Reviewerly/exHarmony huggingface dataset:
Automated Detection of Breast Cancer Histopathology Image Using Convolutional Neural Network and Transfer Learning

cancer caused 2.3 million cases and 685,000 deaths in 2020. Histopathology analysis is one of the tests used to determine a patient’s prognosis. However, histopathology analysis is a time-consuming and stressful process. With advances in deep learning methods, computer vision science can be used to detect cancer in medical images, which is expected to improve the accuracy of prognosis. This study aimed to apply Convolutional Neural Network (CNN) and Transfer Learning methods to classify breast cancer histopathology images to diagnose breast tumors. This method used CNN, Transfer Learning ((Visual Geometry Group (VGG16), and Residual Network (ResNet50)). These models undergo data augmentation and balancing techniques applied to undersampling techniques. The dataset used for this study was ”The BreakHis Database of microscopic biopsy images of breast tumors (benign and malignant),” with 1693 data classified into two categories: Benign and Malignant. The results of this study were based on recall, precision, and accuracy values. CNN accuracy was 94%, VGG16 accuracy was 88%, and ResNet50 accuracy was 72%. The conclusion was that the CNN method is recommended in detecting breast cancer to diagnose breast cancer.
Comparative Study of Teaching Effectiveness &amp; Performance of Trained and Untrained Teachers at School Level Working in Private and Government Sector in District Kabul –Afghanistan

The study compared the professional skills of trained and untrained teachers towards effective teaching in private &amp;Gov. sector schools in District Kabul. The convenient sampling technique was employed in this descriptive cross-sectional study. A total of 9 private schools and 9 government schools were selected from Kabul, from each school 5 trained and 5 untrained teachers were selected for the study to whom the research instrument was administered. The sample consisted of a total of 180 school teachers. Five-point liker scale questionnaire was developed, including four important factors regarding teaching effectiveness, i.e., preparing lesson plan, introduction of instructional material, task orientation and ideas and contribution of students. The study found that for these above-mentioned factors, there was found in some way the differences between the effectiveness of trained and untrained.
GTR: An SQL Generator With Transition Representation in Cross-Domain Database Systems

Recent studies have focused on using natural language (NL) to automatically retrieve useful data from database (DB) systems. As an important component of autonomous DB systems, the NL-to-SQL technique can assist DB administrators in writing high-quality SQL statements and make persons with no SQL background knowledge learn complex SQL languages. However, existing studies cannot deal with the issue that the expression of NL inevitably mismatches the implementation details of SQLs, and the large number of out-of-domain (OOD) words makes it difficult to predict table columns. In particular, it is difficult to accurately convert NL into SQL in an end-to-end fashion. Intuitively, it facilitates the model to understand the relations if a “bridge” transition representation (TR) is employed to make it compatible with both NL and SQL in the phase of conversion. In this article, we propose an automatic SQL generator with TR called GTR in cross-domain DB systems. Specifically, GTR contains three SQL generation steps: 1) GTR learns the relation between questions and DB schemas; 2) GTR uses a grammar-based model to synthesize a TR; and 3) GTR predicts SQL from TR based on the rules. We conduct extensive experiments on two commonly used datasets, that is, WikiSQL and Spider. On the testing set of the Spider and WikiSQL datasets, the results show that GTR achieves 58.32% and 71.29% exact matching accuracy which outperforms the state-of-the-art methods, respectively.
Students’ Problems In Pronouncing -Ed Endings In Reading Aloud At Universitas Pgri Sumatera Barat

This research is aimed at analyzing students’ problems in pronouncing -ed endings in reading aloud at Universitas PGRI Sumatera Barat registered in 2022/2023 academic year. This research was conducted since there were lots of students got difficulties in pronouncing -ed endings in the classroom activities. In addition, the research method used by the reseracher was qualitative descriptive. However, the instruments used were reading aloud test, interview, and audio recording. The researcher took 42 students as the sample to be tested. After conducting the test, the researcher got 30 students who got a low score, and the were interviewed as the sample of this research. The findings of this research showed that most problems occurred were caused by the difference between the way to pronounce the words of their mother tongue and English, locality (environment), personality, motivation, and lack of knowledge.
Understanding representations of uncertainty, an eye-tracking study – Part 2: The effect of expertise

