Reviewerly/exHarmony huggingface dataset:
Cheerbot: A Customer Service AI Chatbot for Foam-Pom Pangasinan

Businesses are using digital channels to accommodate customer inquiries. However, customers still complain about long response times or worse, the lack of it. Customers of Foam-Pom Pangasinan usually encounter problems in inquiry and customer service response. This study aims to identify and analyze the design elements, features, architectural model, and usability of the AI chatbot application for the business. The different features for the User and Admin were created that involved the Product List, Schedule Appointment, AI Chatbot, and Feedback. The chatbot architectural model implemented was a retrieval-based model to successfully answer the user's inquiries regarding Foam-Pom. This allows the application to retrieve the most appropriate response to a conversational input from a whole pool of candidate responses. The chatbot was trained to rank the best response from the set of predefined responses through the use of Dialogflow as an engine. The researchers utilized USE Questionnaires for the preliminary testing of 130 respondents from which it received an above-average score and garnered positive perception from the customers. Further studies can: a) create a dynamic application with more design features, b) add more regional dialects, c) add voice generated response and speech-to-text transcription, d) create the system using other architectural models, and e) test the system on a larger number of respondents.
Arabic named entity recognition in social media based on BiLSTM-CRF using an attention mechanism

Named Entity Recognition (NER) is a vitally important task of Natural Language Processing (NLP), which aims at finding named entities in natural language text and classifying them into predefined categories such as persons (PER), places (LOC), organizations (ORG), and so on. In the Arabic context, the current NER approaches based on deep learning are mainly based on word embedding or character-level embedding as input. However, using a single granularity representation has problems with out-of-vocabulary (OOV), word embedding errors, and relatively simple semantic content. This paper presents a multi-headed self-attention mechanism implemented in the BiLSTM-CRF neural network structure to recognize Arabic named entities on social media using two embeddings. Unlike other state-of-the-art approaches, this approach combines character and word embedding at the embedding layer, and the attention mechanism calculates the similarity over the entire sequence of characters and captures local context information. The proposed approach better recognized NEs in Dialect Arabic, reaching an F1 value of 74.15% on Darwish’s dataset (a publicly available Arabic NER benchmark for social media). According to our knowledge, our findings outperform the current state-of-the-art models for Arabic Named Entity Recognition on social media.
Multi-level Encoding and Decoding in a Wavelength-Multiplexed Photonic Tensor Processor

Photonic processors have enabled applications in machine learning and neuromorphic computing. An information encoding/decoding scheme will be critical for scalable photonic computing. We experimentally demonstrate a "multi-level" encoding/decoding scheme and operation procedures.
Constrained Non-Affine Alignment of Embeddings

Embeddings are one of the fundamental building blocks for data analysis tasks. Embeddings are already essential tools for large language models and image analysis, and their use is being extended to many other research domains. The generation of these distributed representations is often a data-and computation-expensive process; yet the holistic analysis and adjustment of them after they have been created is still a developing area. In this paper, we first propose a very general quantitatively measure for the presence of features in the embedding data based on if it can be learned. We then devise a method to remove or alleviate undesired features in the embedding while retaining the essential structure of the data. We use a Domain Adversarial Network (DAN) to generate a non-affine transformation, but we add constraints to ensure the essential structure of the embedding is preserved. Our empirical results demonstrate that the proposed algorithm significantly outperforms the state-of-art unsupervised algorithm on several data sets, including novel applications from the industry.
Effective Learning of Cybersecurity Concepts with Model-Eliciting Activities

As security is a crucial aspect in the process of developing software systems, software engineers must have a strong understanding of security concepts for an application being developed and tested. There has been a growing demand for these skills to be taught on all knowledge levels in computing courses. This paper builds on a study related to a series of security modules designed to meet that demand for teaching security concepts to students in computer science courses. Six small lessons in three security modules are implemented into a CS2 course, and the outcomes of this implementation are assessed. Each concept in the modules is broken up into a general description of the security problem, sample code written in Java, and sample code of the solution. Along with the security modules, an open-ended, problem-solving Model-Eliciting Activity (MEA) was developed as a project for students to demonstrate an understanding of the security concepts. Experimental studies were conducted to investigate the teaching effectiveness of implementing cyber security modules with the MEA project and students' experiences in conceptual modeling tasks in problem solving. After implementing the security modules with the MEA, students showed a good understanding of cyber security concepts, and the instructor's beliefs about teaching shifted from teacher-centered to student-centered. 41.7% of the developed solutions from the MEA groups showed a sufficient degree of creativity, and 58.3% of the solutions seemed suitable for real-world implementation. The initial activities leading to student developed solutions effectively prepared students within the scope of the course, but additional discussion and resources may be necessary to expand on creativity and practicality.
Deck Building in Collectible Card Games using Genetic Algorithms: A Case Study of Legends of Code and Magic

