Reviewerly/exHarmony huggingface dataset:
Email classification via intention-based segmentation

Email is the most popular way of personal and official communication among people and organizations. Due to untrusted virtual environment, email systems may face frequent attacks like malware, spamming, social engineering, etc. Spamming is the most common malicious activity, where unsolicited emails are sent in bulk, and these spam emails can be the source of malware, waste resources, hence degrade the productivity. In spam filter development, the most important challenge is to find the correlation between the nature of spam and the interest of the users because the interests of users are dynamic. This paper proposes a novel dynamic spam filter model that considers the changes in the interests of users with time while handling the spam activities. It uses intention-based segmentation to compare different segments of text documents instead of comparing them as a whole. The proposed spam filter is a multi-tier approach where initially, the email content is divided into segments with the help of part of speech (POS) tagging based on voices and tenses. Further, the segments are clustered using hierarchical clustering and compared using the vector space model. In the third stage, concept drift is detected in the clusters to identify the change in the interest of the user. Later, the classification of ham emails into various categories is done in the last stage. For experiments Enron dataset is used and the obtained results are promising.
Optimality-Based Analysis of XCSF Compaction in Discrete Reinforcement Learning

Learning classifier systems (LCSs) are population-based predictive systems that were originally envisioned as agents to act in reinforcement learning (RL) environments. These systems can suffer from population bloat and so are amenable to compaction techniques that try to strike a balance between population size and performance. A well-studied LCS architecture is XCSF, which in the RL setting acts as a Q-function approximator. We apply XCSF to a deterministic and stochastic variant of the FrozenLake8x8 environment from OpenAI Gym, with its performance compared in terms of function approximation error and policy accuracy to the optimal Q-functions and policies produced by solving the environments via dynamic programming. We then introduce a novel compaction algorithm (Greedy Niche Mass Compaction - GNMC) and study its operation on XCSF's trained populations. Results show that given a suitable parametrisation, GNMC preserves or even slightly improves function approximation error while yielding a significant reduction in population size. Reasonable preservation of policy accuracy also occurs, and we link this metric to the commonly used steps-to-goal metric in maze-like environments, illustrating how the metrics are complementary rather than competitive.
Interactive Natural Language-Based Person Search

In this work, we consider the problem of searching people in an unconstrained environment, with natural language descriptions. Specifically, we study how to systematically design an algorithm to effectively acquire descriptions from humans. An algorithm is proposed by adapting models, used for visual and language understanding, to search a person of interest (POI) in a principled way, achieving promising results without the need to re-design another complicated model. We then investigate an iterative question-answering (QA) strategy that enable robots to request additional information about the POI's appearance from the user. To this end, we introduce a greedy algorithm to rank questions in terms of their significance, and equip the algorithm with the capability to dynamically adjust the length of human-robot interaction according to model's uncertainty. Our approach is validated not only on benchmark datasets but on a mobile robot, moving in a dynamic and crowded environment.
Study on the Prediction of Imbalanced Bank Customer Churn Based on Generative Adversarial Network

Abstract The imbalanced commercial bank customer data will lead to the unpredictability of the minority class. Therefore, this paper proposes an imbalanced data method based on generative adversarial network to deal the problem of poor prediction performance of traditional classifiers for minority class. This paper method is based on the generative adversarial network to generate minority class samples to improve imbalanced data. Finally, the classifier is used to train the balanced data to improve the prediction performance of minority class. In this experiment, the data of a commercial bank customer were measured with indicators such as F1, Precision, and compared with traditional data sampling methods such as SMOTE, BSSMOTE. This method is feasible and applicable to the classification of imbalanced data of banks by observing the experimental results, which has better application value.
ResQ - Smart Safety Band Automated heart rate and fall monitoring system

With an increasing trend of women and elderly people living and travelling alone, their safety has become a matter of utmost importance. In this paper, we propose a novel and innovative Internet of Things (IoT) based solution in the form of a “smart safety band”. The extremely lightweight and mobile smart band is disguised as an innocuous fitness band. Various sensors interface with a microcontroller that transmits data to the web server directly. It can detect falls and sudden attacks manifesting as a spike in heart rate. If the user does not cancel in 30 seconds, a notification with user details, location and type of emergency is sent to emergency services and trusted contacts. The system can function even if the band has been destroyed after sending the first alert. Automation ensures that even if the user is incapacitated, the band automatically signals the server without requiring any user input.
Print vs Digital Reading Comprehension in EFL

