Reviewerly/exHarmony huggingface dataset:
An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models

Warning: This work deals with statements of a stereotypical nature that may be upsetting.Bias research in NLP is a rapidly growing and developing field.Similar to CrowS-Pairs (Nangia et al., 2020), we assess gender bias in masked-language models (MLMs) by studying pairs of sentences that are identical except that the individuals referred to have different gender.Most bias research focuses on and often is specific to English.Using a novel methodology for creating sentence pairs that is applicable across languages, we create, based on CrowS-Pairs, a multilingual dataset for English, Finnish, German, Indonesian and Thai.Additionally, we propose S JSD , a new bias measure based on Jensen-Shannon divergence, which we argue retains more information from the model output probabilities than other previously proposed bias measures for MLMs.Using multilingual MLMs, we find that S JSD diagnoses the same systematic biased behavior for non-English that previous studies have found for monolingual English pre-trained MLMs.S JSD outperforms the CrowS-Pairs measure, which struggles to find such biases for smaller non-English datasets.
Trees probe deeper than strings: an argument from allomorphy

Linguists disagree on whether morphological representations should be strings or trees.We argue that tree-based views of morphology can provide new insights into morphological complexity even in cases where the posited tree structure closely matches the surface string.Our argument is based on a subregular case study of morphologically conditioned allomorphy, where the phonological form of some morpheme (the target) is conditioned by the presence of some other morpheme (the trigger) somewhere within the morphosyntactic context.The trigger and target can either be linearly adjacent or non-adjacent, and either the trigger precedes the target (inwardly sensitive) or the target precedes the trigger (outwardly sensitive).When formalized as string transductions, the only complexity difference is between local and non-local allomorphy.Over trees, on the other hand, we also see a complexity difference between inwardly sensitive and outwardly sensitive allomorphy.Just as unboundedness assumptions can sometimes tease apart patterns that are equally complex in the finitely bounded case, tree-based representations can reveal differences that disappear over strings.
Alternative non-BERT model choices for the textual classification in low-resource languages and environments

Natural Language Processing (NLP) tasks in non-dominant and low-resource languages have not experienced significant progress.Although pre-trained BERT models are available, GPU-dependency, large memory requirement, and data scarcity often limit their applicability.As a solution, this paper proposes a fusion chain architecture comprised of one or more layers of CNN, LSTM, and BiLSTM and identifies precise configuration and chain length.The study shows that a simpler, CPU-trainable non-BERT fusion CNN + BiLSTM + CNN is sufficient to surpass the textual classification performance of the BERT-related models in resource-limited languages and environments.The fusion architecture competitively approaches the state-of-the-art accuracy in several Bengali NLP tasks and a six-class emotion detection task for a newly developed Bengali dataset.Interestingly, the performance of the identified fusion model, for instance, CNN + BiLSTM + CNN, also holds for other lowresource languages and environments.Efficacy study shows that the CNN + BiLSTM + CNN model outperforms BERT implementation for Vietnamese languages and performs almost equally in English NLP tasks experiencing artificial data scarcity.For the GLUE benchmark and other datasets such as Emotion, IMDB, and Intent classification, the CNN + BiLSTM + CNN model often surpasses or competes with BERT-base, TinyBERT, DistilBERT, and mBERT.Besides, a position-sensitive selfattention layer role further improves the fusion models' performance in the Bengali emotion classification.The models are also compressible to as low as ≈ 5× smaller through pruning and retraining, making them more viable for resource-constrained environments.Together, this study may help NLP practitioners and serve as a blueprint for NLP model choices in textual classification for low-resource languages and environments.
Harmless Transfer Learning for Item Embeddings

Learning embedding layers (for classes, words, items, etc.) is a key component of lots of applications, ranging from natural language processing, recommendation systems to electronic health records, etc.However, the frequency of real-world items follows a long-tail distribution in these applications, causing naive training methods perform poorly on the rare items.A line of previous works address this problem by transferring the knowledge from the frequent items to rare items by introducing an auxiliary transfer loss.However, when defined improperly, the transfer loss may introduce harmful biases and deteriorate the performance.In this work, we propose a harmless transfer learning framework that limits the impact of the potential biases in both the definition and optimization of the transfer loss.On the definition side, we reduce the bias in transfer loss by focusing on the items to which information from high-frequency items can be efficiently transferred.On the optimization side, we leverage a lexicographic optimization framework to efficiently incorporate the information of the transfer loss without hurting the minimization of the main prediction loss function.Our method serves as a plug-in module and significantly boosts the performance on a variety of NLP and recommendation system tasks.
The Defence of 2d Poisoning Attack

