Reviewerly/exHarmony huggingface dataset:
A Conceptual IR Chatbot Framework with Automated Keywords-based Vector Representation Generation

Abstract This paper proposes a conceptual remodel of Information Retrieval (IR) chatbot framework designed to eliminate the need for large Question-Answer (QA) pair dataset in chatbot’s machine learning training and knowledge base development. Within ten proposed framework’s components, we describe Ans2Q: a Neural Network model for question type approximation, and HR6: an IR score ranking calculation based on Ans2Q output. Fundamentally, these two components are the variance in which the proposed framework differs from others. Together with process flow explanation, we also provide several related formulas that hopefully can be used to implement this framework. Our general aim with this framework is to provide a tool that can be used to develop close domain chatbot with small knowledge and no readily available QA pair datasets.
Joint Representation Learning of Legislator and Legislation for Roll Call Prediction

In this paper, we explore to learn representations of legislation and legislator for the prediction of roll call results. The most popular approach for this topic is named the ideal point model that relies on historical voting information for representation learning of legislators. It largely ignores the context information of the legislative data. We, therefore, propose to incorporate context information to learn dense representations for both legislators and legislation. For legislators, we incorporate relations among them via graph convolutional neural networks (GCN) for their representation learning. For legislation, we utilize its narrative description via recurrent neural networks (RNN) for representation learning. In order to align two kinds of representations in the same vector space, we introduce a triplet loss for the joint training. Experimental results on a self-constructed dataset show the effectiveness of our model for roll call results prediction compared to some state-of-the-art baselines.
Enhancement of the steepness measurement of a film thickness edge using wavelet transforms with fringe thinning

Demodulation of steep abrupt heights is a challenging task in interferometry because scattering from the steep region is typically weak. In this paper, we enhance the steepness measurement of steep abrupt heights in a phase-contrast image by applying a fringe thinning process before demodulation with the Morlet wavelet transforms. We demonstrate the proposed method with two steep abrupt heights of 200 µm and 30 mm. Use of this method improves the steepness substantially as compared with conventional measurements.
Virtual welding: A didactic perspective

This research was conducted as part of a Technical and Vocational Education and Training (TVET) programme in Technical and Industrial Production and is a collaboration between Aas upper secondary school in Akershus and the Department of Vocational Teacher Education at Oslo Metropolitan University (OsloMet). Starting in autumn 2017, simulated welding has been the first step in the use of simulation teaching in order to produce qualified professionals. In TVET education, Virtual Reality technology is a new training tool for welding, and little is known about the pedagogical approaches and didactic challenges.&#x0D; This article is based on interviews that were conducted with teachers and their immediate supervisors to gain insight into their prior knowledge, the approaches they intended to use, and their initial experiences of the introduction of simulation-based teaching. The research consists primarily of qualitative analyses, based on a phenomenological approach. The focus for the article is the possibilities and limitations of a virtual welding machine, as well as how it might be introduced as an educational and didactic tool in upper secondary school.&#x0D; The article provides useful knowledge for those who will be using some form of simulation in teaching in general and for simulated welding in particular. This applies to both current TVET teachers and those undergoing Technical and Vocational Teacher Education.
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Constant step-size Stochastic Gradient Descent exhibits two phases: a transient phase during which iterates make fast progress towards the optimum, followed by a stationary phase during which iterates oscillate around the optimal point. In this paper, we show that efficiently detecting this transition and appropriately decreasing the step size can lead to fast convergence rates. We analyse the classical statistical test proposed by Pflug (1983), based on the inner product between consecutive stochastic gradients. Even in the simple case where the objective function is quadratic we show that this test cannot lead to an adequate convergence diagnostic. We then propose a novel and simple statistical procedure that accurately detects stationarity and we provide experimental results showing state-of-the-art performance on synthetic and real-world datasets.
Bayesian Inference with Engineered Likelihood Functions for Robust Amplitude Estimation

The number of measurements demanded by hybrid quantum-classical algorithms such as the variational quantum eigensolver (VQE) is prohibitively high for many problems of practical value. Quantum algorithms that reduce this cost (e.g. quantum amplitude and phase estimation) require error rates that are too low for near-term implementation. Here we propose methods that take advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. Our scheme derives inspiration from quantum metrology, phase estimation, and the more recent alpha-VQE proposal, arriving at a general formulation that is robust to error and does not require ancilla qubits. The central object of this method is what we call the engineered likelihood function (ELF), used for carrying out Bayesian inference. We show how the ELF formalism enhances the rate of information gain in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond. Similar to VQE, we expect small-scale implementations to be realizable on today's quantum devices.
Joint Adaptive Feature Smoothing and Topology Extraction via Generalized PageRank GNNs.