Printed texts have long been used as the prime medium of learning to read and reading to learn. However, the ubiquity of technology has emerged digital texts, and the accelerating influx of digital texts requires new comprehension skills and strategies. This article reviews and synthesizes current ideas and research findings on digital reading in EFL context to provide a more solid theoretical basis for digital texts use in reading comprehension programs. Discussion in this article begins with the comparison of the nature of conventional or print reading versus digital reading and the characteristics of printed texts versus digital texts. After that, the discussion proceeds to the findings of relevant studies concerning the effect of digital reading to comprehension, students and instructors' perception of digital texts, and strategies for reading digital texts. Keywords: digital reading, hypertext, printed text, reading comprehension, EFL
Continual Learning of Control Primitives: Skill Discovery via Reset-Games

Reinforcement learning has the potential to automate the acquisition of behavior in complex settings, but in order for it to be successfully deployed, a number of practical challenges must be addressed. First, in real world settings, when an agent attempts a task and fails, the environment must somehow "reset" so that the agent can attempt the task again. While easy in simulation, this could require considerable human effort in the real world, especially if the number of trials is very large. Second, real world learning often involves complex, temporally extended behavior that is often difficult to acquire with random exploration. While these two problems may at first appear unrelated, in this work, we show how a single method can allow an agent to acquire skills with minimal supervision while removing the need for resets. We do this by exploiting the insight that the need to "reset" an agent to a broad set of initial states for a learning task provides a natural setting to learn a diverse set of "reset-skills". We propose a general-sum game formulation that balances the objectives of resetting and learning skills, and demonstrate that this approach improves performance on reset-free tasks, and additionally show that the skills we obtain can be used to significantly accelerate downstream learning.
UCD-CS at W-NUT 2020 Shared Task-3: A Text to Text Approach for COVID-19 Event Extraction on Social Media

In this paper, we describe our approach in the shared task: COVID-19 event extraction from Twitter.The objective of this task is to extract answers from COVID-related tweets to a set of predefined slot-filling questions.Our approach treats the event extraction task as a question answering task by leveraging the transformer-based T5 text-to-text model.According to the official evaluation scores returned, namely F1, our submitted run achieves competitive performance compared to other participating runs (Top 3).However, we argue that this evaluation may underestimate the actual performance of runs based on textgeneration.Although some such runs may answer the slot questions well, they may not be an exact string match for the gold standard answers.To measure the extent of this underestimation, we adopt a simple exact-answer transformation method aiming at converting the well-answered predictions to exactly-matched predictions.The results show that after this transformation our run overall reaches the same level of performance as the best participating run and state-of-the-art F1 scores in three of five COVID-related events.Our code is publicly available to aid reproducibility 1 .
A methodical framework for service oriented architecture adoption: Guidelines, building blocks, and method fragments

Rapidly-changing business requirements expect high business process flexibility that can be achieved using service oriented architecture (SOA). This requires enterprises to adopt SOA and assess their SOA adoption maturity to achieve continuous improvement. SOA realization demands service development with varying levels of granularity. The research aims to develop a methodical framework for SOA realization based on Welke's SOA maturity model, a model that assumes a methodology dimension. The framework is concerned with formalizing knowledge on how to identify and shape the main building blocks of a method at each maturity level. The research applies the principles of design science research and method engineering to develop a methodical framework for SOA realization. The research identifies the gaps in SOA realization methods and illustrates how a methodical framework based on a maturity model facilitates the SOA adoption process. The evaluation results revealed that the framework would help enterprises to select method fragments required at each maturity level to accomplish business excellence. The implications of this research are twofold: from a theoretical perspective, the researchers or practitioners can use the results for further study. From a practical standpoint, enterprises can use the methodical guidelines to assess their current maturity level and select and implement the required method fragments from the method base provided in the proposed framework.
Linear programming bounds for quantum amplitude damping codes

Given that approximate quantum error-correcting (AQEC) codes have a potentially better performance than perfect quantum error correction codes, it is pertinent to quantify their performance. While quantum weight enumerators establish some of the best upper bounds on the minimum distance of quantum error-correcting codes, these bounds do not directly apply to AQEC codes. Herein, we introduce quantum weight enumerators for amplitude damping (AD) errors and work within the framework of approximate quantum error correction. In particular, we introduce an auxiliary exact weight enumerator that is intrinsic to a code space and moreover, we establish a linear relationship between the quantum weight enumerators for AD errors and this auxiliary exact weight enumerator. This allows us to establish a linear program that is infeasible only when AQEC AD codes with corresponding parameters do not exist. To illustrate our linear program, we numerically rule out the existence of three-qubit AD codes that are capable of correcting an arbitrary AD error.
Generating Persuasive Visual Storylines for Promotional Videos

