Reviewerly/exHarmony huggingface dataset:
Externally validating the IoTDevID device identification methodology using the CIC IoT 2022 Dataset

In the era of rapid IoT device proliferation, recognizing, diagnosing, and securing these devices are crucial tasks. The IoTDevID method (IEEE Internet of Things 2022) proposes a machine learning approach for device identification using network packet features. In this article we present a validation study of the IoTDevID method by testing core components, namely its feature set and its aggregation algorithm, on a new dataset. The new dataset (CIC-IoT-2022) offers several advantages over earlier datasets, including a larger number of devices, multiple instances of the same device, both IP and non-IP device data, normal (benign) usage data, and diverse usage profiles, such as active and idle states. Using this independent dataset, we explore the validity of IoTDevID's core components, and also examine the impacts of the new data on model performance. Our results indicate that data diversity is important to model performance. For example, models trained with active usage data outperformed those trained with idle usage data, and multiple usage data similarly improved performance. Results for IoTDevID were strong with a 92.50 F1 score for 31 IP-only device classes, similar to our results on previous datasets. In all cases, the IoTDevID aggregation algorithm improved model performance. For non-IP devices we obtained a 78.80 F1 score for 40 device classes, though with much less data, confirming that data quantity is also important to model performance.
Semi-supervised multi-channel speaker diarization with cross-channel attention

Most neural speaker diarization systems rely on sufficient manual training data labels, which are hard to collect under real-world scenarios. This paper proposes a semi-supervised speaker diarization system to utilize large-scale multi-channel training data by generating pseudo-labels for unlabeled data. Furthermore, we introduce cross-channel attention into the Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding (NSD-MA-MSE) to learn channel contextual information of speaker embeddings better. Experimental results on the CHiME-7 Mixer6 dataset which only contains partial speakers' labels of the training set, show that our system achieved 57.01% relative DER reduction compared to the clustering-based model on the development set. We further conducted experiments on the CHiME-6 dataset to simulate the scenario of missing partial training set labels. When using 80% and 50% labeled training data, our system performs comparably to the results obtained using 100% labeled data for training.
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations

Large language models (LLMs) are trained to imitate humans to explain human decisions. However, do LLMs explain themselves? Can they help humans build mental models of how LLMs process different inputs? To answer these questions, we propose to evaluate $\textbf{counterfactual simulatability}$ of natural language explanations: whether an explanation can enable humans to precisely infer the model's outputs on diverse counterfactuals of the explained input. For example, if a model answers "yes" to the input question "Can eagles fly?" with the explanation "all birds can fly", then humans would infer from the explanation that it would also answer "yes" to the counterfactual input "Can penguins fly?". If the explanation is precise, then the model's answer should match humans' expectations. We implemented two metrics based on counterfactual simulatability: precision and generality. We generated diverse counterfactuals automatically using LLMs. We then used these metrics to evaluate state-of-the-art LLMs (e.g., GPT-4) on two tasks: multi-hop factual reasoning and reward modeling. We found that LLM's explanations have low precision and that precision does not correlate with plausibility. Therefore, naively optimizing human approvals (e.g., RLHF) may not be a sufficient solution.
Translating Latin with Artificial Intelligence

The major hindrance in the study of earlier scientific literature is the availability of Latin translations into modern languages. This is particular true for the works of Euler who authored about 850 manuscripts and wrote a thousand letters and received back almost two thousand more. The translation of many of these manuscripts, books and letters have been published in various sources over the last two centuries, but many more have not yet appeared. Fortunately, nowadays, the artificial intelligence AI translation can be used to circumvent the challenges of translating such substantial number of texts. To validate this tool, benchmark tests have been performed to compare the performance of two popular AI translating algorithms, namely Google Translate and ChatGPT. Since it was found that ChatGPT performed better on these tests, this translating support was then used on an excerpt of a 1739 letter from Johann Bernoulli to Euler, where he notifies that he was sending to Euler the first part of his manuscript Hydraulica. The findings highlight ChatGPT as a valuable translation tool, catering not only to general Latin practitioners but also proving beneficial for specialized Latin translators.
Prediction of breast cancer with 98% accuracy