Collectible card games (CCGs) are a kind of game in which players use a variety of cards to achieve the goal of game, for example, defeating the opponent. To play a game, players need to build a card deck, which consists of specified number of cards; only cards in the deck can be used in the game. Deck building is an important part in playing CCGs. This paper addresses deck building in a recently released CCG, Legends of Code and Magic. Our strategy is to evaluate cards by some scoring functions and then select cards into the deck accordingly. We adopt three types of scoring functions and use genetic algorithms (GA) to set proper values of parameters in the scoring functions. We checked the performance of the three versions of deck-building GAs against existing agents and found that scoring by card attributes is effective. We also analyzed the decks built by these algorithms and discussed their pros and cons.
Generation of Adversarial Mechanisms in Deep Neural Networks

Deep learning is a subspace of intelligence system learning that experienced prominent results in almost all the application domains. However, Deep Neural Network found to be susceptible to perturbed inputs such that the model generates output other than the expected one. By including insignificant perturbation to the input effectuate computer vision models to make an erroneous prediction. Though, it is still a dilemma whether humans are prone to comparable errors. In this paper, we focus on this issue by leveraging the latest practices that help to generate adversarial examples in computer vision applications by considering diverse identified parameters, unidentified parameters, and architectures. The analysis of the distinct techniques has been done by considering different common parameters. Adversarial examples are easily transferable while designing computer vision applications that control the condition of the classifications of labels. The finding highlights that some methods like Zoo and Deepfool achieved 100% success for the nontargeted attack but are application-specific.
Learning Sparse Evidence- Driven Interpretation to Understand Deep Reinforcement Learning Agents

Recent advances in machine learning require interpretability and explainability for reliable and trustworthy systems. However, explanations of machine learning models are often hard to achieve given the large amount of information from the complex machine learning models. Evidence-driven reinforcement learning provides snapshot images to understand the learning experiences and the learned behaviors; however, it requires human labor to analyze a large number of retrieved snapshot images. Imposing sparsity of the evidence collection process for interpretation is, thus, significant to make human interpretation easy. In this paper, we proposed novel sparse evidence collection methods to discarding less important images for interpretation. We discuss the trade-offs between the sparsity and re-approximation accuracy and the quality of evidence in different Atari game environments.
Analysis of Key Factors Affecting Undergraduate Entrepreneurship Ability from a Big Data Perspective

To achieve rapid social and economic growth, society has raised expectations for the overall quality and competence of today’s college students in the environment of big data. College students must have a strong spirit of creation and entrepreneurship, as well as a precise level of professional knowledge and practical talents. Based on this backdrop, a weighted K-means clustering method (WK-means) is utilized to investigate the key determinants of college students’ entrepreneurial ability and their interactions with important text content, such as the college student innovation and entrepreneurship competition. To begin, extract a number of characteristics from the sample of entrepreneurial college students, particularly the entrepreneurial ability components. The sample is then divided into two groups. Entrepreneurs who succeed are categorized as positive, while those who fail are classified as negative. The clustering result yields the feature weight of each sample. The weight of each attribute is proportionate to its importance. Second, using the multivariate relationship analysis approach, the correlation between various factors is determined, and the impact of various factors on college students’ entrepreneurial capacity is further investigated. The results of the experiment demonstrate that adaptability and learning ability are the most important characteristics influencing college students’ entrepreneurial aptitude, whereas self-control has little impact.
SPGC: An Integrated Framework of Secure Computation and Differential Privacy for Collaborative Learning

Achieving differential privacy and utilizing secure multiparty computation are the two major approaches used for ensuring privacy in privacy-preserving machine learning. However, the privacy guarantee by existing integration protocols of both approaches for collaborative learning weakens when more participants join the protocols. In this work, we present Secure and Private Gradient Computation (SPGC), a novel collaborative learning framework with a strong privacy guarantee independent of the number of participants while providing high accuracy. The main idea of SPGC is to create noise for the differential privacy within secure multiparty computation. We also created an implementation of SPGC and used it in experiments to measure its accuracy and training time. The results show that SPGC is more accurate than a naive protocol based on local differential privacy by up to 5.6%.
An empirical evaluation of deep learning‐based source code vulnerability detection: Representation versus models

