Reviewerly/exHarmony huggingface dataset:
Task-Agnostic Low-Rank Adapters for Unseen English Dialects

Large Language Models (LLMs) are trained on corpora disproportionally weighted in favor of Standard American English. As a result, speakers of other dialects experience significantly more failures when interacting with these technologies. In practice, these speakers often accommodate their speech to be better understood. Our work shares the belief that language technologies should be designed to accommodate the diversity in English dialects and not the other way around. However, prior works on dialect struggle with generalizing to evolving and emerging dialects in a scalable manner. To fill this gap, our method, HyperLoRA, leverages expert linguistic knowledge to enable resource-efficient adaptation via hypernetworks. By disentangling dialect-specific and cross-dialectal information, HyperLoRA improves generalization to unseen dialects in a task-agnostic fashion. Not only is HyperLoRA more scalable in the number of parameters, but it also achieves the best or most competitive performance across 5 dialects in a zero-shot setting. In this way, our approach facilitates access to language technology for billions of English dialect speakers who are traditionally underrepresented.
From Ledger to P2P Network: De-anonymization on Bitcoin Using Cross-Layer Analysis

Cryptocurrency has the characteristics of decentralization and anonymization, which have emerged and attracted widespread attention from various parties. However, cryptocurrency anonymization breeds illegal activities such as money laundering, gambling, and phishing. Thus, it is essential to deanonymity on Cryptocurrency transactions. This paper proposes a cross-layer analysis method for Bitcoin transactions deanonymization. Through acquiring large-scale original transaction information and combining the characteristics of the network layer and the transaction layer, we propose a propagation pattern extraction model and associated address clustering model. We achieve the matching of the suspected transaction with the originator’s IP address for high precision and low overhead. Through experimental analysis in a real Bitcoin system, the cross-layer method can effectively match the original transaction with the target node, which reaches an accuracy of 81.3% and is 30% higher than the state-of-the-art method. By controlling several factors, such as different times and nodes, the characteristics of the extracted transaction propagation pattern can be proved reasonable and reliable. The practicality and effectiveness of the cross-layer analysis are higher than that of a single-level scheme.
Augment the Pairs: Semantics-Preserving Image-Caption Pair Augmentation for Grounding-Based Vision and Language Models

Grounding-based vision and language models have been successfully applied to low-level vision tasks, aiming to precisely locate objects referred in captions. The effectiveness of grounding representation learning heavily relies on the scale of the training dataset. Despite being a useful data enrichment strategy, data augmentation has received minimal attention in existing vision and language tasks as augmentation for image-caption pairs is non-trivial. In this study, we propose a robust phrase grounding model trained with text-conditioned and text-unconditioned data augmentations. Specifically, we apply text-conditioned color jittering and horizontal flipping to ensure semantic consistency between images and captions. To guarantee image-caption correspondence in the training samples, we modify the captions according to pre-defined keywords when applying horizontal flipping. Additionally, inspired by recent masked signal reconstruction, we propose to use pixel-level masking as a novel form of data augmentation. While we demonstrate our data augmentation method with MDETR framework, the proposed approach is applicable to common grounding-based vision and language tasks with other frameworks. Finally, we show that image encoder pretrained on large-scale image and language datasets (such as CLIP) can further improve the results. Through extensive experiments on three commonly applied datasets: Flickr30k, referring expressions and GQA, our method demonstrates advanced performance over the state-of-the-arts with various metrics. Code can be found in https://github.com/amzn/augment-the-pairs-wacv2024.
CTC_BERT: a chinese text correction model with multi-scale semantic feature caption

Text correction aims to determine whether natural language text contains grammatical errors, text errors, etc., and correct sentences. Previous work usually adopts byte pair encoding (BPE), which may lead to semantically related Chinese characters being separated. In addition, the previous models can only extract the superficial semantic features, but cannot capture the global deep semantic relationships. In this paper, we introduce the time convolutional network (TCN) to capture multi-scale semantic information, so as to promote the development of global semantic information. The CTC_BERT model uses the synonym masking strategy to reduce the semantic segmentation of related words and adds a fully connection layer as the error detection layer. In order to verify the performance of the CTC_BERT model, a comparison experiment has been carried out on SIGHAN2015+Wang271K Chinese error correction dataset. The results show that the accuracy of this model can reach 81.4%, which is better than that of BERT, BART, ConvSeq2Seq and other conventional models, and effectively improves the performance of text error correction.
TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping

