Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection

The effective detection of evidence of financial anomalies requires collaboration among multiple entities who own a diverse set of data, such as a payment network system (PNS) and its partner banks. Trust among these financial institutions is limited by regulation and competition. Federated learning (FL) enables entities to collaboratively train a model when data is either vertically or horizontally partitioned across the entities. However, in real-world financial anomaly detection scenarios, the data is partitioned both vertically and horizontally and hence it is not possible to use existing FL approaches in a plug-and-play manner. Our novel solution, PV4FAD, combines fully homomorphic encryption (HE), secure multi-party computation (SMPC), differential privacy (DP), and randomization techniques to balance privacy and accuracy during training and to prevent inference threats at model deployment time. Our solution provides input privacy through HE and SMPC, and output privacy against inference time attacks through DP. Specifically, we show that, in the honest-but-curious threat model, banks do not learn any sensitive features about PNS transactions, and the PNS does not learn any information about the banks' dataset but only learns prediction labels. We also develop and analyze a DP mechanism to protect output privacy during inference. Our solution generates high-utility models by significantly reducing the per-bank noise level while satisfying distributed DP. To ensure high accuracy, our approach produces an ensemble model, in particular, a random forest. This enables us to take advantage of the well-known properties of ensembles to reduce variance and increase accuracy. Our solution won second prize in the first phase of the U.S. Privacy Enhancing Technologies (PETs) Prize Challenge.
RGB-X Object Detection via Scene-Specific Fusion Modules

Multimodal deep sensor fusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions. However, existing deep sensor fusion methods usually employ convoluted architectures with intermingled multimodal features, requiring large coregistered multimodal datasets for training. In this work, we present an efficient and modular RGB-X fusion network that can leverage and fuse pretrained single-modal models via scene-specific fusion modules, thereby enabling joint input-adaptive network architectures to be created using small, coregistered multimodal datasets. Our experiments demonstrate the superiority of our method compared to existing works on RGB-thermal and RGB-gated datasets, performing fusion using only a small amount of additional parameters. Our code is available at https://github.com/dsriaditya999/RGBXFusion.
Securing Refugee Identity: A Literature Review on Blockchain-based Smart Contract

Identity documentation for refugees is a complex process and crucial for host nations. A secured identity management system ensures both security and the efficient provision of services for the host nation and the donor organizations. Realizing the benefits, a handful of studies enriched the blockchain-based security identification for refugees. The research studies presented the introductory, conceptual, and practical solution related to the blockchain-based smart contract. There is a common agreement in the studies that blockchain-based smart contract not only streamlines refugee identity verification but also safeguards against unauthorized entries. Since it is a technology as well, it has been essential to know the present status of the technology in the social context. In such a situation it becomes essential to review the existing research studies to provide insight for future studies. In this study, we reviewed current studies using a thematic approach. Our findings suggest researchers are more inclined to provide conceptual models as the models are important in advancing technology; however, the models need to be implemented for practical advances. However, the main contribution of this study is that this study gathers current efforts in smart contract-based refugee identity management. This study is important for the refugee host nations as well as for stakeholders. Knowledge gained from the study is expected to provide insight into how the technology can be developed using existing theory and implementation frameworks.
Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES

As part of the WMT-2023 "Test suites" shared task, in this paper we summarize the results of two test suites evaluations: MuST-SHE-WMT23 and INES. By focusing on the en-de and de-en language pairs, we rely on these newly created test suites to investigate systems' ability to translate feminine and masculine gender and produce gender-inclusive translations. Furthermore we discuss metrics associated with our test suites and validate them by means of human evaluations. Our results indicate that systems achieve reasonable and comparable performance in correctly translating both feminine and masculine gender forms for naturalistic gender phenomena. Instead, the generation of inclusive language forms in translation emerges as a challenging task for all the evaluated MT models, indicating room for future improvements and research on the topic.
Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs

Despite the recent progress in text summarization made by large language models (LLMs), they often generate summaries that are factually inconsistent with original articles, known as "hallucinations" in text generation. Unlike previous small models (e.g., BART, T5), current LLMs make fewer silly mistakes but more sophisticated ones, such as imposing cause and effect, adding false details, overgeneralizing, etc. These hallucinations are challenging to detect through traditional methods, which poses great challenges for improving the factual consistency of text summarization. In this paper, we propose an adversarially DEcoupling method to disentangle the Comprehension and EmbellishmeNT abilities of LLMs (DECENT). Furthermore, we adopt a probing-based efficient training to cover the shortage of sensitivity for true and false in the training process of LLMs. In this way, LLMs are less confused about embellishing and understanding; thus, they can execute the instructions more accurately and have enhanced abilities to distinguish hallucinations. Experimental results show that DECENT significantly improves the reliability of text summarization based on LLMs.
Mean BERTs make erratic language teachers: the effectiveness of latent bootstrapping in low-resource settings

This paper explores the use of latent bootstrapping, an alternative self-supervision technique, for pretraining language models. Unlike the typical practice of using self-supervision on discrete subwords, latent bootstrapping leverages contextualized embeddings for a richer supervision signal. We conduct experiments to assess how effective this approach is for acquiring linguistic knowledge from limited resources. Specifically, our experiments are based on the BabyLM shared task, which includes pretraining on two small curated corpora and an evaluation on four linguistic benchmarks.
Machine learning on quantum experimental data toward solving quantum many-body problems

Advancements in the implementation of quantum hardware have enabled the acquisition of data that are intractable for emulation with classical computers. The integration of classical machine learning (ML) algorithms with these data holds potential for unveiling obscure patterns. Although this hybrid approach extends the class of efficiently solvable problems compared to using only classical computers, this approach has been realized for solving restricted problems because of the prevalence of noise in current quantum computers. Here, we extend the applicability of the hybrid approach to problems of interest in many-body physics, such as predicting the properties of the ground state of a given Hamiltonian and classifying quantum phases. By performing experiments with various error-reducing procedures on superconducting quantum hardware with 127 qubits, we managed to acquire refined data from the quantum computer. This enabled us to demonstrate the successful implementation of classical ML algorithms for systems with up to 44 qubits. Our results verify the scalability and effectiveness of the classical ML algorithms for processing quantum experimental data.
Generating Context-Aware Natural Answers for Questions in 3D Scenes

3D question answering is a young field in 3D vision-language that is yet to be explored. Previous methods are limited to a pre-defined answer space and cannot generate answers naturally. In this work, we pivot the question answering task to a sequence generation task to generate free-form natural answers for questions in 3D scenes (Gen3DQA). To this end, we optimize our model directly on the language rewards to secure the global sentence semantics. Here, we also adapt a pragmatic language understanding reward to further improve the sentence quality. Our method sets a new SOTA on the ScanQA benchmark (CIDEr score 72.22/66.57 on the test sets).
Exploiting Image-Related Inductive Biases in Single-Branch Visual Tracking

Despite achieving state-of-the-art performance in visual tracking, recent single-branch trackers tend to overlook the weak prior assumptions associated with the Vision Transformer (ViT) encoder and inference pipeline. Moreover, the effectiveness of discriminative trackers remains constrained due to the adoption of the dual-branch pipeline. To tackle the inferior effectiveness of the vanilla ViT, we propose an Adaptive ViT Model Prediction tracker (AViTMP) to bridge the gap between single-branch network and discriminative models. Specifically, in the proposed encoder AViT-Enc, we introduce an adaptor module and joint target state embedding to enrich the dense embedding paradigm based on ViT. Then, we combine AViT-Enc with a dense-fusion decoder and a discriminative target model to predict accurate location. Further, to mitigate the limitations of conventional inference practice, we present a novel inference pipeline called CycleTrack, which bolsters the tracking robustness in the presence of distractors via bidirectional cycle tracking verification. Lastly, we propose a dual-frame update inference strategy that adeptively handles significant challenges in long-term scenarios. In the experiments, we evaluate AViTMP on ten tracking benchmarks for a comprehensive assessment, including LaSOT, LaSOTExtSub, AVisT, etc. The experimental results unequivocally establish that AViTMP attains state-of-the-art performance, especially on long-time tracking and robustness.
Model Uncertainty based Active Learning on Tabular Data using Boosted Trees

