Reviewerly/exHarmony huggingface dataset:
Hg and As pollution in the soil-plant system evaluated by combining multispectral UAV-RS, geochemical survey and machine learning

The combination of a low-density geochemical survey, multispectral data obtained with Unmanned Aerial Vehicle-Remote Sensing (UAV-RS), and a machine learning technique was tested in the search for a statistically robust prediction of contaminant distribution in soil and vegetation, for zones with a highly variable pollutant load. To this end, a novel methodology was devised by means of a limited geochemical study of topsoil and vegetation combined with multispectral data obtained by UAV-RS. The methodology was verified in an area affected by Hg and As contamination that typifies abandoned mining-metallurgy sites in recent decades. A broad selection of spectral indices were calculated to evaluate soil-plant system response, and four machine learning techniques (Multiple Linear Regression, Random Forest, Generalized Boosted Models, and Multivariate Adaptive Regression Spline) were tested to obtain robust statistical models. Random Forest (RF) provided the best non-biased models for As and Hg concentration in soil and vegetation, with R2 and rRMSE (%) ranging from 0.501 to 0.630 and from 180.72 to 46.31, respectively, and with acceptable values for RPD and RPIQ statistics. The prediction and mapping of contaminant content and distribution in the study area were well enough adjusted to the geochemical data and revealed superior accuracy for As than Hg, and for vegetation than topsoil. The results were more precise than those obtained in comparable studies that applied satellite or spectrometry data. In conclusion, the methodology presented emerges as a powerful tool for studies addressing soil and vegetation pollution and an alternative approach to classical geochemical studies, which are time-consuming and expensive.
Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning

Are Federated Learning (FL) systems free from backdoor poisoning with the arsenal of various defense strategies deployed? This is an intriguing problem with significant practical implications regarding the utility of FL services. Despite the recent flourish of poisoning-resilient FL methods, our study shows that carefully tuning the collusion between malicious participants can minimize the trigger-induced bias of the poisoned local model from the poison-free one, which plays the key role in delivering stealthy backdoor attacks and circumventing a wide spectrum of state-of-the-art defense methods in FL. In our work, we instantiate the attack strategy by proposing a distributed backdoor attack method, namely Cerberus Poisoning (CerP). It jointly tunes the backdoor trigger and controls the poisoned model changes on each malicious participant to achieve a stealthy yet successful backdoor attack against a wide spectrum of defensive mechanisms of federated learning techniques. Our extensive study on 3 large-scale benchmark datasets and 13 mainstream defensive mechanisms confirms that Cerberus Poisoning raises a significantly severe threat to the integrity and security of federated learning practices, regardless of the flourish of robust Federated Learning methods.
Hybrid CNN-Transformer Feature Fusion for Single Image Deraining

Since rain streaks exhibit diverse geometric appearances and irregular overlapped phenomena, these complex characteristics challenge the design of an effective single image deraining model. To this end, rich local-global information representations are increasingly indispensable for better satisfying rain removal. In this paper, we propose a lightweight Hybrid CNN-Transformer Feature Fusion Network (dubbed as HCT-FFN) in a stage-by-stage progressive manner, which can harmonize these two architectures to help image restoration by leveraging their individual learning strengths. Specifically, we stack a sequence of the degradation-aware mixture of experts (DaMoE) modules in the CNN-based stage, where appropriate local experts adaptively enable the model to emphasize spatially-varying rain distribution features. As for the Transformer-based stage, a background-aware vision Transformer (BaViT) module is employed to complement spatially-long feature dependencies of images, so as to achieve global texture recovery while preserving the required structure. Considering the indeterminate knowledge discrepancy among CNN features and Transformer features, we introduce an interactive fusion branch at adjacent stages to further facilitate the reconstruction of high-quality deraining results. Extensive evaluations show the effectiveness and extensibility of our developed HCT-FFN. The source code is available at https://github.com/cschenxiang/HCT-FFN.
Analysis of the challenges for blockchain technology adoption in the Indian health-care sector