Data augmentation methods for neural machine translation are particularly useful when limited amount of training data is available, which is often the case when dealing with low-resource languages. We introduce a novel augmentation method, which generates new sentences by swapping objects and subjects across bisentences. This is performed simultaneously based on the dependency parse trees of the source and target sentences. We name this method TreeSwap. Our results show that TreeSwap achieves consistent improvements over baseline models in 4 language pairs in both directions on resource-constrained datasets. We also explore domain-specific corpora, but find that our method does not make significant improvements on law, medical and IT data. We report the scores of similar augmentation methods and find that TreeSwap performs comparably. We also analyze the generated sentences qualitatively and find that the augmentation produces a correct translation in most cases. Our code is available on Github.
Innovation and Word Usage Patterns in Machine Learning

In this study, we delve into the dynamic landscape of machine learning research evolution. Initially, through the utilization of Latent Dirichlet Allocation, we discern pivotal themes and fundamental concepts that have emerged within the realm of machine learning. Subsequently, we undertake a comprehensive analysis to track the evolutionary trajectories of these identified themes. To quantify the novelty and divergence of research contributions, we employ the Kullback-Leibler Divergence metric. This statistical measure serves as a proxy for ``surprise'', indicating the extent of differentiation between the content of academic papers and the subsequent developments in research. By amalgamating these insights, we gain the ability to ascertain the pivotal roles played by prominent researchers and the significance of specific academic venues (periodicals and conferences) within the machine learning domain.
Design of a conceptual framework for cybersecurity culture amongst online banking users in Nigeria

This study aims to construct a comprehensive conceptual framework that elucidates the critical human factors influencing cybersecurity culture among online banking users in Nigeria. The research methodology is grounded in a meticulous examination of existing literature in the cybersecurity culture domain, serving as the foundation for this framework. The literature review reveals a conspicuous absence of academic research on cybersecurity culture within Nigeria and underscores the importance of comprehending its unique nuances. Key findings from the literature review highlight the prominence of “cybersecurity awareness,” “cybersecurity policy,” and “cybersecurity education” as influential factors. “Cybersecurity awareness” emerges as the most pivotal factor due to its recurrent emphasis and recognized centrality. “Cybersecurity policy” and “cybersecurity education” secure the second and third positions, respectively, due to their acknowledged significance in cultivating a security-conscious mindset among online banking users. Furthermore, the literature review exposes a research gap concerning the requisite “cybersecurity knowledge” that should permeate organizations and individuals to augment cybersecurity culture. Additionally, it reveals the underexplored influence of “social norms” and “interpersonal trust” in molding cybersecurity culture. This research accentuates the dearth of cybersecurity culture research within Nigeria and underscores the importance of understanding its unique facets. The proposed conceptual framework provides a valuable resource for designing tailored cybersecurity strategies and programs in Nigeria’s online banking sector. It advocates for prioritizing cybersecurity awareness, education, and policy, empowering users with the knowledge and skills needed to safeguard themselves against cyber threats. The model also highlights the relevance of recognizing the role played by social dynamics, interpersonal trust, and social norms in shaping cybersecurity behaviours.
Color image encryption by a non-chain Galois ring extension

Abstract The S-box is a vital component for producing nonlinearity in a block cipher. Seldom this component is constructed through finite fields; however, sometimes Galois rings are used for its construction. In this work, we utilize the extended Galois ring to construct a Substitution box (S-box) over the ring <?CDATA $\frac{{GR}\left(\mathrm{4,4}\right)[x]}{\lt {x}^{2}\gt }.$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfrac> <mml:mrow> <mml:mi mathvariant="italic">GR</mml:mi> <mml:mrow> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mn>4,4</mml:mn> </mml:mrow> </mml:mfenced> </mml:mrow> <mml:mo stretchy="false">[</mml:mo> <mml:mi>x</mml:mi> <mml:mo stretchy="false">]</mml:mo> </mml:mrow> <mml:mrow> <mml:mo>&lt;</mml:mo> <mml:msup> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>&gt;</mml:mo> </mml:mrow> </mml:mfrac> <mml:mo>.</mml:mo> </mml:math> The designed S-box is generated by multiple elements and thus exhibits a high level of confusion compared to typical S-boxes. An encrypted color image is compared to existing encrypted S-box images to investigate their effects. First, we separate the distinct channels from a digital image and create 256-order matrices with 16-bit entries for each channel. We then apply the designed image encryption algorithm to these matrices. For the permutation step, we use the 1-dimensional logistic map to generate the key. Then, we apply the exclusive-or operation to all three layers of the image using the key. Finally, concatenate all these layers to create the encrypted image. The result is the proposal of a new RGB image encryption technique. The proposed encryption technique can replace several image encryption techniques that rely on S-boxes. In general, this technique aims to achieve optimal values.
Widely Applicable Strong Baseline for Sports Ball Detection and Tracking