Abstract Cancer is a tumor that affects people worldwide, with a higher incidence in females but not excluding males. It ranks among the top five deadliest types of cancer, particularly prevalent in less developed countries with deficient healthcare programs. Finding the best algorithm for effective breast cancer prediction with minimal error is crucial. In this scientific article, we employed the SMOTE method in conjunction with the R package Shiny to enhance the algorithms and improve prediction accuracy. We classified the tumor types as benign and malignant (B/M). Various algorithms were analyzed using a Kaggle dataset, and our study identified the superior algorithm as logistic regression. We evaluated algorithm performance using confusion matrices to visualize results and the ROC Curve to obtain a comprehensive measure of performance. Additionally, we calculated precision by dividing the number of correct predictions by the total predictions Keywords Breast cancer, Smote, Benign, Malignant.
QontSum: On Contrasting Salient Content for Query-focused Summarization

Query-focused summarization (QFS) is a challenging task in natural language processing that generates summaries to address specific queries. The broader field of Generative Information Retrieval (Gen-IR) aims to revolutionize information extraction from vast document corpora through generative approaches, encompassing Generative Document Retrieval (GDR) and Grounded Answer Retrieval (GAR). This paper highlights the role of QFS in Grounded Answer Generation (GAR), a key subdomain of Gen-IR that produces human-readable answers in direct correspondence with queries, grounded in relevant documents. In this study, we propose QontSum, a novel approach for QFS that leverages contrastive learning to help the model attend to the most relevant regions of the input document. We evaluate our approach on a couple of benchmark datasets for QFS and demonstrate that it either outperforms existing state-of-the-art or exhibits a comparable performance with considerably reduced computational cost through enhancements in the fine-tuning stage, rather than relying on large-scale pre-training experiments, which is the focus of current SOTA. Moreover, we conducted a human study and identified improvements in the relevance of generated summaries to the posed queries without compromising fluency. We further conduct an error analysis study to understand our model's limitations and propose avenues for future research.
Assessing and Exploiting Domain Name Misinformation

Cloud providers' support for network evasion techniques that misrepresent the server's domain name is more prevalent than previously believed, which has serious implications for security and privacy due to the reliance on domain names in common security architectures. Domain fronting is one such evasive technique used by privacy enhancing technologies and malware to hide the domains they visit, and it uses shared hosting and HTTPS to present a benign domain to observers while signaling the target domain in the encrypted HTTP request. In this paper, we construct an ontology of domain name misinformation and detail a novel measurement methodology to identify support among cloud infrastructure providers. Despite several of the largest cloud providers having publicly stated that they no longer support domain fronting, our findings demonstrate a more complex environment with many exceptions. We also present a novel and straightforward attack that allows an adversary to man-in-the-middle all the victim's encrypted traffic bound to a content delivery network that supports domain fronting, breaking the authenticity, confidentiality, and integrity guarantees expected by the victim when using HTTPS. By using dynamic linker hijacking to rewrite the HTTP Host field, our attack does not generate any artifacts that are visible to the victim or passive network monitoring solutions, and the attacker does not need a separate channel to exfiltrate data or perform command-and-control, which can be achieved by rewriting HTTP headers.
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning

Due to the broad range of applications of multi-agent reinforcement learning (MARL), understanding the effects of adversarial attacks against MARL model is essential for the safe applications of this model. Motivated by this, we investigate the impact of adversarial attacks on MARL. In the considered setup, there is an exogenous attacker who is able to modify the rewards before the agents receive them or manipulate the actions before the environment receives them. The attacker aims to guide each agent into a target policy or maximize the cumulative rewards under some specific reward function chosen by the attacker, while minimizing the amount of manipulation on feedback and action. We first show the limitations of the action poisoning only attacks and the reward poisoning only attacks. We then introduce a mixed attack strategy with both the action poisoning and the reward poisoning. We show that the mixed attack strategy can efficiently attack MARL agents even if the attacker has no prior information about the underlying environment and the agents' algorithms.
Learning from Pseudo-labeled Segmentation for Multi-Class Object Counting

Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing, based on only a few annotated exemplars. In this paper, we point out that the task of counting objects of interest when there are multiple object classes in the image (namely, multi-class object counting) is particularly challenging for current object counting models. They often greedily count every object regardless of the exemplars. To address this issue, we propose localizing the area containing the objects of interest via an exemplar-based segmentation model before counting them. The key challenge here is the lack of segmentation supervision to train this model. To this end, we propose a method to obtain pseudo segmentation masks using only box exemplars and dot annotations. We show that the segmentation model trained on these pseudo-labeled masks can effectively localize objects of interest for an arbitrary multi-class image based on the exemplars. To evaluate the performance of different methods on multi-class counting, we introduce two new benchmarks, a synthetic multi-class dataset and a new test set of real images in which objects from multiple classes are present. Our proposed method shows a significant advantage over the previous CAC methods on these two benchmarks.
Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition

Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets. Existing literature mainly focuses on label completion and co-occurrence learning while facing difficulties with the most common single-positive label manner. To tackle this problem, we present a semantic contrastive bootstrapping (Scob) approach to gradually recover the cross-object relationships by introducing class activation as semantic guidance. With this learning guidance, we then propose a recurrent semantic masked transformer to extract iconic object-level representations and delve into the contrastive learning problems on multi-label classification tasks. We further propose a bootstrapping framework in an Expectation-Maximization fashion that iteratively optimizes the network parameters and refines semantic guidance to alleviate possible disturbance caused by wrong semantic guidance. Extensive experimental results demonstrate that the proposed joint learning framework surpasses the state-of-the-art models by a large margin on four public multi-label image recognition benchmarks. Codes can be found at https://github.com/iCVTEAM/Scob.
In-Context Learning for Data Generation in Aspect-Based Opinion Extraction

Abstract In-Context Learning (ICL), a strategy that generates text by passing a list of input-output examples to a Large Language Model, has shown state-of-the-art performance (SOTA) for many Natural Language Processing tasks. In this work, we aim to apply ICL for the synthetic generation of opinionated triples (sentence, aspect, and opinion) to enhance the training of models in two Aspect-Based Sentiment Analysis (ABSA) tasks: Target-oriented Opinion Words Extraction (TOWE) and Aspect-Based Opinion Pair Extraction (AOPE). Our approach first adapts a pre-trained Language Model (LM) to a downstream task using Task-adaptive Pretraining (TAPT). Next, it uses ICL prompting approaches on the adapted LM to generate the opinionated triples. Finally, a post-processing step applies some heuristics to clean and format the triples. We evaluate the quality of these generated samples by augmenting existing benchmark datasets with them and comparing the performance of four SOTA models on these tasks. The results show that these models trained on the generated synthetic data outperform those trained on a small percentage of human-labelled data. We manually inspected these triples with three human raters, which validated their quality.
Cognitive and Social Strategies to Deal With Anxiety in Speaking: Typical Strategies and Application

This study aimed to reveal students’ strategies to deal with anxiety in performing speaking at Nurul Hakim Islamic Boarding School. The study was qualitative with case study design. The subjects of this study were eleventh grade students which were chosen through purposive sampling technique. The techniques of data collection were semi-structured interview and documentation. The data were analyzed through data reduction, data display and data verification and conclusion. Methodological triangulation technique was used to provide valid data. The result of this study showed there were five strategies used by eleventh grade students at Nurul Hakim Islamic Boarding school to deal with anxiety: Rehearsal, Preparation, Cooperation, Self-talk and Relaxation. From the five strategies, Rehearsal is the dominant strategies used by students to deal with speaking anxiety. Except Preparation and Relaxation, the other strategies were claimed effective to reduce anxiety. The five strategies were classified into two classifications: Cognitive strategy and Social strategy. Cognitive strategy involves Rehearsal and Preparation, while Social strategy involves Cooperation, Self-talk, and Relaxation. In this study, there were no strategies that represent the Metacognitive strategy.
Students’ Learning Anxiety and Speaking Skill in Lower Secondary School: A Correlation Study

Speaking skill is an ability of oral communication owned by the students and requiring self-confidence in front of the people. Besides, speaking in English language is challenging for the students. It will arise their nerves and become anxiety. This research was aimed to figure out the correlation between students’ learning anxiety and speaking skills at lower secondary school. It used quantitative method with correlation design. The data were obtained using questionnaire (FLCAS) for students’ learning anxiety and an English test for speaking skills. The questionnaire was adapted from Horwitz et al. (1986) to discover the students’ learning anxiety. The data was analyzed using SPSS Program in version 25. The population was all the students in the second grade with 102 students, and there were 41 students chosen by simple random sampling as the respondents. The result showed that Hi was accepted and H0 was rejected (0.046 &lt; 0.05). Thus, there is a correlation between students’ learning anxiety and speaking skills
Utilizing Concept Maps to Enhance Students’ Writing Skills