Purpose This paper aims to discover and analyze the challenges hampering blockchain technology’s (BT’s) implementation in the Indian health-care sector. A total of 18 challenges have been prioritized and modeled based on an extensive literature search and professional views. Design/methodology/approach An integrated multi-criteria decision-making approach has been used in two phases. Best worst method (BWM) is used in the first phase to prioritize the challenges with sensitivity analysis to validate the findings and eliminate a few challenges. In the second phase, interpretive structural modeling is applied to the remaining 15 challenges to obtain relative relationships among them with cross-impact matrix multiplication applied to classification analysis for their categorization. Findings The study’s results reveal that limited knowledge and expertise, cost and risk involved, technical issues, lack of clear regulations, resistance to change and lack of top management support are the top-ranked or high-intensity challenges according to the BWM. Interpretive structural modelling findings suggest that the lack of government initiatives has been driving other challenges with the highest driving power. Research limitations/implications This work has been conducted in the Indian context, so careful generalization of the results is needed. Practical implications This work will give health-care stakeholders a better perspective regarding blockchain’s adoption. It will help health-care stakeholders, service providers, researchers and policymakers get a glimpse of the strategies for eradicating mentioned challenges. The analysis will help reduce the challenges’ impact on blockchain’s adoption in the Indian health-care sector. Originality/value The adoption of BT is a novel concept, especially in developing countries such as India. This is one of the few works addressing the challenges to BT adoption in the Indian health-care sector.
Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination

In this work, we investigate a more realistic unsupervised multimodal machine translation (UMMT) setup, inference-time image-free UMMT, where the model is trained with sourcetext image pairs, and tested with only sourcetext inputs.First, we represent the input images and texts with the visual and language scene graphs (SG), where such fine-grained visionlanguage features ensure a holistic understanding of the semantics.To enable pure-text input during inference, we devise a visual scene hallucination mechanism that dynamically generates pseudo visual SG from the given textual SG.Several SG-pivoting based learning objectives are introduced for unsupervised translation training.On the benchmark Multi30K data, our SG-based method outperforms the best-performing baseline by significant BLEU scores on the task and setup, helping yield translations with better completeness, relevance and fluency without relying on paired images.Further in-depth analyses reveal how our model advances in the task setting.
A Survey of Deep Learning for Mathematical Reasoning

Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life.The development of artificial intelligence (AI) systems capable of solving math problems and proving theorems in language has garnered significant interest in the fields of machine learning and natural language processing.For example, mathematics serves as a testbed for aspects of reasoning that are challenging for powerful deep learning models, driving new algorithmic and modeling advances.On the other hand, recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning.In this survey paper, we review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade.We also evaluate existing benchmarks and methods, and discuss future research directions in this domain.Question: Bod has 2 apples and David has 5 apples.How many apples do they have in total?
Revisiting Transformer-based Models for Long Document Classification

The recent literature in text classification is biased towards short text sequences (e.g., sentences or paragraphs).In real-world applications, multi-page multi-paragraph documents are common and they cannot be efficiently encoded by vanilla Transformer-based models.We compare different Transformer-based Long Document Classification (TrLDC) approaches that aim to mitigate the computational overhead of vanilla transformers to encode much longer text, namely sparse attention and hierarchical encoding methods.We examine several aspects of sparse attention (e.g., size of local attention window, use of global attention) and hierarchical (e.g., document splitting strategy) transformers on four document classification datasets covering different domains.We observe a clear benefit from being able to process longer text, and, based on our results, we derive practical advice of applying Transformer-based models on long document classification tasks. 1
The Virtual Currency Schemes

Abstract This chapter starts in Sect. 3.2 by discussing the different virtual currency schemes currently in existence in virtual environments. It will analyse the different currency schemes in relation to the physical economy. Based on this analysis this chapter continues in Sect. 3.3 by discussing the currency schemes in the Metaverse. This chapter will then conclude in Sect. 3.4 by analysing the virtual currency schemes of the Metaverse in relation to the real physical economy.
Demystifying Prompts in Language Models via Perplexity Estimation

Language models can be prompted to perform a wide variety of tasks with zero- and few-shot in-context learning. However, performance varies significantly with the choice of prompt, and we do not yet understand why this happens. In this paper, we analyze the factors that contribute to this variance and establish a new empirical hypothesis: the performance of a prompt is predicted by the extent to which the model is familiar with the language it contains. Over a wide range of tasks, we show that the lower the perplexity of the prompt, the better it is able to perform the task, when considering reasonable prompts that are related to it. As part of our analysis, we also devise a method to automatically extend a small seed set of manually written prompts by paraphrasing with GPT3 and backtranslation. This larger set allows us to verify that perplexity is a strong predictor of the success of a prompt and we show that the lowest perplexity prompts are consistently effective.
Large Language Models Can Self-Improve

Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improve their reasoning abilities by self-thinking without external inputs. In this work, we demonstrate that an LLM is also capable of self-improving with only unlabeled datasets. We use a pre-trained LLM to generate “high-confidence” rationale-augmented answers for unlabeled questions using Chain-of-Though (CoT) prompting and self-consistency, and fine-tune the LLM using those self-generated solutions as target outputs. We show that without any ground truth label, our approach improves the general reasoning ability of a 540B-parameter LLM (74.4%→82.1% on GSM8K, 90.0%→94.4% on OpenBookQA, and 63.4%→67.9% on ANLI-A3) and can also be adapted to extreme low-resource cases where even training questions and CoT prompts are limited. We conduct ablation studies and show that fine-tuning on diverse reasoning paths is critical for self-improvement.
Deep Regression Network for the Single Image Super Resolution of Multimedia Text Image

A key problem in low-level computer vision with several applications is super resolution of a picture. In order to create high resolution (HR) images with the necessary edge structures and texture information, Single Image Super Resolution (SISR) transforms low resolution (LR) photos into HR images. Additional information is provided by HR pictures, which can be used for a variety of tasks like security and medical imaging. However, the rebuilt image suffers from some loss in detail areas, making it more difficult to achieve higher accuracy with lower error. Regression network-based super-resolution (RNSR), which converts LR images into HR images, was created to solve these problems. For SISR, a deep regression network with 50 layers is created. The suggested RNSR approach translates LR multimedia text images into HR images with 98% accuracy, 0.02% error, 97% precision, and 94% specificity, according to the simulation study. The regression network may produce high-quality photos based on the effectiveness of the suggested RNSR approach.
Artificial Neural Networks Advantages and Disadvantages

Artificial neural networks (ANNs) are the modelling of the human brain with the simplest definition, and the building blocks are neurons. There are about 100 billion neurons in the human brain. Each neuron has a connection point between 1,000 and 100,000. In the human brain, information is stored in such a way as to be distributed, and we can extract more than one piece of this information from our memory in parallel when necessary. We are not mistaken when we say that a human brain is made up of thousands of very, very powerful parallel processors. In multi-layer artificial neural networks, there are also neurons placed in a similar manner to the human brain. Each neuron is connected to other neurons with specific coefficients. During training, information is distributed to these connection points so that the network is learned.
SegGPT: Towards Segmenting Everything In Context

We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images. The training of SegGPT is formulated as an in-context coloring problem with random color mapping for each data sample. The objective is to accomplish diverse tasks according to the context, rather than relying on specific colors. After training, SegGPT can perform arbitrary segmentation tasks in images or videos via in-context inference, such as object instance, stuff, part, contour, and text. SegGPT is evaluated on a broad range of tasks, including few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation. Our results show strong capabilities in segmenting in-domain and out-of-domain targets, either qualitatively or quantitatively.
Differences in Language and Style Between Two Social Media Communities

Microblogs are increasingly used as communication channels for organisations and their related communities. In this work, we are interested in the effect of community on the resulting microblog language use. We analyse content from Twitter, examining tweets relating to two government organisations—one conducting scientific research, the other providing social services. We find that the two different communities have significant differences in style and language use, observing marked differences in formality and tone as measured by properties such as pronominal usage, orthographic convention, and use of Twitter features. We posit that these differences arise due to underlying differences in the communication goals of the two user groups. Tools working with Twitter, to extract and represent information, may therefore need different approaches in different domains.
Towards Analyzing Micro-Blogs for Detection and Classification of Real-Time Intentions

Micro-blog forums, such as Twitter, constitute a powerful medium today that people use to express their thoughts and intentions on a daily, and in many cases, hourly, basis. Extracting ‘Real-Time Intention’ (RTI) of a user from such short text updates is a huge opportunity towards web personalization and social net- working around dynamic user context. In this paper, we explore the novel problem of detecting and classifying RTIs from micro-blogs. We find that employing a heuristic based ensemble approach on a reduced dimension of the feature space, based on a wide spectrum of linguistic and statistical features of RTI expressions, achieves significant improvement in detect- ing RTIs compared to word-level features used in many social media classification tasks today. Our solution approach takes into account various salient characteristics of micro-blogs towards such classification – high dimensionality, sparseness of data, limited context, grammatical in-correctness, etc.
Integrated Visual Servoing Solution to Quadrotor Stabilization and Attitude Estimation Using a Pan and Tilt Camera