The poisoning attack as one of the adversarial machine learning attack has become severe threat to many artificial intelligent systems which use machine learning (ML) and deep learning (DL). Those systems can be re-trained us ing data collected during operations. With such an poisoning attack, the attackers can possibly evade those systems by disrupting the retraining. Especially, the adversarial machine learning combined with malware has been developed [3], which increases the possibility for malware to evade detection systems. Even though there are some academic outcomes for that, [3, 1, 2]. there are no enough strategic tools in industry yet because of wide attack surfaces [4]. We took the malware as our experiment object to learn the influence from poisoning attack and also explore the strategic defence technology under 2D data. In order to prevent such an attack efficiently, we explored the com bined approach with generative neural network (GAN) and ensemble train ing. Moreover, we simulated the whole-stage process from data collection, feature extraction and detection to final defence. During our experiments, we found some components, including combination of merged classifiers, dif ferent hidden layers, number of units and test size all have influences to the prevention performance for poisoning attack. With some specific configura tions, including automatic ratios for merged classifiers, optimized numbers for hidden layers, number of units and test size, the detection model for our generated ransomware dataset can achieve steady accuracy above 95% under adversarial one dimensional (2D) input. In order to verify the robustness.
CALint: A Tool for Enforcing the Clean Architecture’s Dependency Rule in Python

Clean Architecture (CA) aims to address the need for more loosely coupled components and better cohesion. CA focuses on preparing software engineers to write more stable, durable, and flexible applications capable of distinguishing between details (e.g., what framework it uses) and the business logic requirements. A literature review shows that considerable effort has been devoted to cataloging and solving code smells related to code, often called code smells. However, the same does not apply to architecture smells – its software architecture counterpart. Similar research regarding other programming languages such as Java, PHP, or C# represents noteworthy works in the area, but they do not address Python applications directly. This work directs efforts towards redesigning and adapting existing Python programs to the CA principles by detecting the code smells that break the CA constraints through the developed CALint tool. Moreover, this approach proposes two extended refactoring techniques to solve these smells efficiently by grouping and comparing static code analysis and reuse them to enforce Clean Architecture’s Dependency Rule programmatically. To demonstrate the feasibility of the two refactoring techniques described in this work and the CALint tool, we applied them to three different case studies. The major findings of this work include two extended refactoring techniques and the development of a tool to verify non-conformities related to the Clean Architecture dependency rule. The results show common cases where the dependency rule was violated and highlighted by the CALint tool, which are fixed with the support of refactoring steps.
EKF based Direct Target Motion Analysis

Traditional target motion analysis (TMA) is normally carried out with a two-step method, where the first step is to search for the parameterized measurements (e.g., bearing, Doppler) of a target with signal processing algorithms, followed by a recursive or batched geographic tracking algorithm. In this paper, we propose a new direct TMA (DTMA) algorithm with a sensor array based on the extended Kalman filter (EKF) running within the Cartesian coordinate. In such a scenario, the sensor has to execute a maneuver to gain the observability with respect to target state. By exploiting the direction of arrival (DOA) effect of the received signal, the Jacobian matrix, which is essential for linearizing the measurement function with the EKF method, is achieved. A remarkable feature of the proposed algorithm is that it can directly estimate target state in the Cartesian coordinate from the single snapshot received signal. The effectiveness of proposed algorithm is assessed via simulation experiments.
PINGAN_AI at SemEval-2022 Task 9: Recipe knowledge enhanced model applied in Competence-based Multimodal Question Answering

This paper describes our system used in the SemEval-2022 Task 09: R2VQ -Competencebased Multimodal Question Answering.We propose a knowledge-enhanced model for predicting answer in QA task, this model use BERT as the backbone.We adopted two knowledge-enhanced methods in this model: the knowledge auxiliary text method and the knowledge embedding method.We also design an answer extraction task pipeline, which contains an extraction-based model, an automatic keyword labeling module, and an answer generation module.Our system ranked 3rd in task 9 and achieved an exact match score of 78.21 and a word-level F1 score of 82.62.
You Only Demanufacture Once (Yodo): WEEE Retrieval Using Unsupervised Learning