Supervised machine learning relies on the availability of good labelled data for model training. Labelled data is acquired by human annotation, which is a cumbersome and costly process, often requiring subject matter experts. Active learning is a sub-field of machine learning which helps in obtaining the labelled data efficiently by selecting the most valuable data instances for model training and querying the labels only for those instances from the human annotator. Recently, a lot of research has been done in the field of active learning, especially for deep neural network based models. Although deep learning shines when dealing with image\textual\multimodal data, gradient boosting methods still tend to achieve much better results on tabular data. In this work, we explore active learning for tabular data using boosted trees. Uncertainty based sampling in active learning is the most commonly used querying strategy, wherein the labels of those instances are sequentially queried for which the current model prediction is maximally uncertain. Entropy is often the choice for measuring uncertainty. However, entropy is not exactly a measure of model uncertainty. Although there has been a lot of work in deep learning for measuring model uncertainty and employing it in active learning, it is yet to be explored for non-neural network models. To this end, we explore the effectiveness of boosted trees based model uncertainty methods in active learning. Leveraging this model uncertainty, we propose an uncertainty based sampling in active learning for regression tasks on tabular data. Additionally, we also propose a novel cost-effective active learning method for regression tasks along with an improved cost-effective active learning method for classification tasks.
Analysis Validation of Modern Physics Learning Media Based on Smartphone Integrated Project Based Learning to Improve Students’ Creativity and Scientific Literacy

The development of modern physics learning media based on project based learning integrated with smartphones to increase students' creativity and scientific literacy has been successfully carried out. The aim of this development is to produce modern physics learning media based on project based learning integrated with smartphones that is valid to increase students' creativity and scientific literacy. The media development uses a 4D model which consists of four stages, namely: define, design, develop and disseminate. Based on the results of assessments by material experts, learning media experts and learning experts, it can be concluded that modern physics learning media based on project based learning integrated with smartphones is very suitable to be used as a learning media in increasing students' creativity and scientific literacy.
Life History Support System “LHS” - Recording Memories and Sharing Stories for Family Social Network

The world is aging rapidly, and the population of over 65 years old in Japan is 29.1% (Sep. 15, 2022, Japan Statistic Bureau of Japan), which is the highest in Japanese history. Human memory and knowledge are rapidly being digitized on an incalculable scale. While its value as a booster for monetization is now known worldwide, such private and personal heritage, especially its whereabouts remains unknown. In particular, the memory and knowledge of elders are not recorded appropriately for the next generations, we claim that the current system has shown an enormous loss of value in society, especially for the family members. Therefore, the desire to interview and document the life experiences of different generations of family members is very important. However, interviewing and documenting are difficult to achieve for various reasons, in such cases as when family members live apart from each other. Therefore, our research group has started to develop Life History Support System called “LHS”. The new system aims to solve the problem and preserve elderlies' wisdom and knowledge cultivated in turbulent times, such as during WW II and the post-war years of recovery. The LHS is designed for the Family Social Network, allowing digital information to be accessed only by the same members. LHS is an application that runs on smartphones, tablets and PC which is connected to the Internet and works as a social network system (SNS), but the main difference between conventional SNS is (1) LHS can be accessed only by the family members or designated members, (2) it mainly works as a card type database to share topic cards among members. We have developed a prototype system using Apple’s Claris FileMaker database system which runs on-premises private server. Then, to test the prototype's applicability, we have performed a preliminary interview experiment in an actual user environment (family members living together or living apart, and the elderly person living alone). The result shows that we could identified the experience of “fun” by both, an interviewer and interviewee, during the process of recall of memories with the LHS setup. Rather, we confirm the needs in longitudinal study to capture the continuous use of the LHS. Since the LHS inherently gains its value by long-term regular use, interviewing, recording and viewing by many family members, it is necessary to add new functions based on some theories. We are planning to include gamification functions to LHS. This paper describes the LHS system overview and the current development status.
NARRATIVE TEXTS IN INDONESIAN ELT TEXTBOOKS: A SYSTEMIC FUNCTIONAL ANALYSIS FOR EDUCATIONAL PURPOSES