Video contents have become a critical tool for promoting products in E-commerce. However, the lack of automatic promotional video generation solutions makes large-scale video-based promotion campaigns infeasible. The first step of automatically producing promotional videos is to generate visual storylines, which is to select the building block footage and place them in an appropriate order. This task is related to the subjective viewing experience. It is hitherto performed by human experts and thus, hard to scale. To address this problem, we propose WundtBackpack, an algorithmic approach to generate storylines based on available visual materials, which can be video clips or images. It consists of two main parts, 1) the Learnable Wundt Curve to evaluate the perceived persuasiveness based on the stimulus intensity of a sequence of visual materials, which only requires a small volume of data to train; and 2) a clustering-based backpacking algorithm to generate persuasive sequences of visual materials while considering video length constraints. In this way, the proposed approach provides a dynamic structure to empower artificial intelligence (AI) to organize video footage in order to construct a sequence of visual stimuli with persuasive power. Extensive real-world experiments show that our approach achieves close to 10% higher perceived persuasiveness scores by human testers, and 12.5% higher expected revenue compared to the best performing state-of-the-art approach.
A Generic Approach to the Verification of the Permutation Property of Sequential and Parallel Swap-Based Sorting Algorithms

Sorting is one of the fundamental operations in computer science, and many sequential and parallel algorithms have been proposed in the literature. Swap-based sorting algorithms are one category of sorting algorithms where elements are swapped repeatedly to achieve the desired order. Since these algorithms are widely used in practice, their (functional) correctness, i.e., proving sortedness and permutation properties, is of utmost importance. However, proving the permutation property using automated program verifiers is much more challenging as the formal definition of this property involves existential quantifiers. In this paper, we propose a generic pattern to verify the permutation property for any sequential and parallel swap-based sorting algorithm automatically. To demonstrate our approach, we use VerCors, a verification tool based on separation logic for concurrent and parallel programs, to verify the permutation property of bubble sort, selection sort, insertion sort, parallel odd-even transposition sort, quick sort, two in-place merge sorts and TimSort for any arbitrary size of input.
Perfect state transfer on hypercubes and its implementation using superconducting qubits

We propose a protocol for perfect state transfer between any pair of vertices in a hypercube. Given a pair of distinct vertices in the hypercube we determine a sub-hypercube that contains the pair of vertices as antipodal vertices. Then a switching process is introduced for determining the sub-hypercube of a memory enhanced hypercube that facilitates perfect state transfer between the desired pair of vertices. Furthermore, we propose a physical architecture for the pretty good state transfer implementation of our switching protocol with fidelity arbitrary close to unity, using superconducting transmon qubits with tunable couplings. The switching is realised by the control over the effective coupling between the qubits resulting from the effect of ancilla qubit couplers for the graph edges. We also report an error bound on the fidelity of state transfer due to faulty implementation of our protocol.
On Coresets for Support Vector Machines

We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models trained on the coreset are provably competitive with those trained on the original data set. Since the size of the coreset is generally much smaller than the original set, our preprocess-then-train scheme has potential to lead to significant speedups when training SVM models. We prove lower and upper bounds on the size of the coreset required to obtain small data summaries for the SVM problem. As a corollary, we show that our algorithm can be used to extend the applicability of any off-the-shelf SVM solver to streaming, distributed, and dynamic data settings. We evaluate the performance of our algorithm on real-world and synthetic data sets. Our experimental results reaffirm the favorable theoretical properties of our algorithm and demonstrate its practical effectiveness in accelerating SVM training.
Semantics-preserving inlining for metaprogramming

Inlining is used in many different ways in programming languages: some languages use it as a compiler-directive solely for optimization, some use it as a metaprogramming feature, and others lay their design in-between. This paper presents inlining through the lens of metaprogramming and we describe a powerful set of metaprogramming constructs that help programmers to unfold domain-specific decisions at compile-time. In a multi-paradigm language like Scala, the concern for generality of inlining poses several interesting questions and the challenge we tackle is to offer inlining without changing the model seen by the programmer. In this paper, we explore these questions by explaining the rationale behind the design of Scala-3's inlining capability and how it relates to its metaprogramming architecture.
Relationship between Recognition Accuracy and Numerical Precision in Convolutional Neural Network Models

Quantization is an important technique for implementing convolutional neural networks on edge devices. Quantization often requires relearning, but relearning sometimes cannot be always be applied because of issues such as cost or privacy. In such cases, it is important to know the numerical precision required to maintain accuracy. We accurately simulate calculations on hardware and accurately measure the relationship between accuracy and numerical precision.
Investigation on the Difficulties and Challenges of Teachers Online Teaching in Primary and Middle Schools of Guangxi Middle School