In many important applications, the acquired graph-structured data includes both node features and graph topology information. Graph neural networks (GNNs) are able to accurately process both the feature signals and graph topology individually. Nevertheless, they face the problem of trading-off the benefits of a shallow network architecture and deep multi-step information propagation when attempting to optimize their learning performance using both data components. Most existing GNN implementations based on node feature propagation are shallow due to the fact that a large number of propagation steps leads to feature over-smoothing and hence diminishes their discriminative power. In contrast, when processing topological information, it is common to use label propagation and PageRank methods that require a large number of message passing steps. We address these two contradictory requirements by combining GNNs with an adaptive generalized PageRank (GPR) scheme in a model termed GPR-GNN. GPR-GNN is the first known architecture that not only provably mitigates feature over-smoothing but also adaptively learns the weights of the GPR model to optimize topological information extraction. Our theoretical analysis of the GPR-GNN method is facilitated by novel synthetic benchmark datasets generated by the contextual stochastic block model. We also compare the performance of our NN architecture with that of several state-of-the-art GNNs on the problem of node-classification, using nine well-known benchmark datasets. The results demonstrate that GPR-GNN offers significant performance improvement compared to existing techniques on both synthetic and benchmark data.
Deep Learning Based Detection and Channel Tracking for MIMO Systems

In this paper we consider Multiple-Input-Multiple-Output (MIMO) detection and time varying channel tracking using deep neural networks. BiLSTM-DetNet(BD-Net) is considered as a nonlinear projection operator which can be optimized as the maximum likelihood estimate using the projection gradient descent method. The trained BD-Net is also used for channel tracking based on gradient descent method. The numerical results show that the proposed approaches achieve state-of-the-art performance while maintaining fast convergence and low computational complexity requirements.
IMPROVING ARABIC SENTIMENT ANALYSIS ON SOCIAL MEDIA: A COMPARATIVE STUDY ON APPLYING DIFFERENT PRE-PROCESSING TECHNIQUES

Regardless of the clear growth of Arabic texts on social networking sites (SNSs), it is still difficult to understand or summarize users' opinions or perspectives on a specific topic. Accordingly, Arabic text classification is one of the most challenging topics. This is because of several issues related to the nature of the Arabic language and words that have different variation in meaning. In this paper, after tokenizing the Arabic words, we investigate the role of several pre-processing techniques before classifying Arabic text into different categories. Arabic words were converted into vectors using the term frequency-inverse document frequency (TF-IDF) technique. The findings show that applying Linear Support Vector Machine (LSVC) with stop words and without stemming techniques can outperform the application of Decision Tree (DT) and Random Forest (RF) methods. It was found that the effectiveness of the proposed LSVC is 99.37%. These outcomes are significant to identify users' opinions on SNSs and can have many implications on political, social, economic, and business sectors.
Online Safety as a New Component of Digital Literacy for Young People

Introduction. Digital literacy refers to the skills required to achieve digital competence, the confident and critical use of information and communication technology for le arning, leisure, communication and future work of young people. Digital competence has a dual nature. First of all, it is the technical ability to operate programs, pages, equipment. Secondly, it is also the ability to use digital media safely. Both perspectives are important in the educational perspective, i.e. media educat ion and socialisation. Materials and Methods. The paper presents the attempts to measure digital literacy in the area of threats resutling from using the new media in the group of upper-secondary school students (fourth educational cycle). The study was carried out using a diagnostic test with 18 questions. The research was conducted in the group of 1693 youths aged 15–21. The research was designed based on traditional methods of testing knowledge and skills. Results. The findings showed that the weakest digital literacy component was the copyright-related knowledge and the strongest area was online shopping and financial operations. All digital literacy components are interrelated. The improvement in one area leads to the development of other digital literacy elements. Despite this correlation, digital literacy is a heterogeneous concept. There are also differences regarding certain digital literacy components, determined by gender – girls obtained higher test results in terms of the soft competencies whereas boys were better with the technical aspects of digital literacy. Based on the cluster analysis, we noticed that 41.41% of the students obtained good and very good results from the competence test. More than half of the students require further education in most of the analysed areas. Discussion and Conclusion. For educational decision-makers, the findings highlight the importance of designing training programs aimed at developing students’ digital literacies, with a special focus on new topics as sexting, piracy and cyberbullying.
Beyond Knowledge and Skills: Exploring Leadership Motivation as a Critical Construct for Student Leadership Development