In this work, we present a novel Sports Ball Detection and Tracking (SBDT) method that can be applied to various sports categories. Our approach is composed of (1) high-resolution feature extraction, (2) position-aware model training, and (3) inference considering temporal consistency, all of which are put together as a new SBDT baseline. Besides, to validate the wide-applicability of our approach, we compare our baseline with 6 state-of-the-art SBDT methods on 5 datasets from different sports categories. We achieve this by newly introducing two SBDT datasets, providing new ball annotations for two datasets, and re-implementing all the methods to ease extensive comparison. Experimental results demonstrate that our approach is substantially superior to existing methods on all the sports categories covered by the datasets. We believe our proposed method can play as a Widely Applicable Strong Baseline (WASB) of SBDT, and our datasets and codebase will promote future SBDT research. Datasets and codes are available at https://github.com/nttcom/WASB-SBDT .
Super-Resolution Emulation of Large Cosmological Fields with a 3D Conditional Diffusion Model

High-resolution (HR) simulations in cosmology, in particular when including baryons, can take millions of CPU hours. On the other hand, low-resolution (LR) dark matter simulations of the same cosmological volume use minimal computing resources. We develop a denoising diffusion super-resolution emulator for large cosmological simulation volumes. Our approach is based on the image-to-image Palette diffusion model, which we modify to 3 dimensions. Our super-resolution emulator is trained to perform outpainting, and can thus upgrade very large cosmological volumes from LR to HR using an iterative outpainting procedure. As an application, we generate a simulation box with 8 times the volume of the Illustris TNG300 training data, constructed with over 9000 outpainting iterations, and quantify its accuracy using various summary statistics.
ASSESSMENT OF THE HEAVY METAL POLLUTION OF THE SOIL OF AJAOKUTA STEEL COMPANY, AJAOKUTA, KOGI STATE NIGERIA

Soil samples from different sections of Ajaokuta Steel Complex, Ajaokuta were collected randomly for the analysis of the concentration of selected heavy metals caused by the activities of the iron and steel complex. The results obtained were compared with WHO, NAFDAC and SON standards; and the parameters assessed fall within the permissible limits. The low concentration could be attributed to the fact that the Ajaokuta Steel Company has not been in operation for more than five years. The analysis was done using pH meter and atomic absorption spectrometric technique. The pH of the soil S1 (7.50), S2 (7.60), S3 (7.10) and S5 (7.20) were slightly basic and S4 (6.10) and S6 (6.90) were slightly acidic and the concentration of the heavy metals in ppm were within the permissible range except for Fe which is in a high concentration, which was found to range between 97.4to116.5 mg/Kg. Heavy metal pollutants in the environment are of immense concern globally, so there is need to study and enlighten the habitants of such environments where this giant company is situated as clarified in this study.
A flexible framework for synthesizing human activity patterns with application to sequential categorical data

The ability to synthesize realistic data in a parametrizable way is valuable for a number of reasons, including privacy, missing data imputation, and evaluating the performance of statistical and computational methods. When the underlying data generating process is complex, data synthesis requires approaches that balance realism and simplicity. In this paper, we address the problem of synthesizing sequential categorical data of the type that is increasingly available from mobile applications and sensors that record participant status continuously over the course of multiple days and weeks. We propose the paired Markov Chain (paired-MC) method, a flexible framework that produces sequences that closely mimic real data while providing a straightforward mechanism for modifying characteristics of the synthesized sequences. We demonstrate the paired-MC method on two datasets, one reflecting daily human activity patterns collected via a smartphone application, and one encoding the intensities of physical activity measured by wearable accelerometers. In both settings, sequences synthesized by paired-MC better capture key characteristics of the real data than alternative approaches.
Language Models can be Logical Solvers

Logical reasoning is a fundamental aspect of human intelligence and a key component of tasks like problem-solving and decision-making. Recent advancements have enabled Large Language Models (LLMs) to potentially exhibit reasoning capabilities, but complex logical reasoning remains a challenge. The state-of-the-art, solver-augmented language models, use LLMs to parse natural language logical questions into symbolic representations first and then adopt external logical solvers to take in the symbolic representations and output the answers. Despite their impressive performance, any parsing errors will inevitably result in the failure of the execution of the external logical solver and no answer to the logical questions. In this paper, we introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers and bypasses the parsing errors by learning to strict adherence to solver syntax and grammar. LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers. Experimental results on two public deductive reasoning datasets demonstrate that LoGiPT outperforms state-of-the-art solver-augmented LMs and few-shot prompting methods on competitive LLMs like ChatGPT or GPT-4.
Differential Fault Analysis Against AES Based on a Hybrid Fault Model

