the IFC value is improved by about 0.4523, and the SSIM value is improved by about 0.4500, which improves the low light level imaging quality to a higher extent, and provides excellent technical guidance for the remote sensor to obtain the object information at night.
Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods

This study integrates path planning, fuzzy theory, neural networks, image processing, range sensors, webcam, global navigation satellite system (GNSS), and real-time kinematic (RTK) positioning system into an intelligent wheeled mobile robot (WMR) for outdoor patrolling. The robot system uses ultrasound sensors, laser sensors, and fuzzy controllers to detect obstacles and avoid them. The starting position and the goal position of the WMR in an outdoor environment are given by the GNSS RTK positioning system. Real-time position correction of the robot is performed through the differential global positioning system. The robot system applies the ant algorithm and the Dijkstra algorithm to find the shortest path for patrol tasks. The convolutional neural network image processing is utilized to identify intruders that are appearing in the patrol path. When the WMR detects an intruder by the face detection and recognition methods, the robot captures the photo of this person by the webcam and acquires the location information of this person by the RTK positioning system. Then the WMR sends the location and photo of the intruder to the control center by Wi-Fi and asks for help.
MicroSonic Spaces: Towards an Autonomous Ecosystem of Virtual Sonic Agents

MicroSonic Spaces is an interactive audiovisual installation that explores the concept of the creation of live ecosystems of autonomous agents as a non-linear interactive sound and music composition system. This is an ongoing conceptual and artistic research project that evolves with each new presentation. This article introduces the concept and presents a report of the two initial versions of the installation, that were exhibited in Castelo Branco and Aveiro, Portugal, in 2016 and 2018. The base concepts for the original version will be presented, as well as the main developments in the second version.
Human-Object Interaction Detection Based on Multi-scale Attention Fusion

Human-object interaction detection is one of the key issues of scene understanding. It has widespread applications in advanced computer vision technology. However, due to the diversity of human postures, the uncertainty of the shape and size in objects, as well as the complexity of the relationship between people and objects. It is very challenging to detect the interaction relationship between people and objects. To solve this problem, this paper proposes a multi-scale attention fusion method to adapt to people and objects of different sizes and shapes. This method increases the range of attention which can more accurately judge the relationships between people and objects. Besides, we further propose a weighting mechanism to better characterize the interaction between people and close objects and express people’s intention of interaction. We evaluated the proposed method on HICO-DET and V-COCO datasets, which has verified its effectiveness and flexibility as well as has achieved a certain improvement in accuracy.
Weight Vector Tuning and Asymptotic Analysis of Binary Linear Classifiers

Unlike its intercept, a linear classifier&#x0027;s weight vector cannot be tuned by a simple grid search. Hence, this paper proposes weight vector tuning of a generic binary linear classifier through the parameterization of a decomposition of the discriminant by a scalar which controls the trade-off between conflicting informative and noisy terms. By varying this parameter, the original weight vector is modified in a meaningful way. Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning. This yields computational savings as the proposed tuning method reduces to tuning a scalar compared to tuning the native hyperparameter, which may involve repeated weight vector generation along with its burden of optimization, dimensionality reduction, etc., depending on the classifier. It is also found that weight vector tuning significantly improves the performance of Linear Discriminant Analysis (LDA) under high estimation noise. Proceeding from this second finding, an asymptotic study of the misclassification probability of the parameterized LDA classifier in the growth regime where the data dimensionality and sample size are comparable is conducted. Using random matrix theory, the misclassification probability is shown to converge to a quantity that is a function of the true statistics of the data. Additionally, an estimator of the misclassification probability is derived. Finally, computationally efficient tuning of the parameter using this estimator is demonstrated on real data.
Multi-layer Parallelization in Transportation Management Software

In this paper there is introduced a comparative analysis of multi-layer data model used for parallelization of transportation network objects processing in the process of logistics optimization. A concept of multi-layer parallelization is based on distribution of the objects and subjects of a complex transportation logistics system including the road network, pick-up and delivery points, drivers, tracks and trailers, transportation instructions and order flows to the logical layers established considering the requirements of parallel computing. This paper continues this research by study of parallel computing characteristics comparing to traditional approach. The proposed data model allows increasing the performance of parallel computing application in transportation logistics problem domain. The considered performance metrics include execution time, total parallel overhead, speedup, and efficiency. Research results are implemented and applied in practice as a part of a transportation management software solution.
Comparison of Three Filters Space Objects Parameters Estimation via Astrometric and Photometric Data