Theorists position leadership capacity, leadership self-efficacy, and leadership motivation as central constructs in the leadership development process (Chan & Drasgow, 2001; Dugan, 2017). Although researchers have explored distinct connections between psychological constructs of leadership capacity and leadership self-efficacy, less attention has been given to leadership motivation and its collective connection with these constructs. Findings from this scholarship confirm theorized relationships between these pivotal constructs and situate leadership motivation as a critical component of the developmental process for socially responsible leadership.
Blogging in Extensive Reading: Students’ Voice in Blended Learning Classroom

The use of a blog as a platform for extensive reading becomes valuable since it provides the possibility of readership and opportunity for interaction and collaboration. As the online journal facilitating users to display information in chronological order and connected to the Internet, the blog also can help students to get a wide variety of reading materials, which should be conducted in extensive reading activities. However, the use of a blog in this study is integrated with the process of learning in the classroom so that the students can use it for reporting their reading activities and share what they read beyond the classroom. This study is conducted during one semester in one of the universities in Indonesia using six students of the first grade joining an extensive reading course as the participants. In this research, a case study is used as a research method. It aims at analyzing the students' perception of the use of blogs in extensive reading. Findings show that the blog is beneficial for promoting the students' reading interest, building autonomy and creativity, and providing a reflective platform. The blog consists of several features allowing students to get the numerous reading sources that are appropriate with their language level and interest. Therefore, it becomes useful since it can facilitate students to build their responsibility to select their reading material and to reflect the progress of their learning activities.
Target detection algorithm based on improved multi-scale SSD

Abstract The traditional SSD algorithm has a serious abstraction of feature extraction content, which makes it difficult to achieve effective detection of small targets. At the same time, the problem of feature layer fusion is difficult due to different scales. In this paper, an improved SSD based target detection algorithm is proposed. By introducing feature enhancement method, the adjustment steps of high-level feature size are omitted, Which makes it unnecessary to reduce the dimension of features, and at the same time, it uses the multi-scale candidate area which accords with the proportion of pedestrians in the detection network to enhance the feature extraction ability of small targets, effectively improves the accuracy and operation speed of SSD algorithm, and saves the loss of the network.
Certifying the Weighted Path Order (Invited Talk).

The weighted path order (WPO) unifies and extends several termination proving techniques that are known in term rewriting. Consequently, the first tool implementing WPO could prove termination of rewrite systems for which all previous tools failed. However, we should not blindly trust such results, since there might be problems with the implementation or the paper proof of WPO.In this work, we increase the reliability of these automatically generated proofs. To this end, we first formally prove the properties of WPO in Isabelle/HOL, and then develop a verified algorithm to certify termination proofs that are generated by tools using WPO. We also include support for max-polynomial interpretations, an important ingredient in WPO. Here we establish a connection to an existing verified SMT solver. Moreover, we extend the termination tools NaTT and TTT2, so that they can now generate certifiable WPO proofs.
NIZK from SNARG

We give a construction of a non-interactive zero-knowledge (NIZK) argument for all $$\mathsf {NP}$$ languages based on a succinct non-interactive argument (SNARG) for all $$\mathsf {NP}$$ languages and a one-way function. The succinctness requirement for the SNARG is rather mild: We only require that the proof size be $$|\pi |=\mathsf {poly}(\lambda )(|x|+|w|)^c$$ for some constant $$c<1/2$$ , where |x| is the statement length, |w| is the witness length, and $$\lambda $$ is the security parameter. Especially, we do not require anything about the efficiency of the verification. Based on this result, we also give a generic conversion from a SNARG to a zero-knowledge SNARG assuming the existence of CPA secure public-key encryption. For this conversion, we require a SNARG to have efficient verification, i.e., the computational complexity of the verification algorithm is $$\mathsf {poly}(\lambda )(|x|+|w|)^{o(1)}$$ . Before this work, such a conversion was only known if we additionally assume the existence of a NIZK. Along the way of obtaining our result, we give a generic compiler to upgrade a NIZK for all $$\mathsf {NP}$$ languages with non-adaptive zero-knowledge to one with adaptive zero-knowledge. Though this can be shown by carefully combining known results, to the best of our knowledge, no explicit proof of this generic conversion has been presented.
Hybrid approach for semantic similarity calculation between Tamil words