Writing is one of the difficult skills in a language. The difficulty of writing includes spelling, punctuation, word choices, and so on. However, many students already studied writing since middle school. As a result, many of them have not yet been able to write correctly. The aim of this paper is to find out an increase in descriptive writing skills by utilizing a concept map. This study used descriptive and quantitative research on car design. The sample was 20 students using purposive sampling. The instrument was an objective test. The procedure for collecting data used to test distribution and analysis data. The finding showed that students had excellent to very good achievement in writing skills through concept maps. It was indicated by the results of the mean score in the post-test. it indicates that the student’s writing ability was in good to Very Good criteria. It means that writing a descriptive text with a concept map was better than writing descriptive text without a concept map. This research found the pedagogical implication that concept maps help teachers in providing active learning, creative, effective, and fun for students according to learning style. Newly, it can increase awareness of teachers on students’ learning difficulties and efforts to overcome their obstacles in writing.
PiTL: Cross-modal Retrieval with Weakly-supervised Vision-language Pre-training via Prompting

Vision-language (VL) Pre-training (VLP) has shown to well generalize VL models over a wide range of VL downstream tasks, especially for cross-modal retrieval. However, it hinges on a huge amount of image-text pairs, which requires tedious and costly curation. On the contrary,weakly-supervised VLP (W-VLP) explores means with object tags generated by a pre-trained object detector (OD) from images. Yet, they still require paired information, i.e. images and object-level annotations, as supervision to train an OD.
PENGEMBANGAN MEDIA KOTAK PINTER AKSARA JAWA (PIRAWA) PADA MATA PELAJARAN BAHASA JAWA KELAS IV SD NEGERI GONDANG 03 KABUPATEN BATANG

The problem was found that the learning media was limited, the learning method was only lectures and assignments, the teacher had not used creative learning media which resulted in students not being enthusiastic and students' learning motivation low. The purposes of this research are 1) Knowing the procedure for developing Pirawa Box media, 2) Knowing the feasibility level of Pirawa Box media, 3) Knowing the practicality of Pirawa Box media. This study uses the Research and Development (RnD) method. Data collection techniques using interviews and questionnaires. Data were analyzed using qualitative descriptive and quantitative descriptive. The results showed that the Kotak Pirawa media was declared feasible with the analysis results from the media expert validation of 100% and the results of the material expert validation analysis of 95.7% very good category. The Pirawa Box media was stated to be practical with a percentage of 100% in the very good category. Through the results of the student response questionnaire analysis obtained a percentage of 99%. Based on the results of this study, the Pirawa Box media developed was declared feasible and practical to use in the learning process.
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE THINK PAIR AND SHARE UNTUK MENINGKATKAN HASIL BELAJAR IPS PADA SISWA KELAS V SDN CENTRE MANGALLI KECAMATAN PALANGGA KABUPATEN GOWA

This study aims to describe the application of the think pair and share cooperative learning model in improving social studies learning outcomes for fifth grade students at SDN Center Mangalli. The approach used is a qualitative approach. This type of research is classroom action research (PTK) which is carried out in two cycles and each cycle is held in two meetings. Each cycle goes through 4 stages, namely the planning stage, the implementation stage, the observation stage, and the reflection stage. The focus of this research is the focus of the process and focus of learning and documentation. The subjects of this study were 25 class V teachers and fifth grade students consisting of 17 male students and 8 female students. In this study, the researcher acted as the executor of the action and the class teacher as the observer. Data analysis techniques are qualitative and quantitative. The results showed that the application of the cooperative learning model type think pair and share in cycle I was 56% (enough) of students who passed with an average student score of 67.6. Then in cycle II there was an increase to 92% (very good) of students who passed with an average student score of 86.4. Based on the analysis of social studies learning outcomes, it can be concluded that there is an increase in social studies learning outcomes of students by using the think pair and share cooperative learning model.
Intercultural Communication Interaction of Multicultural Society in West Kalimantan Province: Ethnographic Studies

Indonesia has a society with various cultures, due to which there are many ethnic groups with their respective cultural structures and living side by side, which is called multicultural. West Kalimantan Province is one of the regions in Indonesia where multicultural societies live side by side, known as TIDAYU, namely the Tionghua or Chinese, Dayaknese and Melayu or Malays, which are the three largest tribes in West Kalimantan and divided in various places in West Kalimantan. In fact, one of the cities in West Kalimantan, namely Singkawang City was rewarded as the number one most tolerant city in 2018 based on the assessment of the SETARA Institute in collaboration with the Indonesian Ministry of Home Affairs and the Pancasila Ideology Development Agency, and was again ranked number one in 2021. Based on the research results using ethnology-based qualitative method, people in West Kalimantan have been accustomed to cultural diversity from an early age; therefore, the life of the multicultural society in West Kalimantan has a high level of tolerance and intercultural communication in West Kalimantan runs in harmony and respects each other.&#x0D; Keywords: intercultural communication, interaction, multicultural society, tolerance