In this paper, a differential fault analysis based on a hybrid fault model is proposed. The hybrid fault model is comprising a one-byte and multi-byte by injecting faults in the state. Through both theory and simulations, which successfully derived the key of AES-128, 192, and 256 with two, three, and four pairs of faulty ciphertexts (pairs of faulty ciphertext refers to the correct ciphertext and the responding faulty ciphertext after injecting faults) without exhaustive search, respectively. Compared with the latest methods, the method proposed only requires the fault injected in a single round, thus it is easier to carry out to an attacker. When considering AES-192, fewer faulty ciphertexts are needed. In addition, for both AES-192 and 256, our method requires fewer depths of induced fault (the entire key can be retrieved only need to induce fault in the T-2 round). Thus, the DFA proposed in this article is more efficient.
Spotting Fake Profiles in Social Networks via Keystroke Dynamics

Spotting and removing fake profiles could curb the menace of fake news in society. This paper, thus, investigates fake profile detection in social networks via users' typing patterns. We created a novel dataset of 468 posts from 26 users on three social networks: Facebook, Instagram, and X (previously Twitter) over six sessions. Then, we extract a series of features from keystroke timings and use them to predict whether two posts originated from the same users using three prominent statistical methods and their score-level fusion. The models' performance is evaluated under same, cross, and combined-cross-platform scenarios. We report the performance using k-rank accuracy for k varying from 1 to 5. The best-performing model obtained accuracies between 91.6-100% on Facebook (Fusion), 70.8-87.5% on Instagram (Fusion), and 75-87.5% on X (Fusion) for k from 1 to 5. Under a cross-platform scenario, the fusion model achieved mean accuracies of 79.1-91.6%, 87.5-91.6%, and 83.3-87.5% when trained on Facebook, Instagram, and Twitter posts, respectively. In combined cross-platform, which involved mixing two platforms' data for model training while testing happened on the third platform's data, the best model achieved accuracy ranges of 75-95.8% across different scenarios. The results highlight the potential of the presented method in uncovering fake profiles across social network platforms.
Step by Step to Fairness: Attributing Societal Bias in Task-oriented Dialogue Systems

Recent works have shown considerable improvements in task-oriented dialogue (TOD) systems by utilizing pretrained large language models (LLMs) in an end-to-end manner. However, the biased behavior of each component in a TOD system and the error propagation issue in the end-to-end framework can lead to seriously biased TOD responses. Existing works of fairness only focus on the total bias of a system. In this paper, we propose a diagnosis method to attribute bias to each component of a TOD system. With the proposed attribution method, we can gain a deeper understanding of the sources of bias. Additionally, researchers can mitigate biased model behavior at a more granular level. We conduct experiments to attribute the TOD system's bias toward three demographic axes: gender, age, and race. Experimental results show that the bias of a TOD system usually comes from the response generation model.
A Method to Detect Human Hands Moving Objects

The action of moving an object with a hand is one of the main activities in human-object interaction. This paper proposes a novel method to detect human hands moving objects in video. The proposed method analyzes the motion information in the area around the extracted forearm tip and determines that the hand is moving an object if there is a part showing the similar motion as something moved by hand in the surrounding area. Since this method detects hands moving objects based on features that can be acquired without extracting the object regions from video, it is expected to be applied to various situations.
Template Matching Algorithm Implementation For Introduction To Indonesian Traditional House

Digital image processing is an important way in computer vision to determine the shape of digital image objects. Many image processing applications have been produced, one of which is pattern recognition. Pattern recognition can be interpreted as the process of classifying a number of objects into several categories based on similarities and similarities in characteristics. In this study, the pattern recognition of traditional houses in Indonesia will be carried out. In the pattern recognition process, several stages of image processing (image pre-processing) are needed which aims to make the pattern obtained can be recognized accurately. For the introduction of this traditional house, several stages will be carried out before it can finally be recognized by the system. First, the image will be converted into a gray image, after the gray image is obtained, it will be converted into a binary image using thresholding and then followed by normalizing the image size (resize). After that, the resized image will be recognized using the Template Matching method. The results of this study, using 20 examples of traditional house patterns categorized into 10 types of traditional houses in Indonesia, the system can recognize 16 traditional house patterns correctly with an accuracy percentage of 80%. But this system also still has imperfections. Imperfections occur due to several factors such as taking the angle of the object in the test image that does not match the angle of the object in the target image, and also the presence of additional objects that cover the shape of the traditional house that will be recognized; such as: trees, cars, etc., as well as poor test image conditions.