This paper aims at examining social functions, schematic structure, transitivity, and modality in narrative texts in an English textbook published by the Indonesian government and used at the tenth grade English classes. Three narrative texts were found in the textbook and selected as samples. Hallidayan concepts of social functions, schematic structure, transitivity and modality were used as theoretical epistemological concepts and his lexico-grammatical analytical framework was used as a methodological procedure of data analysis. Clauses in the three texts (i.e., Issumboshi, The Legend of Malin Kundang, and Strong Wind) were coded by numbering them and analyzed in a series of systemic functional linguistic analyses: Theme-Rheme, process-type analysis, mood-residue analysis, and functional analysis. The texts display this schematic pattern: orientation^evaluation^compilcation^resolution. The absence of reorientation within the generic structure implies the written nature of the texts. The study also presents linguistic patterns in transitivity where particular process types were used in each schematic structure. The study also proves that, although narrated in written modes and declarative clauses were dominantly exploited, the time of event and the judgment of the narrating writers were static where temporal settings of the events were always in the past and the narrators’ judgment of them remained high. These types of time and judgment enabled the narrators to convey socialization, education and entertainment functions of narrative texts and narration practices. Additionally, these analyses could exemplify an education model of systemic functional linguistic analysis and reduce currently common misconceptions in Indonesian ELT textbooks.
Konsentrasi Limbah Pencemar MBAS (Methylen Blue Active Substance) pada Perairan Muara Kali Ijo, Kabupaten Kebumen

Muara Kali Ijo is the place where rivers flow in Kebumen Regency, which carry a lot of domestic wastes resulting from sanitation activities, namely detergent. The study aims to determine the quality of Muara Kali Ijo Waters based on MBAS concentration (Methylen Blue Active Substances). MBAS is a standard method for determining the concentration of anionic surfactants in detergents. This study uses descriptive methods with purposive random sampling techniques. Sampling was carried out on March 11, 2023 with a total of five stations. The results of the analysis of the MBAS concentration in the BBSPJPPI laboratory ranged from 0.031 – 0.064 mg/L and the measurement of water quality parameters was still relatively good, below the quality standard threshold. PCA results show that there is a negative correlation between MBAS concentration and water quality parameters. Productive anthropogenic activities around the area have a potential to contribute detergent waste from washing activities and increase MBAS concentrations.&#x0D; Keywords: antropogenic, muara kali ijo, MBAS, PCA, waters quality
RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency

In this paper, we study the problem of continuous 3D shape representations. The majority of existing successful methods are coordinate-based implicit neural representations. However, they are inefficient to render novel views or recover explicit surface points. A few works start to formulate 3D shapes as ray-based neural functions, but the learned structures are inferior due to the lack of multi-view geometry consistency. To tackle these challenges, we propose a new framework called RayDF. It consists of three major components: 1) the simple ray-surface distance field, 2) the novel dual-ray visibility classifier, and 3) a multi-view consistency optimization module to drive the learned ray-surface distances to be multi-view geometry consistent. We extensively evaluate our method on three public datasets, demonstrating remarkable performance in 3D surface point reconstruction on both synthetic and challenging real-world 3D scenes, clearly surpassing existing coordinate-based and ray-based baselines. Most notably, our method achieves a 1000x faster speed than coordinate-based methods to render an 800x800 depth image, showing the superiority of our method for 3D shape representation. Our code and data are available at https://github.com/vLAR-group/RayDF
TRAINING OF A CONVOLUTIONAL NEURAL NETWORK FOR HAND GESTURE RECOGNIZING ON THE KAGGLE ASL ALPHABET DATASET

Nowadays, hand gesture recognizing is important topic. It is used in virtual assistant work, for sign language translation, in virtual and augmented reality applications, and in entertainment services. The paper deals with the convolutional neural network training using different technologies. The neural network is trained to classify American manual alphabet and 3 extended gestures using photographs. The open access dataset Kaggle ASL Alphabet was used for training. Kaggle ASL Alphabet provides 87000 images of 29 classes for image classification and hand gesture recognizing.
Bidirectional Captioning for Clinically Accurate and Interpretable Models

Vision-language pretraining has been shown to produce high-quality visual encoders which transfer efficiently to downstream computer vision tasks. While generative language models have gained widespread attention, image captioning has thus far been mostly overlooked as a form of cross-modal pretraining in favor of contrastive learning, especially in medical image analysis. In this paper, we experiment with bidirectional captioning of radiology reports as a form of pretraining and compare the quality and utility of learned embeddings with those from contrastive pretraining methods. We optimize a CNN encoder, transformer decoder architecture named RadTex for the radiology domain. Results show that not only does captioning pretraining yield visual encoders that are competitive with contrastive pretraining (CheXpert competition multi-label AUC of 89.4%), but also that our transformer decoder is capable of generating clinically relevant reports (captioning macro-F1 score of 0.349 using CheXpert labeler) and responding to prompts with targeted, interactive outputs.