The presented research developed a generic retrieval system named You Only Demanufacture Once (YODO) based on content-based image retrieval (CBIR) to identify the product model and retrieve product model-specific demanufacturing instructions. The system compares the visual features represented on a color image of the WEEE with a database of known descriptions representing previously imaged WEEE. The performance of YODO is evaluated with a case study for laptop model identification, where a large dataset is created including 4089 images of a representative laptop waste stream. The results demonstrate a top-1 retrieval mean average precision (mAP) of 93.75%. After running YODO on 3600 laptops, theÂ results show an 85% chance that the next laptop presented to the system is already registered in the database. This corroborates that a fast learning rate can be achieved, allowing a YODO system to support the robotic demanufacturing by making prior product-specific learnings available.
Countering Personalized Speech

Social media platforms use personalization algorithms to make content curation decisions for each end user. These “personalized instances of content curation” (“PICCs”) are essentially speech conveying a platform’s predictions on content relevance for each end user. Yet, PICCs are causing some of the worst problems on the internet. First, they facilitate the precipitous spread of mis- and disinformation by exploiting the very same biases and insecurities that drive end user engagement with such content in the first place. Second, they exacerbate social media addiction and related mental health harms by leveraging users’ affective needs to drive engagement to greater and greater heights. Lastly, they help erode end user privacy and autonomy as both sources and incentives for data collection.As with any harmful speech, the solution is often counterspeech. Free speech jurisprudence considers counterspeech the most speech-protective weapon to combat false or harmful speech. Thus, to combat problematic PICCs, social media platforms, policymakers, and other stakeholders should embolden end users’ counterspeech capabilities in the digital public sphere. One way to implement this solution is through platform-provided end user personalization tools. The prevailing end user personalization inputs prevent users from providing effective countermeasures against problematic PICCs, since on most, if not all, major social media platforms, these inputs confer limited ex post control over PICCs. To rectify this deficiency and empower end users, I make several proposals along key regulatory modalities to move end user personalization towards more robust ex ante capabilities that also filter by content type and characteristics, rather than just ad hoc filters on specific pieces of content and content creators.
Increasing Teachers’ Trust in Automatic Text Assessment Through Named-Entity Recognition

In this study, we investigate teachers' trust in automatic text assessments and explore whether teachers' trust increases when we show them, in addition to the rating classification score, contextual didactic keywords (e.g., important didactic terms or persons, so-called entity) in the text through named-entity recognition (NER). The results of the present study make it clear that for the majority of the participating teachers (N = 34), trust in automatic text assessment increases as soon as they are shown important task-related keywords in the text. In order to increase teachers' acceptance and trust in the assessment result of an automatic text response, we recommend that when developing future correction-supporting assessment tools, the automatic extraction of important task-related didactic keywords from the text should be considered in addition to text score.
Neural Network-based Approach for Source Code Classification to Enhance Software Maintainability and Reusability

One of the most essential resources in software development is a program's source code. When a developer starts coding from scratch for each project, it takes more time and money to develop. When it comes to software reuse and maintainability, topic modelling is critical since it may be difficult for developers to remove outdated source code from huge systems with a lot of code. In a variety of ways, topic modelling approaches have been used to analyze and model source codes. Using various statistical techniques and methodologies, several pieces of research have been done to extract topics from source codes. Attempts to extract topics from method names, identifiers, and comments are the most common. These topic extraction approaches are interdependent, and if software best practices aren't followed in older systems, it might cause chaos. Motivated by these observations, in this paper, the authors have conducted a study on extracting source code using a JavaParser and predicting the source code functionality name through the artificial neural network model. It shows an average accuracy of 88 percent semantic function prediction rate. This is a new approach for topic modelling and the first attempt in building an artificially intelligent model to predict the semantic function name of the source code.
Monogamy constraints on entanglement of four-qubit pure states

We report a set of monogamy constraints on one-tangle, two-tangles, three-tangles and four-way correlations of a general four-qubit pure state. It is found that given a two-qubit marginal state $\rho$ of a four qubit pure state $\left\vert \Psi_{4}\right\rangle $, the non-Hermitian matrix $\rho\widetilde{\rho}$ where $\widetilde{\rho}$ $=\left( \sigma_{y} \otimes\sigma_{y}\right) \rho^{\ast}\left( \sigma_{y}\otimes\sigma_{y}\right) $, contains information not only about the entanglement properties of the two-qubits in state $\rho$ but also about three tangles involving the selected pair as well as four-way correlations of the pair of qubits in $\left\vert \Psi_{4}\right\rangle $. To extract information about tangles of a four-qubit state $\left\vert \Psi_{4}\right\rangle $, the coefficients in the characteristic polynomial of matrix $\rho\widetilde{\rho}$ are analytically expressed in terms of $2\times2$ matrices of state coefficients. Four-tangles distinguish between different types of entangled four-qubit pure states.
Agent Data Encryption and Storage Algorithm for Wireless Sensor Networks Based on Blockchain Technology for Future Smart City Networks