To ensure safety for in-orbital satellites, it is important to identify space objects. Therefore, the parameters estimation problem of space objects has attracted more and more research interests in aerospace engineering. The unscented Kalman filter (UKF) and the particle filter (PF) are two common estimation methods applied to solve this problem through nonlinear filtering techniques. However, UKF has the disadvantage of low estimation accuracy and divergence. The PF has a high computational cost when applied to estimate parameters of space objects. To solve those two drawbacks, the square root cubature Kalman filter (SRCKF) is applied, which can guarantee high estimation accuracy and robustness while still reducing the computational cost. Simulation results are presented to compare those three filters’ parameter estimation performance of space objects. It is seen that the SRCKF achieves better estimation accuracy than the UKF. The SRCKF and the UKF have higher computational efficiency than the PF.
Writer-Independent Offline Signature Verification Using Deep Siamese Neural Network

Several researchers have worked on signature verification problems from different aspects utilizing insights from signal processing and computer vision, since the last few decades. Despite the advancement in technology, signature model building with an appropriate classifier to distinguish between skilled forgeries and genuine is still a critical problem. This paper presents Siamese neural network-based signature verification system which consists of twin convolutional neural networks with shared weights which maximize the distance between dissimilar pairs while simultaneously minimizing the distance between similar pairs. The signatures are paired among similar (genuine, genuine) and dissimilar (genuine, forged) pairs. This research achieves higher classification accuracy compared to state-of-the-art methods. The results are validated on our created dataset and CEDAR, UTSig, BHSig260, GDPS300, GDPS synthetic signature benchmark datasets.Pawar, Dipti Mannige, Vivek
Cantonese speaker recognition system based on EM algorithm in noisy environments

With the wide application of electronic computers and artificial intelligent machines, people find that the best way of communication between man and machine is voice communication. Nowadays, highly accurate speaker identification systems are required. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. In this paper, Mel-Frequency Cepstrum Coefficient (MFCC) is used to extract speakers features, and Gaussian Mixture Model (GMM) is established for the speakers, Maximum Likelihood Estimate (MLE) is use to estimate the model parameters of GMM. The aim is to identify the Cantonese speaker quickly and accurately in the noisy environment. Experimental results indicate that the method is very effective under noisy environments.
Visual Language for Device Management in Telecommunication Product Line

In the present paper, we consider the task of creating hardware specifications for telecommunication devices that close the communication gap between hardware engineers and software developers. This task has arisen during the development of a family of telecommunication systems at Huawei. The specifications need to describe hardware from the viewpoint of driver development (i.e., device management), omitting many hardware details and including information for automatic generation of driver data structures and signatures. Furthermore, they need to be comprehensive and illustrative for both engineering groups. To meet this challenge, we propose a visual language supporting five views (representations): Structured view (all structural elements of a device), Composite view (all connections of a device), Datapath view (device parts that process the data flow), Control view (device parts that control the data flow processing), and Service view (device parts that provide additional functionality). We present an implementation of the visual language built with the Eclipse Modeling Tools Xtext/EMF/Sirius and integrated into a development environment for device management. We have received positive feedback from the device management software engineers.
Balance the Robustness and Invisibility of Digital Watermarking by Image Entropy in Multiple Domains

Most of the existing digital watermarking techniques hide the secret information into the fixed position of the image with a fixed strength and seldom consider characteristics, such as texture and luminous, of the host image, thus not sufficiently balance the trade-off between the invisibility and robustness. Aiming at this issue, we propose a scheme that can dynamically change the embedding strength and location according to the image features. Therefore, image entropy is applied to appropriately select the embedding, and the embedding strength is adjusted by the just noticeable difference (JND) model, which is calculated in the wavelet domain according to the human visual system (HVS). Moreover, to better resist geometric attacks, the host image is divided into non-overlapping blocks and performed singular value decomposition (SVD). The experimental results implicate that the scheme achieves satisfying imperceptibility and is superior to the similar robust watermarking schemes under common attacks and geometric attacks.
A Recurrent Two-Stage Anatomy-Guided Network for Registration of Liver DCE-MRI