Abstract. As the ability to make predictions regarding uncertainty information representing natural hazards increases, an important question for those designing and communicating hazard forecasts is how visualizations of uncertainty influence understanding amongst the intended, potentially varied, target audiences. End-users have a wide range of differing expertise and backgrounds, possibly influencing the decision-making process they undertake for a given forecast presentation. Our previous, Part 1 study (Mulder et al., 2023) examined how the presentation of uncertainty information influenced end-user decision making. Here, we shift the focus to examine the decisions and reactions of participants with differing areas of expertise (meteorology, psychology, and graphic-communication students) when presented with varied hypothetical forecast representations (boxplot, fan plot, or spaghetti plot with and without median lines) using the same eye-tracking methods and experiments. Participants made decisions about a fictional scenario involving the choices between ships of different sizes in the face of varying ice thickness forecasts. Eye movements to the graph area and key and how they changed over time (early, intermediate, and later viewing periods) were examined. More fixations (maintained gaze on one location) and more fixation time were spent on the graph and key during early and intermediate periods of viewing, particularly for boxplots and fan plots. The inclusion of median lines led to less fixations being made on all graph types during early and intermediate viewing periods. No difference in eye movement behaviour was found due to expertise; however, those with greater expertise were more accurate in their decisions, particularly during more difficult scenarios. Where scientific producers seek to draw users to the central estimate, an anchoring line can significantly reduce cognitive load, leading both experts and non-experts to make more rational decisions. When asking users to consider extreme scenarios or uncertainty, different prior expertise can lead to significantly different cognitive loads for processing information, with an impact on one's ability to make appropriate decisions.
From Stock Shots to Ghost Data: Tracking Audiovisual Archives about the European Union

This paper deals with a major challenge linked to the collection of audiovisual documents within television and web archives. Looking for repeated sequences within a corpus of thousands of videos, we faced the fact that the footage we were looking for reveals itself to be reachable only as ghost data. In fact, any audiovisual sequence reused within different contexts exists conceptually as the repetition of one single visual unit, but from the point of view of the metadata tagging its occurrences, each item is a distinct document. Like a ghost, the shot is there, scattered among different places, but the metadata cannot point us to the visual form repeated, despite its evidence to the human viewer. When facing large amounts of data, to relate a visual unit to its occurrences, data analysis techniques are needed. We describe our procedures of collection and annotation, and the solutions combining qualitive work and a computer-aided approach to face this main challenge, within the research project Crossing Borders Archives (CROBORA).
Web-Based Altamart Store Sales System Design

Altamart is a grocery store that serves buying and selling transactions for goods for daily needs. Altamart also provides various types of food and drinks such as snacks, bread, instant noodles, syrup, mineral water, and others. Altamart still uses manual tools for transactions in stores. Therefore, to develop technology, a Web-Based Application was designed to improve performance at the Altamart store. Simply using this application consumers can transact remotely, consumers can also find out the total price of goods to be purchased, consumers know the details of goods easily and billing records can be accessed via the internet and also consumers can choose various payment systems offered. In addition, consumers can also choose a home delivery service. So consumers can wait for the goods to be delivered to their destination without having to come to the store. However, it can only deliver within the surrounding area. From this research a WEB-Based Application was created for buying and selling transactions that can make it easier for buyers, also makes it easier for users (Grocery Stores), so that it can help users record incoming and outgoing goods or if you make points, the results obtained are based on trial results are as follows: (1) WEB-Based Application for ordering goods has been successfully created; (2) With the Goods Ordering Application, consumers can find out information on the stock of goods and prices of goods, for users it can make it easier to manage stock of goods; (3) Providing accurate and up-to-date stock information; (4) This application is for ordering goods in the store
Energy-Aware Federated Learning With Distributed User Sampling and Multichannel ALOHA

Distributed learning on edge devices has attracted increased attention with the advent of federated learning (FL). Notably, edge devices often have limited battery and heterogeneous energy availability, while multiple rounds are required in FL for convergence, intensifying the need for energy efficiency. Energy depletion may hinder the training process and the efficient utilization of the trained model. To solve these problems, this letter considers the integration of energy harvesting (EH) devices into a FL network with multi-channel ALOHA, while proposing a method to ensure both low energy outage probability and successful execution of future tasks. Numerical results demonstrate the effectiveness of this method, particularly in critical setups where the average energy income fails to cover the iteration cost. The method outperforms a norm based solution in terms of convergence time and battery level.
3D salient object detection based on light field integral imaging

Potent usage of the multi-scale light field information for salient object detection (SOD) is the essential requirement of three-dimensional (3D) SOD. On this basis, a light field 3D-SOD scheme is proposed that employs the pixel mapping algorithm to achieve a more distinct representation of spatial and angular information in the four-dimensional (4D) light field, collaboratively mining the global saliency cues via the co-salient object detection (CoSOD) network. Compared with the previous method, our scheme filters out most of the noise by thoroughly leveraging the global dependence of the 4D light field, offering significant enhancements in saliency extraction performance and efficiency. Additionally, the 3D reconstruction results demonstrate the integral retention of the spatial and angular information of the original light field.
Online Incremental Learning Based on Crowdsourcing For Indonesian Ontology Relation Extraction

