Reviewerly/exHarmony huggingface dataset:
Adversarial Attack Generation Empowered by Min-Max Optimization

The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness. Nevertheless, min-max optimization beyond the purpose of AT has not been rigorously explored in the adversarial context. In this paper, we show how a general framework of min-max optimization over multiple domains can be leveraged to advance the design of different types of adversarial attacks. In particular, given a set of risk sources, minimizing the worst-case attack loss can be reformulated as a min-max problem by introducing domain weights that are maximized over the probability simplex of the domain set. We showcase this unified framework in three attack generation problems -- attacking model ensembles, devising universal perturbation under multiple inputs, and crafting attacks resilient to data transformations. Extensive experiments demonstrate that our approach leads to substantial attack improvement over the existing heuristic strategies as well as robustness improvement over state-of-the-art defense methods trained to be robust against multiple perturbation types. Furthermore, we find that the self-adjusted domain weights learned from our min-max framework can provide a holistic tool to explain the difficulty level of attack across domains. Code is available at https://github.com/wangjksjtu/minmax-adv.
Towards a Better Understanding of Noise in Natural Language Processing

In this paper, we propose a definition and taxonomy of various types of non-standard textual content -generally referred to as "noise"in Natural Language Processing (NLP).While data pre-processing is undoubtedly important in NLP, especially when dealing with usergenerated content, a broader understanding of different sources of noise and how to deal with them is an aspect that has been largely neglected.We provide a comprehensive list of potential sources of noise, categorise and describe them, and show the impact of a subset of standard pre-processing strategies on different tasks.Our main goal is to raise awareness of non-standard content -which should not always be considered as "noise" -and of the need for careful, task-dependent preprocessing.This is an alternative to blanket, all-encompassing solutions generally applied by researchers through "standard" preprocessing pipelines.The intention is for this categorisation to serve as a point of reference to support NLP researchers in devising strategies to clean, normalise or embrace nonstandard content.
Controlling Machine Translation for Multiple Attributes with Additive Interventions

Fine-grained control of machine translation (MT) outputs along multiple attributes is critical for many modern MT applications and is a requirement for gaining users' trust.A standard approach for exerting control in MT is to prepend the input with a special tag to signal the desired output attribute.Despite its simplicity, attribute tagging has several drawbacks: continuous values must be binned into discrete categories, which is unnatural for certain applications; interference between multiple tags is poorly understood.We address these problems by introducing vector-valued interventions which allow for fine-grained control over multiple attributes simultaneously via a weighted linear combination of the corresponding vectors.For some attributes, our approach even allows for fine-tuning a model trained without annotations to support such interventions.In experiments with three attributes (length, politeness and monotonicity) and two language pairs (English to German and Japanese) our models achieve better control over a wider range of tasks compared to tagging, and translation quality does not degrade when no control is requested.Finally, we demonstrate how to enable control in an already trained model after a relatively cheap fine-tuning stage.
Aspect-based Sentiment Analysis in Question Answering Forums

Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews.Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects.This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair.Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question and the associated opinion clues in the answer.To this end, we propose a model with a specific design of crosssentence aspect-opinion interaction modeling to address this task.The proposed method is evaluated on three real-world datasets and the results show that our model outperforms several strong baselines adopted from related state-of-the-art models.
Design and construction of a solar tracking system for Linear Fresnel Concentrator

An open circuit solar tracking system has been designed, manufactured, simulated and implemented for a handcrafted prototype of a small scale linear Fresnel reflector with a single axis of motion. The electronic control system is governed by an Arduino UNO R3 board and two main auxiliary systems; a Microstep Driver TB6600 and an RTC DS1302 module. Further, a mechanism was implemented that joins the reflectors to a stepper motor that executes a single movement per sequence, to move the set of reflector mirrors that make up the reflection system of the device. The positioning angles of the reflectors determined by the control algorithm models for solar tracking, allowed to feed the TONATIUH software to evaluate the path of the solar rays through the 3D modeling of the Linear Fresnel reflector on a real scale. The software was designed to follow the path of the sun by means of astronomical equations. In this way, the mirrors of the Linear Fresnel reflector can follow the solar path on a single axis from 7 am to 5 pm, making changes in the position of each element in 15-minute intervals. The percentage of position deviation of the reflectors does not exceed 1% between the location of the full-scale system mirrors and the position angles provided by the control algorithm. The efficiency of the implemented automatic solar tracking system increased by more than 50% compared to the manual tracking system.
The impact of e-commerce drivers on e-customer loyalty: Evidence from KSA