Abstract Aiming at the problems of long storage time and low encryption accuracy of the traditional agent data encryption storage algorithm in wireless sensor networks, an agent data encryption storage algorithm based on blockchain technology is proposed. Analyze the blockchain hierarchy and data, take the sensor node with the signal energy value exceeding the predetermined threshold as the source node for generating sensing data, and obtain the agent data transmission path of the wireless sensor network. Based on the obtained agent data transmission path in the data layer of data block, the agent data are encrypted by generating an encryption key and data encryption. According to the agent data encryption results, the agent data encryption storage model of the wireless sensor network is constructed. The simulation results show that the proposed algorithm has shorter storage time and higher encryption accuracy.
Network Infrastructure Design and Website Management of Perumdam Tirta Bengkayang

The management and optimization of IT infrastructure are vital for the Regional-Owned Enterprise Agency, Perumdam Titra Bengkayang, considering the number of connections on 2021 was 10.100 SR and the number continues to grow. This causes the need to improve services in the field of information technology, especially facilities and infrastructure for information disclosure of public bodies involving facilities such as network infrastructure and websites as well as online payment systems. Since the change of director and of name from PDAM to Perumdam Tirta Bengkayang, it is stated in the five year strategic plan to change the status of information disclosure from the black zone in 2019 at least to yellow for 2020-2022 and blue or green for 2023-2024. Through this activity, the design of a network system among the branches of the Perumdam Tirta Bengkayang office and website management to facilitate monitoring and evaluation of information disclosure for public agencies were done. This activity resulted in a network design based on inter-branch networks for future improvements and website management based on wordpress CMS.
Low-Code Platform for Health Protocols Implementation in Sabilussalam Mosque During The COVID-19 Pandemic

The COVID-19 pandemic condition has changed various aspects of life, especially Muslims’ prayer activities. COVID-19 pandemic has especially affected mosques as a place for Muslims to pray five times a day and to have Friday prayer congregation. To prevent the spread of COVID-19, the Sabilussalam Mosque located at Jl. Dr. Hatta Bandung has restricted its capacity and ensured visitors’ body temperature below 37,4 C manually. However, physical contact still occured, and its capacity still exceeded 50%. Therefore, the adoption of self-check temperature and automatic capacity counter as a method of mitigating the COVID-19 pandemic in mosques was needed. This community service aims at implementing a system for controlling mosque capacity and for avoiding physical contact during praying. The Low Code-based app counts temperature data broadcasted by K3 Pro and stored in NowDB cloud-service. As a result, the system manages to control the mosque's capacity to a maximum of 50% without any physical contact because it relies on internet connection.
A Hierarchical Pitman-Yor mixture of Scaled Dirichlet Distributions

In this paper, we propose a novel clustering method, hierarchical Pitman-Yor process mixture of scaled Dirichlet (SD) distributions, and apply it on a challenging medical application. The flexibility of SD distribution and its good potential to fit non-Gaussian data motivated us to construct our new model on it. Moreover, some challenges in healthcare domain such as high annotation costs of a medical data and sensitivity encouraged us to focus on this unsupervised approach. We learn our proposed model with batch and online variational inference which enable us to estimate model complexity and parameters at the same time. To measure the performance of our model, we evaluated it on human activity recognition and then compared it with other similar alternatives. The results indicate the robustness of our proposed model.
Analysis and Design of a Guided Sampling based Path Planning using CNNs

Intelligent sampling has a strong effect on path planning performance. Learning sampling distributions from the expert planning algorithm demonstrations could contribute to an optimized and improved planning performance. In this study, we offer a novel CNN-based network to predict suitable sampling distributions for a faster path planning process. We also propose several improvements and modifications to strengthen the link between intelligent sampling networks and path planning. Our proposed method is tested against the more commonly used random sampling approach in various conditions (i.e. three different sample sizes, two different path planners). The test results showed that the proposed method is remarkably more sample efficient when compared with conventional planning approaches on large sample sets. Additionally, this novel approach results in a more user-friendly and intuitive design, with much less computational parameters, hence, in a path planning approach that is more convenient for real-time implementation.
Autonomous Mobile Robot: Guidelines for a Simple Architecture

A simple architecture for an autonomous mobile robot is presented. Additionally, an allocation of competencies for its components is established. The architecture is flexible and can be used as a guideline for engineers working in the design of autonomous mobile robots. Following the architecture a control level has been fully developed for a real robot, and the experimental results showed that design decisions were taken quickly.