Abstract Vulnerabilities in the source code of the software are critical issues in the realm of software engineering. Coping with vulnerabilities in software source code is becoming more challenging due to several aspects such as complexity and volume. Deep learning has gained popularity throughout the years as a means of addressing such issues. This paper proposes an evaluation of vulnerability detection performance on source code representations and evaluates how machine learning (ML) strategies can improve them. The structure of our experiment consists of three deep neural networks (DNNs) in conjunction with five different source code representations: abstract syntax trees (ASTs), code gadgets (CGs), semantics‐based vulnerability candidates (SeVCs), lexed code representations (LCRs), and composite code representations (CCRs). Experimental results show that employing different ML strategies in conjunction with the base model structure influences the performance results to a varying degree. However, ML‐based techniques suffer from poor performance on class imbalance handling and dimensionality reduction when used in conjunction with source code representations.
Visual-Text Reference Pretraining Model for Image Captioning

People can accurately describe an image by constantly referring to the visual information and key text information of the image. Inspired by this idea, we propose the VTR-PTM (Visual-Text Reference Pretraining Model) for image captioning. First, based on the pretraining model (BERT/UNIML), we design the dual-stream input mode of image reference and text reference and use two different mask modes (bidirectional and sequence to sequence) to realize the VTR-PTM suitable for generating tasks. Second, the target dataset is used to fine tune the VTR-PTM. To the best of our knowledge, VTR-PTM is the first reported pretraining model to use visual-text references in the learning process. To evaluate the model, we conduct several experiments on the benchmark datasets of image captioning, including MS COCO and Visual Genome, and achieve significant improvements on most metrics. The code is available at https://github.com/lpfworld/VTR-PTM.
Quantum Computing for Transport Network Design Problems

Transport Network Design Problem (TNDP) is a well-studied problem for planning and operations of transportation systems. They are widely used to determine links for capacity enhancement, link closures to schedule maintenance, identify new road or transit links and more generally network enhancements under resource constraints. As changes in network capacities result in redistribution of demand on the network, resulting in changes in the congestion patterns, TNDP are generally modelled as a bi-level problem, which are known to be NP-hard. Meta-heuristic methods, such as Tabu Search Method are relied upon to solve these problems, which have been demonstrated to achieve near optimality in reasonable time. The advent of quantum computing has afforded an opportunity to solve these problems faster. We formulate the TNDP problem as bi-level problem, with the upper level formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, that is solved using quantum annealing on a DWAVE quantum computer. We compare the results with Tabu Search. We find that quantum annealing provides globally efficient solutions by orders of magnitude faster. The proposed solution has implications for networks across different contexts including communications, traffic, industrial operations, electricity, water, broader supply chains and epidemiology.
Blockchain Based Energy Trade

In today’s world, there is a growing demand for intelligent applications to make city life an interesting experience and sustainable. This paper introduces a Blockchain based peer-to-peer transaction framework applicable for the distributed clean energy trade. The energy-trading model is designed to initiate transactions autonomously based on the data received from the energy management unit. The application works autonomously to manage the generation, consumption and distribution of clean energy using distributed renewable energy sources. The proposed Decentralized Autonomous transaction platform is an autonomous Blockchain-based peer-to-peer transaction framework that aims to increase clean energy production, consumption and optimization, which indeed will reduce greenhouse gas emissions in metro-cities where the excessive clean energy from the distributed energy sources, including homes, office buildings and factories are autonomously traded among the population inside the city.
A prototype of auto badminton training robot

Modern badminton training machines, which circumvents the requirement of two opposing players for conducting a match, are either expensive or has complex operation. This research involved the prototyping of a shuttlecock robot that enables a badminton player to practice independently. First, a three-dimensional model of the badminton robot was designed. Subsequently, the dynamics of the mechanism were evaluated. Next, the hardware was designed using MATLAB. Finally, the trajectory of the badminton shuttlecock was determined using a series of experimental tests. The as-developed product was reliable and stable, enabling badminton players to practice independently. Moreover, the designed robot can be manufactured at a lower cost than similar machines developed in other studies.
Hybridizing Cuckoo Search with Naked Mole-rat Algorithm: Adapting for CEC 2017 and CEC 2021 Test Suites

Cuckoo search (CS) has proved its worth and is one among the most competitive algorithm for numerical optimization. In order to improve its exploitation properties, this paper presents the hybridization of CS with a recently introduced naked mole-rat algorithm (NMRA). The major modification include <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$i$</tex> ) new techniques based on barebones for global and NMRA for local search respectively are devised, ii) simulated annealing based mating factor for enhanced exploitation iii) an oscillating switch probability to balance between exploration and exploitation, and iv) shrinking population size reduction is used to minimize the computational burden. Apart from that, division of generations and population is also employed. The proposed mutation adaptive CS with MNRA (MaCN) is tested on CEC 2017 and CEC 2021 numerical benchmarks. From the experimental and statistical results, it can be said that MaCN is highly competitive with respect to MVMO, SaDN, JADE, SHADE, CV1.0, CSsin and CVnew algorithms.
Differences in Perceptions of Non-face-to-face Liberal Arts Classes at Universities and their Effect on Satisfaction