Semantic similarity, sometimes referred as semantic relatedness, is one of the important concepts that help in various applications that involve natural language processing. In literature, there are plenty of similarity measures to compute the relationship among words in monolingual and cross-lingual documents. They help us in understanding text, finding plagiarism, information retrieval etc. They can be categorised based on the resources used into corpus-based and knowledge-based measures. These measures are plenty for the English language. For the Tamil language, there are hardly any works in calculating the similarity between words. In this paper, we proposed a similarity finding technique that exploits the knowledge from the resources like Tamil Indo WordNet, Tamil Wikitionary and Oxford Tamil Dictionary. We have used the definitions and example sentences of each word that are available through each of these resources for similarity calculation. The proposed approach is evaluated using human evaluated Miller Charles and Rubenstein Goodenough datasets.
Application-Oblivious L7 Parsing Using Recurrent Neural Networks

Extracting fields from layer 7 protocols such as HTTP, known as L7 parsing, is the key to many critical network applications. However, existing L7 parsing techniques center around protocol specifications, thereby incurring large human efforts in specifying data format and high computational/memory costs that poorly scale with the explosive number of L7 protocols. To this end, this paper introduces a new framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">content-based L7 parsing</i> , where the content instead of the format becomes the first class citizen. Under this framework, users only need to label what content they are interested in, and the parser learns an extraction model from the users’ labeling behaviors. Since the parser is specification-independent, both the human effort and computational/memory costs can be dramatically reduced. To realize content-based L7 parsing, we propose REPLAY which builds on recurrent neural network (RNN) and addresses a series of technical challenges like large labeling overhead and slow parsing speed. We prototype REPLAY on GPUs, and show it can achieve a precision of 98% and a recall of 97%, with a throughput as high as 12Gbps for diverse extraction tasks.
Back to the Future for Dialogue Research

This “blue sky” paper argues that future conversational systems that can engage in multiparty, collaborative dialogues will require a more fundamental approach than existing technology. This paper identifies significant limitations of the state of the art, and argues that our returning to the plan-based approach to dialogue will provide a stronger foundation. Finally, I suggest a research strategy that couples neural network-based semantic parsing with plan-based reasoning in order to build a collaborative dialogue manager.
Experimenting with Generative Adversarial Networks to Expand Sparse Physiological Time-Series Data.

Machine Learning research and its application have gained enormous relevance in recent years. Their usage in medical settings could support patients, increase patient safety and assist health professionals in various tasks. However, medical data is often sparse, which renders big data analytics methods like deep learning ineffective. Data synthesis helps to augment small data sets and potentially improves patient data integrity. The presented work illustrates how Generative Adversarial Networks can be applied specifically to small data sets for enlarging sparse data. Following a state-of-the-art analysis is conducted, experimental methods with such networks are documented, which have been applied to three different data sets. Results from all three sets are presented and take-away messages are summarized. Concluding, the results' quality and limitations of the work are discussed.
Epistemic Plan Recognition

The plan recognition task is to infer an actor's plan and goal given observations about its behavior. We submit that in some cases, for plan recognition to be effective and complete, it must appeal to a notion of epistemics to i) recognize epistemic goals, where the actor is trying to achieve some state of knowledge or belief; and ii) model the observer, and its knowledge of the actor, as first class elements of the plan recognition process. To this end, we formalize the notion of Epistemic Plan Recognition, which builds on two growing areas of research: epistemic planning and plan recognition. Our epistemic plan recognition specification appeals to an epistemic logic framework to represent agent beliefs. To realize our specification, we cast the epistemic plan recognition problem as an epistemic planning problem, whose solutions can be generated using existing epistemic planning tools. Finally, we evaluate our approach by utilizing and comparing existing epistemic planners on a diverse set of epistemic plan recognition problems.
On the primitive roots and the generalized Golomb's conjecture

&lt;abstract&gt; &lt;p&gt;In this article, we use elementary methods and the estimate for character sums to study the properties of a certain primitive roots modulo &lt;italic&gt;p&lt;/italic&gt; (an odd prime), and prove that the generalized Golomb's conjecture is correct in a reduced residue system modulo &lt;italic&gt;p&lt;/italic&gt;. This solved an open problem proposed by W. P. Zhang and T. T. Wang in &lt;sup&gt;[&lt;xref ref-type="bibr" rid="b3"&gt;3&lt;/xref&gt;]&lt;/sup&gt;.&lt;/p&gt; &lt;/abstract&gt;