Ontology is one form of structured representation of knowledge. Ontology is widely used and developed in information retrieval because of its ability to represent knowledge in a form that machines and humans can understand. With the increasing scale and complexity of ontology, there are more significant challenges in identifying extra-logical errors. Ontological development methods mostly use machine learning, which is at risk of missed extra-logical errors. To handle it, crowdsourcing is used, i.e. dividing a large job into several small jobs and hiring the masses to complete it. Data processing is usually done offline to take advantage of crowdsourcing, and batches are converted into online and incremental. Online incremental learning directly arranges an iterative model after a change is made by ensuring that the knowledge that has been obtained before is maintained. This study built an interactive medium to present the initial relationship between concept pairs. Crowdsourcing participants were asked to validate the relationship repeatedly until a specified accuracy value was reached. This study found that the crowdsourcing process was able to improve the model used in the relationship extraction process, from F1-Score 87.2% to 89.8%. Improvements using crowdsourcing achieve the same result as improvements by experts. Thus, crowdsourcing can correct extra-logical errors appropriately as an expert. In addition, it was also found that offline incremental learning using Random Forest resulted in higher model accuracy than incremental online learning using Mondrian Forest. The accuracy of the Random Forest model has a final accuracy of 90.6%, while the accuracy of the Mondrian Forest model is 89.7%. From these results, it was concluded that incremental online learning cannot provide better results than offline incremental learning to improve the meronymy relationship extraction process.
GPU Accelerated Full Homomorphic Encryption Cryptosystem, Library and Applications for IoT Systems

Deep learning such as convolutional neural networks (CNNs) have been utilized in a number of cloud-based Internet of Things (IoT) applications. Security and privacy are two key considerations in any commercial deployments. Fully homomorphic encryption (FHE) is a popular privacy protection approach, and there have been attempts to integrate FHE with CNNs. However, a simple integration may lead to inefficiency in single-user services and fail to support many of the requirements in real-time applications. In this paper, we propose a novel confused modulo projection based FHE algorithm (CMP-FHE) that is designed to support floating-point operations. Then we developed a parallelized runtime library based on CMP-FHE and compared it with the widely employed FHE library. Our results show that our library achieves a faster speeds. Furthermore, we compared it with the state-of-the-art confused modulo projection based library and the results demonstrated a speed improvement of 841.67 to 3056.25 times faster. Additionally, we construct a Real-Time Homomorphic Convolutional Neural Network (RT-HCNN) under the ciphertext-based framework using CMP-FHE, as well as using graphics processing units (GPUs) to facilitate acceleration. To demonstrate utility, we evaluate the proposed approach on the MNIST dataset. Findings demonstrate that our proposed approach achieves a high accuracy rate of 99.13%. Using GPUs acceleration for ciphertext prediction results in us achieving a single prediction time of 79.5 ms. This represents the first homomorphic CNN capable of supporting real-time application and is approximately 58 times faster than Microsoft’s Lola scheme.
Improving Signal-to-Noise Ratio (SNR) for Quantum Readout Signals Using Adaptive Hardware Filters on FPGA

This research presents an innovative approach to improving the Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals, utilizing FPGA-based adaptive filtering on the control board. Our solution aims to bolster quantum computations' reliability by providing a more accurate and precise way to obtain our readout traces and achieve a higher discernability between excitation states in real-time.
Idioms, Probing and Dangerous Things: Towards Structural Probing for Idiomaticity in Vector Space

The goal of this paper is to learn more about how idiomatic information is structurally encoded in embeddings, using a structural probing method. We repurpose an existing English verbal multi-word expression (MWE) dataset to suit the probing framework and perform a comparative probing study of static (GloVe) and contextual (BERT) embeddings. Our experiments indicate that both encode some idiomatic information to varying degrees, but yield conflicting evidence as to whether idiomaticity is encoded in the vector norm, leaving this an open question. We also identify some limitations of the used dataset and highlight important directions for future work in improving its suitability for a probing analysis.
Direct Speech Quote Attribution for Dutch Literature

We present a dataset and system for quote attribution in Dutch literature.The system is implemented as a neural module in an existing NLP pipeline for Dutch literature (dutchcoref;van Cranenburgh, 2019).Our contributions are as follows.First, we provide guidelines for Dutch quote attribution and annotate 3,056 quotes in fragments of 42 Dutch literary novels, both contemporary and classic.Second, we present three neural quote attribution classifiers, optimizing for precision, recall, and F1.Third, we perform an evaluation and analysis of quote attribution performance, showing that in particular, quotes with an implicit speaker are challenging, and that such quotes are prevalent in contemporary fiction (57%, compared to 32% for classic novels).On the task of quote attribution, we achieve an improvement over the rule-based baseline of 8.0% F1 points on contemporary fiction and 1.9% F1 points on classic novels.Code, models, and annotations for the public domain novels are available under an
Bridging the Gap between Native Text and Translated Text through Adversarial Learning: A Case Study on Cross-Lingual Event Extraction