Registration of hepatic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series remains challenging, due to variable uptakes of the agent on different tissues or even the same tissues in the liver. The differences reflect on the intensity variations, which typically makes traditional intensity-based deformable registration methods fail to align small anatomical structures in liver such as vessels. Although deep-learning-based registration methods have become popular because of their superior efficiency for several years, registration of DCE-MRI series with dynamic intensity change is still under tackle. To solve this challenge, we present a two-stage registration network, in which the first stage aligns the whole liver and the second stage focuses on the registration of anatomical structures like vessels and tumors. Furthermore, we adopt a recurrent registration strategy for the deformation refinement. To evaluate our proposed method, we used clinical DCE-MRI series of 60 patients, and registered the arterial phase and the portal venous phase images onto the pre-contrast phases. Experimental results showed that the proposed method achieved a better registration performance than the traditional method (i.e., SyN) and the deep-learning-based method (i.e., VoxelMorph), especially in aligning anatomical structures such as vessel branches in liver.
Cache-Side-Channel Quantification and Mitigation for Quantum Cryptography

Quantum cryptography allows one to transmit secret information securely, based on the laws of quantum physics. It consists of (1) the transmission of physical particles like photons and (2) the software-based processing of measurements during the transmission. Quantum key distribution (QKD), e.g., transmits material for establishing a shared crypto key in this way. The key material is encoded into the particles in a way that leakage can be detected and mitigated via so-called privacy amplification.In this article, we investigate the role of the software implementation for the security of quantum cryptography. More concretely, we quantify the security of QKD software against cache side channels and show how to integrate cache-side-channel mitigation with the privacy amplification in QKD. We evaluate our approach at one variant of a QKD software that is in practical use. During our evaluation, we detect a cache-side-channel vulnerability, for which we develop a parametric mitigation that combines privacy amplification and program rewriting. We propose a cost model for the combined mitigation, which allows one to optimize the interaction between privacy amplification and program rewriting for the mitigation.
Cryptocurrency Technology: Prospects in the Success of Different Organizations

Evolution of cryptocurrency brings all new dimensions of doing banking, but with time and experience we are now trying to implement its fraud free and error free technology in many other non-banking sectors like Government bodies, Corporates, Educational bodies, Charity firms. and few others too. If blockchain technology used in cryptocurrency is infused in organizations, then it can influence productivity and efficiency directly. In this article the technology behind the cryptocurrency is discussed. The paper will also focus about the prospects in organizations.
User Authentication with Graphical Passwords using Hybrid Images and Hash Function

As per human psychology, people remember visual objects more than texts. Although many user authentication mechanisms are based on text passwords, biometric characteristics, tokens, etc., image passwords have proven to be a substitute due to its ease of use and reliability. The technological advancements and evolutions in authentication mechanisms brought greater convenience but increased the probability of exposing passwords through various attacks like shoulder-surfing, dictionary, key-logger, and social engineering attacks. The proposed methodology addresses these vulnerabilities and ensures to keep up the usability of graphical passwords. The system displays hybrid images that users need to recognize and type the randomly generated alphanumeric or special character values associated with each of them. A mechanism to generate One Time Password (OTP) is included for additional security. As a result, it is difficult for an attacker to capture and misuse the password.
The Intelligent Robot Path Planning and Navigation System Based on Openlayers3 Online Map

Abstract The electronic map becomes more and more popular due to its convenience and powerful functions. This paper aims to use map information to build a system for path planning and navigation of an intelligent robot. It can provide a basis for field autonomous identification of crop diseases and insect pests with the robot as the carrier. The open source Openlayers is used as a tool to link online maps. And Dijkstra algorithm is used to obtain the shortest path. And then it can simulate a robot moving along the path by creating a moving point. In addition, it could accomplish a series of fundamental operations of controlling movement and the display of route details. The feasibility of the functions is verified by experiments and it lays a solid foundation for subsequent work.
Topic Modeling for Amharic User Generated Texts

Topic Modeling is a statistical process, which derives the latent themes from extensive collections of text. Three approaches to topic modeling exist, namely, unsupervised, semi-supervised and supervised. In this work, we develop a supervised topic model for an Amharic corpus. We also investigate the effect of stemming on topic detection on Term Frequency Inverse Document Frequency (TF-IDF) features, Latent Dirichlet Allocation (LDA) features and a combination of these two feature sets using four supervised machine learning tools, that is, Support Vector Machine (SVM), Naive Bayesian (NB), Logistic Regression (LR), and Neural Nets (NN). We evaluate our approach using an Amharic corpus of 14,751 documents of ten topic categories. Both qualitative and quantitative analysis of results show that our proposed supervised topic detection outperforms with an accuracy of 88% by SVM using state-of-the-art-approach TF-IDF word features with the application of the Synthetic Minority Over-sampling Technique (SMOTE) and with no stemming operation. The results show that text features with stemming slightly improve the performance of the topic classifier over features with no stemming.