E-commerce and online shopping is the future of global trade, hence the importance of building E-customer loyalty and maintaining it in the electronic markets becomes even more important. This paper attempts to identify factors affecting the value of electronic commerce in the Saudi environment. Likewise, it explores the impact of these factors on E- customer loyalty. Simple random sampling was used for the purposes of the current study, as the first study included 247 electronic customers. Data were collected through electronic questionnaires sent to the study participants. The results show that the factors had a positive impact in building E- customer loyalty Care, Character, Choice, Convenience, Customization, and Cultivation. Basically, e-commerce is considered a pioneer in business organizations today, and this is witnessed by its increasing and growing profits and expanding its market share, through its focus on the E-loyalty customers. Basically, e-commerce is considered a pioneer in business organizations today, and this is witnessed by its increasing and growing profits and expanding its market share, through its focus on the E-loyalty customers.
A Ubiquitous Clinic Recommendation System Using the Modified Mixed-Binary Nonlinear Programming-Feedforward Neural Network Approach

Most of the existing ubiquitous clinic recommendation (UCR) systems adopt linear mechanisms to aggregate the attribute-level performances of a clinic to evaluate the overall performance. However, such linear mechanisms may not be able to explain the choices of all patients. To solve this problem, the modified mixed binary nonlinear programming (MMBNLP)–feedforward neural network (FNN) approach is proposed in this study. In the proposed methodology, first, the existing MBNLP model is modified to improve the successful recommendation rate using a linear recommendation mechanism. Subsequently, an FNN is constructed to fit the relationship between the attribute-level performances of a clinic and its overall performance, thereby providing possible ways to further enhance the recommendation performance. The results of a regional experiment showed that the MMBNLP–FNN approach improved the successful recommendation rate by 30%.
Local Technology to Enhance Data Privacy and Security in Educational Technology

In educational environments, technological adoption in the last 10 years has enabled a data-driven and decisionmaking paradigm in organizations.The integration of cloud services in schools and universities is a positive shift in the field of learning, but it also presents threats to all academic roles that need to be discussed in terms of protection, privacy, and confidentiality.Cloud storage brings the ubiquity of data to this technical transition and a delusive opportunity for cost savings.In many cases, this suggests that certain actors, beyond the control of schools and colleges, collect, handle and treat educational data on private servers and data centers.This privatization enables the manipulation of stored records, leaks, and unauthorized access.In this article, we expose the possibilities that open from the viewpoint of local technology adoption.We seek to reduce or even totally solve the detrimental effects of using cloud-based instructional and analytical technology, mixing or only using local technology.Technological methods that conform to this alternate viewpoint and new lines of study are also being suggested and created.
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification

The graph neural network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered. Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets are no longer applicable in GNN. This study proposes an ensemble model called Boosting-GNN, which uses GNNs as the base classifiers during boosting. In Boosting-GNN, higher weights are set for the training samples that are not correctly classified by the previous classifiers, thus achieving higher classification accuracy and better reliability. Besides, transfer learning is used to reduce computational cost and increase fitting ability. Experimental results indicate that the proposed Boosting-GNN model achieves better performance than graph convolutional network (GCN), GraphSAGE, graph attention network (GAT), simplifying graph convolutional networks (SGC), multi-scale graph convolution networks (N-GCN), and most advanced reweighting and resampling methods on synthetic imbalanced datasets, with an average performance improvement of 4.5%.
How Do Investors Perceive the Materiality of Data Security Incidents

