Reviewerly/exHarmony huggingface dataset:
IITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using Transformers

This paper presents our submission to task 5 ( Multimedia Automatic Misogyny Identification) of the SemEval 2022 competition.The purpose of the task is to identify given memes as misogynistic or not and further label the type of misogyny involved.In this paper, we present our approach based on language processing tools.We embed meme texts using GloVe embeddings and classify misogyny using BERT model.Our model obtains an F1score of 66.24% and 63.5% in misogyny classification and misogyny labels, respectively.
Multi-Task Learning to Capture Changes in Mood Over Time

This paper investigates the impact of using Multi-Task Learning (MTL) to predict mood changes over time for each individual (social media user).The presented models were developed as a part of the Computational Linguistics and Clinical Psychology (CLPsych) 2022 shared task.Given the limited number of Reddit social media users, as well as their posts, we decided to experiment with different multitask learning architectures to identify to what extent knowledge can be shared among similar tasks.Due to class imbalance at both post and user levels and to accommodate task alignment, we randomly sampled an equal number of instances from the respective classes and performed ensemble learning to reduce prediction variance.Faced with several constraints, we managed to produce competitive results that could provide insights into the use of multitask learning to identify mood changes over time and suicide ideation risk.
An End-to-End Dialogue Summarization System for Sales Calls

Summarizing sales calls is a routine task performed manually by salespeople.We present a production system which combines generative models fine-tuned for customer-agent setting, with a human-in-the-loop user experience for an interactive summary curation process.We address challenging aspects of dialogue summarization task in a real-world setting including long input dialogues, content validation, lack of labeled data and quality evaluation.We show how GPT-3 can be leveraged as an offline data labeler to handle training data scarcity and accommodate privacy constraints in an industrial setting.Experiments show significant improvements by our models in tackling the summarization and content validation tasks on public datasets.
An Encoder Attribution Analysis for Dense Passage Retriever in Open-Domain Question Answering

The bi-encoder design of dense passage retriever (DPR) is a key factor to its success in open-domain question answering (QA), yet it is unclear how DPR's question encoder and passage encoder individually contributes to overall performance, which we refer to as the encoder attribution problem.The problem is important as it helps us identify the factors that affect individual encoders to further improve overall performance.In this paper, we formulate our analysis under a probabilistic framework called encoder marginalization, where we quantify the contribution of a single encoder by marginalizing other variables.First, we find that the passage encoder contributes more than the question encoder to indomain retrieval accuracy.Second, we demonstrate how to find the affecting factors for each encoder, where we train DPR with different amounts of data and use encoder marginalization to analyze the results.We find that positive passage overlap and corpus coverage of training data have big impacts on the passage encoder, while the question encoder is mainly affected by training sample complexity under this setting.Based on this framework, we can devise data-efficient training regimes: for example, we manage to train a passage encoder on SQuAD using 60% less training data without loss of accuracy.
User-Centric Gender Rewriting

In this paper, we define the task of gender rewriting in contexts involving two users (I and/or You) -first and second grammatical persons with independent grammatical gender preferences.We focus on Arabic, a gendermarking morphologically rich language.We develop a multi-step system that combines the positive aspects of both rule-based and neural rewriting models.Our results successfully demonstrate the viability of this approach on a recently created corpus for Arabic gender rewriting, achieving 88.42 M 2 F 0.5 on a blind test set.Our proposed system improves over previous work on the first-person-only version of this task, by 3.05 absolute increase in M 2 F 0.5 .We demonstrate a use case of our gender rewriting system by using it to post-edit the output of a commercial MT system to provide personalized outputs based on the users' grammatical gender preferences.We make our code, data, and pretrained models publicly available. 1
YNU-HPCC at SemEval-2022 Task 2: Representing Multilingual Idiomaticity based on Contrastive Learning

This paper will present the methods 1 we use as the YNU-HPCC team in the SemEval-2022 Task 2, Multilingual Idiomaticity Detection and Sentence Embedding.We are involved in two subtasks, including four settings.In subtask B of sentence representation, we used novel approaches with ideas of contrastive learning to optimize model, where method of CoSENT was used in the pre-train setting, and triplet loss and multiple negatives ranking loss functions in fine-tune setting.We had achieved very competitive results on the final released test datasets.However, for subtask A of idiomaticity detection, we simply did a few explorations and experiments based on the xlm-RoBERTa model.Sentence concatenated with additional MWE as inputs did well in a one-shot setting.Sentences containing context had a poor performance on final released test data in zero-shot setting even if we attempted to extract effective information from CLS tokens of hidden layers.
NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model