Affected by the situation of COVID-19, the new semester has been delayed in many places of the country. In response to the request of the Ministry of Education on “suspended class, ongoing learning” during the epidemic period, schools at all levels throughout the country have carried out online teaching. Through the questionnaire survey, this paper would like to find out the difficulties and challenges facing teachers' online teaching during the epidemic situation, and puts forward some suggestions for online teaching, which may hope to help the promotion of the online teaching.
Floquet engineering of continuous-time quantum walks: Toward the simulation of complex and next-nearest-neighbor couplings

The formalism of continuous-time quantum walks on graphs has been widely used in the study of quantum transport of energy and information, as well as in the development of quantum algorithms. In experimental settings, however, there is limited control over the coupling coefficients between the different nodes of the graph (which are usually considered to be real valued), thereby restricting the types of quantum walks that can be implemented. In this paper, we apply the idea of Floquet engineering in the context of continuous-time quantum walks, i.e., we define periodically driven Hamiltonians which can be used to simulate the dynamics of certain target continuous-time quantum walks. We focus on two main applications: (i) simulating quantum walks that break time-reversal symmetry due to complex coupling coefficients and (ii) increasing the connectivity of the graph by simulating the presence of next-nearest-neighbor couplings. Our paper provides explicit simulation protocols that may be used for directing quantum transport, engineering the dispersion relation of one-dimensional quantum walks, or investigating quantum dynamics in highly connected structures.
ANALISIS TINDAK TUTUR ILOKUSI DALAM VLOG Q&amp;A SESI 3 PADA KANAL YOUTUBE SHERLY ANNAVITA RAHMI

Vlog (video blogging) is a form of blogging activity using vlog medium which represents personality and usually discusses, tells, or discusses about certain content. Besides being entertainment, video blogging also provide knowledge for the audience. One of the vlog that is full of knowledge is the vlog "Q&amp;A Session 3" contained on Sherly Annavita Rahmi's channel on social media Youtube. Please note that the types of vlogs that are often uploaded in the YouTube channel are motivational vlogs, Question &amp; Answers (Q&amp;A), and speakers' opinions on various issues. This study aims to describe the range of illocutionary speech acts that exist in the V&amp;A Q&amp;A Session 3. The research approach is a qualitative descriptive approach. At the stage of data collection, the authors use the observation of referring to downloading vlogs first, and followed by the note taking technique. The author uses the method of referring to downloading vlogs and continued with note taking techniques to collect data. Data analysis was performed by transcription, translation analysis and interpretation, then making conclusions about the types of illocutionary speech acts used by speakers in the V&amp;A Q&amp;A Session 3. The research data is in the form of utterances delivered by Sherly Annavita Rahmi's youtube channel account owner. Based on the data analysis, there are five types of speech acts of are (1) asertive speech acts, including stating, speculating, and mentioning; (2) directive ordering, giving cue, suggesting, and urging; (3) expressive thank you, criticize, and congratulations; (4) commisives, includes offer; and (5) Declarative, including deciding. From this research it is expected to find the types and meanings of illocutionary speech in vlogs that will be useful for reference in life.language, vlog, sherly annavita rahmi, speech acts, illocution
A Robust Real-Time 3D Reconstruction Method for Mixed Reality Telepresence

Mixed Reality (MR) is a technology which enable to bring a virtual element into the real-world environment. MR intends to improve reality on the virtual world immerse onto real-world space. Occasionally the MR has been improved as the display technologies advanced progressively. In MR collaborative interface context, the local and remote user work together on collaborative task while sense the immersive environment in the cooperative application. User telepresence is an immersive telepresence, where the reconstruction of a human appears in a real-life. Up till now, producing full telepresence of the life-size human body may require a high transmission bandwidth of the internet. Therefore, this paper explores on a robust real-time 3D reconstruction method for MR telepresence. This paper discusses the previous works on the reconstruction method of a full-body human and the existing research works that have proposed the reconstruction methods for telepresence. Besides the 3D reconstruction method, this paper also enlightens our recent finding on the MR framework to transport a full-body human from a local location to a remote location. The MR telepresence will be discussed, as well as the robust 3D reconstruction method which has been implemented with user telepresence feature where the user experiences an accurate 3D representation of a remote person. The paper ends with the discussion and results, MR telepresence with robust 3D reconstruction method to execute user telepresence.