Data security incidents are continually increasing; hackers, governments, and other actors increasingly attempt to gain unauthorized access to confidential data. Information Systems (IS) users are becoming more vulnerable to the risks of data breaches. Many stakeholders perceive cybersecurity incidents as indicators of firms' operational and technological internal deficiencies. Previous research has revealed that investors react negatively to data breaches, yet little is known about investors' reactions to material data security incidents. Using a sample of 232 data security incidents for 132 publicly traded companies in the United States, we applied an event study methodology to discern investors' reactions to material versus immaterial incidents. We also use multivariate regression and time-to-event analysis to examine what determines the degree of investors' reactions, considering several intervals around the event day. Our results show that investors perceive material data security incidents as a deficiency of breached companies in comparison to immaterial incidents.
DATLMedQA: A Data Augmentation and Transfer Learning Based Solution for Medical Question Answering

With the outbreak of COVID-19 that has prompted an increased focus on self-care, more and more people hope to obtain disease knowledge from the Internet. In response to this demand, medical question answering and question generation tasks have become an important part of natural language processing (NLP). However, there are limited samples of medical questions and answers, and the question generation systems cannot fully meet the needs of non-professionals for medical questions. In this research, we propose a BERT medical pretraining model, using GPT-2 for question augmentation and T5-Small for topic extraction, calculating the cosine similarity of the extracted topic and using XGBoost for prediction. With augmentation using GPT-2, the prediction accuracy of our model outperforms the state-of-the-art (SOTA) model performance. Our experiment results demonstrate the outstanding performance of our model in medical question answering and question generation tasks, and its great potential to solve other biomedical question answering challenges.
Stabilization of product states and excited-state quantum phase transitions in a coupled qubit-field system

We study a system of a single qubit (or a few qubits) interacting with a soft-mode bosonic field. Considering an extended version of the Rabi model with both parity-conserving and parity-violating interactions, we disclose a complex arrangement of quantum phase transitions in the ground- and excited-state domains. An experimentally testable signature of some of these transitions is a dynamical stabilization of a fully factorized qubit-field state involving the field vacuum. It happens in the ultrastrong coupling regime where the superradiant field equilibrium is far from the vacuum state. The degree of stabilization varies abruptly with interaction parameters and increases with the softness of the field mode. We analyze semiclassical origins of these effects and show their connection to various forms of excited-state quantum phase transitions.
Polyethylene Terephthalate Waste Utilisation for Production of Low Thermal Conductivity Cement Sand Bricks

There is a tremendous increase in plastic waste that negatively impacts the environment due to various industrial activities. Furthermore, plastic waste has non-biodegradable properties that make it hard to reduce its accumulation around the globe. Hence, this study aims to investigate the possibility of incorporating Polyethylene terephthalate (PET) waste as a partial replacement material of sand to improve the thermal insulation properties of cement sand brick by looking at findings of low thermal conductivity value. The study uses a PET plastic bottle that has been cut into small flakes and grind using a granulator machine to produce PET waste granules whose size is not more than 5 mm, similar to the sand size. This waste was added to other raw materials, i.e., cement and sand. The percentages of PET waste vary from 2.5%, 5%, and 7.5% by weight. This study produced two types of samples, i.e., control brick and PET waste cement sand brick. All samples undergo laboratory works involving geotechnical gradation, physical, mechanical, and thermal conductivity testing. Based on the results obtained, the optimum proportion of PET waste replacement in cement sand bricks making is 5% by its having the lowest thermal conductivity value of 0.581 W/mK and meeting the standard requirements of 3.90 MPa &gt; 3.45 MPa (ASTM C129-11 for compressive strength), and 2,146 kg/m3 &gt; 2,000 kg/m3 (ASTM C129-11 for normal weight non-loadbearing brick). Thus, PET plastic bottle waste can be a potential partial sand replacement material in cement sand bricks. Its potential to enhance the thermal conductivity of existing cement sand brick reduces sand consumption, solves plastic waste problems, and promotes a better environmentally-friendly construction industry.
A Survey of Domain Knowledge Elicitation in Applied Machine Learning