This study describes the model design of the NCUEE-NLP system for the Chinese track of the SemEval-2022 MultiCoNER task.We use the BERT embedding for character representation and train the BiLSTM-CRF model to recognize complex named entities.A total of 21 teams participated in this track, with each team allowed a maximum of six submissions.Our best submission, with a macro-averaging F1-score of 0.7418, ranked the seventh position out of 21 teams.
UMUTeam at SemEval-2022 Task 5: Combining image and textual embeddings for multi-modal automatic misogyny identification

In this manuscript we describe the participation of the UMUTeam on the MAMI shared task proposed at SemEval 2022.This task is concerning the identification of misogynous content from a multi-modal perspective.Our participation is grounded on the combination of different feature sets within the same neural network.Specifically, we combine linguistic features with contextual transformers based on text (BERT) and images (BEiT).Besides, we also evaluate other ensemble learning strategies and the usage of non-contextual pretrained embeddings.Although our results are limited, we outperform all the baselines proposed, achieving position 36 in the binary classification task with a macro F1-score of 0.687, and position 28 in the multi-label task of misogynous categorisation, with an macro F1-score of 0.663.
Toward privacy-aware federated analytics of cohorts for smart mobility

Location-based Behavioral Analytics (LBA) holds a great potential for improving the services available in smart cities. Naively implemented, such an approach would track the movements of every citizen and share their location traces with the various smart service providers—similar to today's Web analytics systems that track visitors across the web sites they visit. This study presents a novel privacy-aware approach to location-based federated analytics that removes the need for individuals to share their location traces with a central server. The general approach is to model the behavior of cohorts instead of modeling specific users. Using a federated approach, location data is processed locally on user devices and only shared in anonymized fashion with a server. The server aggregates the data using Secure Multiparty Computation (SMPC) into service-defined cohorts, whose data is then used to provide cohort analytics (e.g., demographics) for the various smart service providers. The approach was evaluated on three real-life datasets with varying dropout rates, i.e., clients not being able to participate in the SMPC rounds. The results show that our approach can privately estimate various cohort demographics (e.g., percentages of male and female visitors) with an error between 0 and 8 percentage points relative to the actual cohort percentages. Furthermore, we experimented with predictive models for estimating these cohort percentages 1-week ahead. Across all three datasets, the best-performing predictive model achieved a Pearson's correlation coefficient above 0.8 (strong correlation), and a Mean Absolute Error (MAE) between 0 and 10 (0 is the minimum and 100 is the maximum). We conclude that privacy-aware LBA can be achieved using existing mobile technologies and federated analytics.
The Gap between the Admitted and the Measured Technical Debt: An Empirical Study

Technical debt is a well understood and used concept in IT development. The metaphor, rooted in the financial world, captures the amount of work that development teams owe to a product. Every time developers take a shortcut within development, the technical debt accumulates. Technical debt identification can be accomplished via manual reporting on the technical debt items, which is called self-admitted technical debt. Several specialised methods and tools have also emerged that promise to measure the technical debt. Based on experience in the community, the impression emerged that the measured technical debt is of a significantly different amount than the self-admitted debt. In this context, we decided to perform empirical research on the possible gap between the two. We investigated 14 production-grade software products while determining the amount of accumulated technical debt via (a) a self-admitting procedure and (b) measuring the debt. The outcomes show clearly the significant difference in the technical debt reported by the two methods. We urge development and quality-assurance teams not to rely on technical debt measurement alone. The tools demonstrated their strength in identifying low-level code technical debt items that violate a set of predefined rules. However, developers should have additional insight into violations, based on the interconnected source code and its relation to the domain and higher-level design decisions.
Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms

Purpose: Validation of artificial intelligence (AI) algorithms in digital pathology with a reference standard is necessary before widespread clinical use, but few examples focus on creating a reference standard based on pathologist annotations. This work assesses the results of a pilot study that collects density estimates of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer biopsy specimens. This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose. Approach: Collaborators and crowdsourced pathologists contributed glass slides, digital images, and annotations. Here, "annotations" refer to any marks, segmentations, measurements, or labels a pathologist adds to a report, image, region of interest (ROI), or biological feature. Pathologists estimated sTILs density in 640 ROIs from hematoxylin and eosin stained slides of 64 patients via two modalities: an optical light microscope and two digital image viewing platforms. Results: The pilot study generated 7373 sTILs density estimates from 29 pathologists. Analysis of annotations found the variability of density estimates per ROI increases with the mean; the root mean square differences were 4.46, 14.25, and 26.25 as the mean density ranged from 0% to 10%, 11% to 40%, and 41% to 100%, respectively. The pilot study informs three areas of improvement for future work: technical workflows, annotation platforms, and agreement analysis methods. Upgrades to the workflows and platforms will improve operability and increase annotation speed and consistency. Conclusions: Exploratory data analysis demonstrates the need to develop new statistical approaches for agreement. The pilot study dataset and analysis methods are publicly available to allow community feedback. The development and results of the validation dataset will be publicly available to serve as an instructive tool that can be replicated by developers and researchers.
Optimisation of Deep Learning Small-Object Detectors with Novel Explainable Verification

In this paper, we present a novel methodology based on machine learning for identifying the most appropriate from a set of available state-of-the-art object detectors for a given application. Our particular interest is to develop a road map for identifying verifiably optimal selections, especially for challenging applications such as detecting small objects in a mixed-size object dataset. State-of-the-art object detection systems often find the localisation of small-size objects challenging since most are usually trained on large-size objects. These contain abundant information as they occupy a large number of pixels relative to the total image size. This fact is normally exploited by the model during training and inference processes. To dissect and understand this process, our approach systematically examines detectors' performances using two very distinct deep convolutional networks. The first is the single-stage YOLO V3 and the second is the double-stage Faster R-CNN. Specifically, our proposed method explores and visually illustrates the impact of feature extraction layers, number of anchor boxes, data augmentation, etc., utilising ideas from the field of explainable Artificial Intelligence (XAI). Our results, for example, show that multi-head YOLO V3 detectors trained using augmented data produce better performance even with a fewer number of anchor boxes. Moreover, robustness regarding the detector's ability to explain how a specific decision was reached is investigated using different explanation techniques. Finally, two new visualisation techniques are proposed, WS-Grad and Concat-Grad, for identifying explanation cues of different detectors. These are applied to specific object detection tasks to illustrate their reliability and transparency with respect to the decision process. It is shown that the proposed techniques can result in high resolution and comprehensive heatmaps of the image areas, significantly affecting detector decisions as compared to the state-of-the-art techniques tested.
The Effect of Project Based Blended Learning Towards Negotiation Text Writing Ability Moderated by Learning Motivation

This study aims to examine the effect of Project-Based Blended Learning on the ability to write negotiating texts moderated by the role of learning motivation. This experimental study involved 60 students in different classes where 30 people are in the control group and 30 others are in the experimental group. The data in this study are obtained from the results of the pretest and posttest to assess the ability of negotiating texts. The collected data is analyzed using the Anova test. Before being analyzed, the researcher tested the instrument and tested the classical assumptions. The test results show that all instrument items are valid and reliable. All data are known to meet the classical assumption test. The results of hypothesis testing indicate that learning motivation is able to moderate the relationship between project-based blended learning and the ability to write negotiating texts. Teachers are advised to apply project-based blended learning with more intensity to be applied in learning. For further researchers, it is possible to integrate this learning model into learning activities to measure the improvement of other students' abilities.
Information System Design for Monitoring and Evaluation of Learning on Blended Learning

The Pandemic Covid-19 necessitates all activities to be network-based, including the collegiate learning process. Prior to the epidemic, instructors were mechanically or face-to-face evaluated for their learning. Monitoring and evaluation, on the other hand, are carried out online during the e-learning learning process. However, in the learning process through e-learning, monitoring and evaluation are carried out online. To facilitate the monitoring and evaluation of online learning by lecturers, it is necessary to build a web-based information system. The purpose of the research is the creation of an information system for monitoring and evaluating learning evaluation through blended learning. The research methodology used is a research and development research design using waterfall analysis with the stages of requirements analysis, system design, implementation, integration and testing, operation and maintenance. The object-oriented research target is the monitoring and evaluation assessment information system. The result of the research is the development of an application for monitoring and evaluating learning assessments there are: (1) Using the system, the monitoring and evaluation of learning carried out by the monitoring and evaluation team can be easily carried out during the evaluation process. (2) The system can display a description of the results of the evaluation of learning monitoring and evaluation, (3) The system developed is used at the college for monitoring and evaluating learning activities through blended learning.
Evaluation on the Impact of Cache Parameter Selection in Access-Driven Cache Attacks