Recent research in cross-lingual learning has found that combining large-scale pretrained multilingual language models with machine translation can yield good performance. We explore this idea for cross-lingual event extraction with a new model architecture that jointly encodes a source language input sentence with its translation to the target language during training, and takes a target language sentence with its translation back to the source language as input during evaluation. However, we observe significant representational gap between the native source language texts during training and the texts translated into source language during evaluation, as well as the texts translated into target language during training and the native target language texts during evaluation. This representational gap undermines the effectiveness of cross-lingual transfer learning for event extraction with machine-translated data. In order to mitigate this problem, we propose an adversarial training framework that encourages the language model to produce more similar representations for the translated text and the native text. To be specific, we train the language model such that its hidden representations are able to fool a jointly trained discriminator that distinguishes translated texts’ representations from native texts’ representations. We conduct experiments on cross-lingual for event extraction across three languages. Results demonstrate that our proposed adversarial training can effectively incorporate machine translation to improve event extraction, while simply adding machine-translated data yields unstable performance due to the representational gap.
External Knowledge Acquisition for End-to-End Document-Oriented Dialog Systems

End-to-end neural models for conversational AI often assume that a response can be generated by considering only the knowledge acquired by the model during training. Document-oriented conversational models make a similar assumption by conditioning the input on the document and assuming that any other knowledge is captured in the model’s weights. However, a conversation may refer to external knowledge sources. In this work, we present EKo-Doc, an architecture for document-oriented conversations with access to external knowledge: we assume that a conversation is centered around a topic document and that external knowledge is needed to produce responses. EKo-Doc includes a dense passage retriever, a re-ranker, and a response generation model. We train the model end-to-end by using silver labels for the retrieval and re-ranking components that we automatically acquire from the attention signals of the response generation model. We demonstrate with automatic and human evaluations that incorporating external knowledge improves response generation in document-oriented conversations. Our architecture achieves new state-of-the-art results on the Wizard of Wikipedia dataset, outperforming a competitive baseline by 10.3% in Recall@1 and 7.4% in ROUGE-L.
Incorporating Task-Specific Concept Knowledge into Script Learning

In this paper, we present Tetris, a new task of Goal-Oriented Script Completion. Unlike previous work, it considers a more realistic and general setting, where the input includes not only the goal but also additional user context, including preferences and history. To address this problem, we propose a novel approach, which uses two techniques to improve performance: (1) concept prompting, and (2) script-oriented contrastive learning that addresses step repetition and hallucination problems. On our WikiHow-based dataset, we find that both methods improve performance.
Shorten the Long Tail for Rare Entity and Event Extraction

The distribution of knowledge elements such as entity types and event types is long-tailed in natural language. Hence information extraction datasets naturally conform long-tailed distribution. Although imbalanced datasets can teach the model about the useful real-world bias, deep learning models may learn features not generalizable to rare or unseen expressions of entities or events during evaluation, especially for rare types without sufficient training instances. Existing approaches for the long-tailed learning problem seek to manipulate the training data by re-balancing, augmentation or introducing extra prior knowledge. In comparison, we propose to handle the generalization challenge by making the evaluation instances closer to the frequent training cases. We design a new transformation module that transforms infrequent candidate mention representation during evaluation with the average mention representation in the training dataset. Experimental results on classic benchmarks on three entity or event extraction datasets demonstrates the effectiveness of our framework.
Poor Man’s Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference

Vilém Zouhar, Shehzaad Dhuliawala, Wangchunshu Zhou, Nico Daheim, Tom Kocmi, Yuchen Eleanor Jiang, Mrinmaya Sachan. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
The Functional Relevance of Probed Information: A Case Study

Recent studies have shown that transformer models like BERT rely on number information encoded in their representations of sentences’ subjects and head verbs when performing subject-verb agreement. However, probing experiments suggest that subject number is also encoded in the representations of all words in such sentences. In this paper, we use causal interventions to show that BERT only uses the subject plurality information encoded in its representations of the subject and words that agree with it in number. We also demonstrate that current probing metrics are unable to determine which words’ representations contain functionally relevant information. This both provides a revised view of subject-verb agreement in language models, and suggests potential pitfalls for current probe usage and evaluation.