Eliciting knowledge from domain experts can play an important role throughout the machine learning process, from correctly specifying the task to evaluating model results. However, knowledge elicitation is also fraught with challenges. In this work, we consider why and how machine learning researchers elicit knowledge from experts in the model development process. We develop a taxonomy to characterize elicitation approaches according to the elicitation goal, elicitation target, elicitation process, and use of elicited knowledge. We analyze the elicitation trends observed in 28 papers with this taxonomy and identify opportunities for adding rigor to these elicitation approaches. We suggest future directions for research in elicitation for machine learning by highlighting avenues for further exploration and drawing on what we can learn from elicitation research in other fields.
Quantum approximate optimization for hard problems in linear algebra

The quantum approximate optimization algorithm (QAOA) by Farhi et al. is a quantum computational framework for solving quantum or classical optimization tasks. Here, we explore using QAOA for binary linear least squares (BLLS); a problem that can serve as a building block of several other hard problems in linear algebra, such as the non-negative binary matrix factorization (NBMF) and other variants of the non-negative matrix factorization (NMF) problem. Most of the previous efforts in quantum computing for solving these problems were done using the quantum annealing paradigm. For the scope of this work, our experiments were done on noiseless quantum simulators, a simulator including a device-realistic noise-model, and two IBM Q 5-qubit machines. We highlight the possibilities of using QAOA and QAOA-like variational algorithms for solving such problems, where trial solutions can be obtained directly as samples, rather than being amplitude-encoded in the quantum wavefunction. Our numerics show that even for a small number of steps, simulated annealing can outperform QAOA for BLLS at a QAOA depth of p\leq3 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>≤</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math> for the probability of sampling the ground state. Finally, we point out some of the challenges involved in current-day experimental implementations of this technique on cloud-based quantum computers.
Study of Keyword Extraction Techniques for Electric Double-Layer Capacitor Domain Using Text Similarity Indexes: An Experimental Analysis

Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert provided keywords from the Electric Double Layer Capacitor (EDLC) domain. The paper also analyses which texts provide better keywords like positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques.
Automated Fetal Head Classification and Segmentation Using Ultrasound Video

During pregnancy, fetal ultrasound provides essential insight into a baby's growth and development.In this ultrasound, accurate assessment of fetal head biometry is critical to the clinical management of pregnancy.Current methodologies used for fetal head biometry heavily rely upon sonographer skills and experience to locate a baby's head.In this paper a novel approach is proposed to automate the fetal head biometry using live ultrasound feed; which is also capable of tackling low abdominal contrast against surroundings.Proposed model is trained on ALEXNET and UNET for classification and segmentation of headframes respectively from ultrasound video.To compute biparietal diameter (BPD) and head circumference (HC); which are essential requirements to compute gestational age, an ellipse is drawn on the contour of the annotated segmented fetal head.It should be noted that to validate gestational age estimate, ellipse are drawn on multiple best classified headframes obtained using ALEXNET.The proposed system is able to estimate gestational age within clinically acceptable ± one week of observed gestational age with an accuracy of 96%.Moreover, it uses robust machine vision features to reduce the sonographer's interaction with the system, thus reducing the overall procedure time and making it independent of sonographer's skill.
Analysis on the Application of Multimedia-Assisted Music Teaching Based on AI Technology

In order to improve the effect of modern music teaching, this paper combines AI technology to construct a multimedia-assisted music teaching system, combines music teaching data processing requirements to improve the algorithm, proposes appropriate music data filtering algorithms, and performs appropriate data compression processing. Moreover, the functional structure analysis of the intelligent music teaching system is carried out with the support of the improved algorithm, and the three-tier framework technology that is currently more widely used is used in the music multimedia teaching system. Finally, in order to realize the complex functions of the system, the system adopts a layered approach. From the experimental research results, it can be seen that the multimedia-assisted music teaching system based on AI technology proposed in this paper can effectively improve the effect of modern music teaching.