Cache attacks exploit the hardware vulnerabilities inherent to modern processors and pose a new threat to Internet of Things (IoT) devices. Intuitively, different cache parameter configurations directly impact the attack effectiveness, but the current research on this issue is not systematic or comprehensive enough. This paper’s primary focus is to evaluate how different cache parameter configurations affect access-driven attacks. We build a flexible and configurable simulation verification environment based on the Chipyard framework. To reduce the interference of other factors, we established a baseline for each category of parameter evaluation. We propose a novel evaluation model, called Key Score Scissors Differential (KSSD), for evaluating common private and shared cache parameters under the local and cross-core attack models, respectively; among these are private cache replacement policy, private cache capacity, cache line size, private cache associativity, shared cache capacity, and shared cache associativity. Ours is the first evaluation of the shared cache under a cross-core attack model. As a result of the evaluation, the quantitative metrics can provide a reliable indication of information leakage level under the current cache configuration, which is helpful for attackers, defenders, and evaluators. Furthermore, we provide detailed explanations and discussions of inconsistent findings by comparing our results with the existing literature.
PENERAPAN METODE SDLC (SYSTEM DEVLOPMENT LIFE CYCLE) PADA WEBSITE PENJUALAN PRODUK VAPOR

Vapors or e-cigarettes are cigarettes with an evaporation process. One shop selling vapor products called the Independent Store sells various kinds of vapor products as well as liquids and other vapor equipment. This Independent Store store has problems in selling because it has not used an online-based sales system. Therefore, it is the desire of the researchers to be able to assist in the process of selling vapor products at the Independent Store store by creating a website. In the process of creating a website for selling vapor products, method is used SDLC (System Development Life Cycle) the waterfall model . With the hope of helping shop owners and employees in the sales process.
Fall Detection using Deep Learning Algorithms and Analysis of Wearable Sensor Data by Presenting a New Sampling Method

Fall is one of the most critical health challenges in the community, which can cause severe injuries and even death. The primary purpose of this study is to develop a deep neural network using wearable sensor data to detect falls. Most datasets in this field suffer from the problem of data imbalance so that the instances belonging to the Fall classes are significantly less than the data of the normal class. This study offers a dynamic sampling technique for increasing the balance rate between the samples belonging to fall and normal classes to improve the accuracy of the learning algorithms. The Sisfall dataset was used in which human activity is divided into three categories: normal activity (BKG), moments before the fall (Alert), and role on the ground (Fall). Three deep learning models, CNN, LSTM, and a hybrid model called Conv-LSTM, were implemented on this dataset, and their performance was evaluated. Accordingly, the Conv-LSTM hybrid model presents 96.23%, 98.59%, and 99.38% in the Sensitivity parameter for the BKG, Alert, and Fall classes, respectively. For the accuracy parameter, we have managed to reach 97.12%. In addition, by using noise smoothing and removal techniques, we can hit a 97.83% accuracy rate. The results indicate the proposed model's superiority compared to other similar studies.
Block Chain Technology in Digital Accounting

The manual system of business process has been restricted by the Nobel Covid-19, and people have begun to work digitally. Block chain technology cannot be ignored when it comes to digitalization. Cryptocurrency and digitalization have grown far more rapidly than any other industry, and this technology is at the heart of it. Especially in the accounting field, this technology could have a major impact. It is possible that block chain technology could transform accounting from a two-entry system to a three-entry system.. As a result of this accounting fundamental need, it becomes imperative to maintain and update financial records. The transactions are recorded in real time, with full accuracy and reliability, and once the transaction is completed, it cannot be changed.
Real-Time Big Data Analytics for Data Stream Challenges: An Overview

The conventional approach of evaluating massive data is inappropriate for real-time analysis; therefore, analysing big data in a data stream remains a critical issue for numerous applications. It is critical in real-time big data analytics to process data at the point where they are arriving at a quick reaction and good decision making, necessitating the development of a novel architecture that allows for real-time processing at high speed and low latency. Processing and anlayzing a data stream in real-time is critical for a variety of applications; however, handling a large amount of data from a variety of sources, such as sensor networks, web traffic, social media, video streams, and other sources, is a considerable difficulty. The main goal of this paper is to give an overview of the current architecture for real time big data analytics, real-time data stream processing methods available, including their system architectures Lambda, kappa, and delta large data stream processing.