In this study, implications for improving the quality of online education in the future were derived by identifying the factors affecting the educational status and class satisfaction of professors and students who have experienced non-face-to-face liberal arts classes at universities. For this purpose, 81 professors who have experience running non-face-to-face liberal arts classes and 560 current students who have taken such classes were the research subjects. As for the contents of the study, the learning experience, immersion, achievement, class satisfaction, continuing course intent, and supplementary points for the improvement of non-face-to-face education in the future were investigated.&lt;/br&gt;As a result of the survey analysis, first, there were statistically significant differences between professors and students regarding class content, evaluation, immersion, achievement, and class satisfaction of the learning experience. &lt;i&gt;appeared to have a positive perception (who appeared to have this perception?&lt;/i&gt;) Second, multiple regression analysis was performed for each professor and student to derive factors affecting overall satisfaction. As a result, in the case of instructors, factors that had a significant effect on class satisfaction were derived from achievement and interaction, and for students, this study confirmed that the degree of immersion, and interaction had a statistically significant effect. Third, as a complement to non-face-to-face liberal arts classes between professors and students, the professors demanded that the classes be conducted without interaction, whereas the students demanded improvement in the quality of video content. In this study, by synthesizing the above results, implications and follow-up studies were suggested for the improvement of non-face-to-face liberal arts classes in the future.
DARNN: Discourse Analysis for Natural languages using RNN and LSTM

Natural language processing (NLP) is a computer science arena concerned with computer connections and human&#x0D; (natural) language connections. One of the significant subfunctions of any application to the NLP is discourse&#x0D; processing. Discourse processing is intended to uncover information from certain information that is understood&#x0D; naturally (NLU). In fulfilling this task, we used special neural networks (RNN with LSTM). Recurrent neural&#x0D; networks are the prerequisites for (RNN). However, RNN does have some of the disadvantages of LSTM (Long&#x0D; Short-Term Memory). Kaggle and Keras are collected with data. The designed system proposed provides LSTM&#x0D; with 72% accuracy. With more text generation deep learning algorithms, the same approach can also be used.
Interaction Analysis of WhatsApp Application Integration in M-Learning

Interaction analysis (IA) describes and analyzes an instructor's verbal interaction with a student (s). The use of interaction analysis has aided in the monitoring of teacher verbal behavior. WhatsApp is also extensively used in language learning, and it is the most widely used program for this function. WhatsApp Interaction was investigated using Language Related Episodes (LRE) and Interaction Analysis to utilize WhatsApp. Eight pairs of pupils were formed from the sampled students. The 16 students were all enrolled in the public university's PYP section. The investigation took place for four weeks, for three days for the class, and the sampled students collaborated in pairs via WhatsApp. Pearson's correlation method was used to demonstrate the association between two or more groups without any modification. Findings indicated that there was a negative correlation between the time spent on WhatsApp and vocabulary scores.
Secure Static Content Delivery for CDN Using Blockchain Technology

A Content Distribution Network (CDN) is a new kind of network to distribute services and content spatially relative to end-users, providing high availability and high performance. The Origin server uses several replicas to reach this goal, but trust issues are present between them and between servers and clients. In this work, we present a proof-of-concept for secure static content delivery (e.g., documents, images) by using Blockchain, a technology with the capability to ensure reliability and trust without a central authority. To test our proposal’s feasibility, we developed a system prototype on the Ethereum private network. The test shows the system’s goodness and the ability to create a new content distribution model over the Internet.
Detecting Fake Online Reviews using Fine-tuned BERT

Fake online reviews are becoming a major problem nowadays with the growing number of online purchases. Recently, natural language processing (NLP) methods that analyze the content of reviews have been increasingly used to detect fake reviews. The problem becomes extremely difficult due to the lack of reliable data caused by the difficulty in labeling fake and honest reviews. In this paper, we not only conduct a structural taxonomy of this topic, but we also present extensive experiments using a state-of-the-art language model BERT (Bidirectional Encoder Representations from Transformers) on different online review datasets. By efficiently fine-tuning this model, we outperform existing detection models by achieving 91% accuracy on the balanced crowdsourced dataset of hotel, restaurant, and doctor reviews and 73% accuracy on the imbalanced third-party Yelp dataset of restaurant reviews.