This paper presents an integrated visual servoing solution to the problem of quadrotor attitude estimation and stabilization based solely on image feedback from a pan and tilt camera and biased rate gyros. The solution comprises a control law for the quadrotor position and attitude, a control law for the pan and tilt camera platform, and an estimator for the vehicle attitude, whose interconnection stabilizes the quadrotor in hover above certain terrain landmarks. Lateral-longitudinal stabilization is achieved with a nested saturation control law by feedback of the image measurements, estimated attitude, and corrected angular velocity measurements and the vehicle is stabilized vertically by means of image feedback. Resorting to the input-to-state stability properties of the controllers, the quadrotor's position and attitude are shown to converge to the desired equilibrium point and, in the end, the interconnected system's closed loop is proven robust to observer estimation errors. Additionally, the pan and tilt camera is actively actuated to keep the relevant features visible in the image field of view for most operating conditions. The robustness and performance of the proposed control and estimation architecture is illustrated through both simulation and experimental results.
Log Transformation and the Effect on Estimation, Implication, and Interpretation of Mean and Measurement Uncertainty in Microbial Enumeration

Abstract Background: Estimation of measurement uncertainty (MU) has been extensively addressed in documents from standard authorities. In microbiology, bacterial counts are log transformed to get a more normal distribution. Unfortunately, the difference between using original and log-transformed data appears to not have been investigated even in publications focusing on MU estimation. Method: Statistical formulae inferencing and estimation of MU using real bacterial enumeration datasets. Results: Both mean and SD calculated from original data carry the same scale and unit as the original data. However, the mean of log-transformed data becomes a geometric mean in log, and the SD becomes the logarithm of a ratio. Furthermore, calculation of RSD obtained by dividing the SD by the mean is meaningless and misleading for log-transformed data. The ratio, the antilog of the SD of log-transformed data, copes with multiplicative and divisive relationships to geometric mean (without log), instead of the arithmetic mean. The ratio can be converted to an analog ratio, which is similar or almost identical to the RSD of the untransformed data, especially when the within-subject variation is small. When MU is estimated from multiple samples with different measurands, the calculated RSD of original data is independent of the mean and can be pooled; however, for log-transformed data, the SD can be combined to estimate the common uncertainty. Conclusions: Calculation and use of RSD of log-transformed data are meaningless and misleading. Procedures outlining the estimation and interpretation of MU from log-transformed data require re-evaluation.
Distributed Nonlinear Semiparametric Support Vector Machine for Big Data Applications on Spark Frameworks

In recent years there has been a noticeable increase in the number of available Big Data infrastructures. This fact has promoted the adaptation of traditional machine learning techniques to be capable of addressing large scale problems in distributed environments. Kernel methods like support vector machines (SVMs) suffer from scalability problems due to their nonparametric nature and the complexity of their training procedures. In this paper, we propose a new and efficient distributed implementation of a training procedure for nonlinear semiparametric (budgeted) SVMs called distributed iterative reweighted least squares (IRWLS). This algorithm uses k-means to select the centroids of the semiparametric model and a new distributed algorithmic implementation of the IRWLS optimization procedure to find the weights of the model. We have implemented the proposed algorithm in Apache Spark and we have benchmarked it against other state-of-the-art methods, either full SVM (p-pack SVM) or budgeted (budgeted stochastic gradient descent). Experimental results show that the proposed algorithm achieves higher accuracy while controlling the size of the final model, and also offers high performance in terms of run time and efficiency, when processing very large datasets (the computation time grows linear with the number of training patterns).
Group collision attack

Key enumeration schemes are used to post-process the scores given by side channel distinguishers and enumerate the key candidates from the most possible one to the least possible one, which can be regarded as optimal tools of key search. However, the application of them is limited by very large key candidate space and computing power consumption. For example, the attacker may spend several weeks or months enumerating the whole 245 key candidates. Unlike the former literature that try to propose a more efficient algorithm to process the distinguishers, scores of key candidates directly, we focus on pre-processing and reducing the key candidate space. To achieve this goal, a new divide and conquer strategy named group collision attack (GCA) is proposed in this paper. The GCA works as follows in brief. The key candidates are first divided into groups on which intra-group collision attack is used to remove the impossible key combinations in each group. Then, the inter-group collision attack is performed to further remove the impossible key combinations between groups. Thus, the complexity of key enumeration is reduced significantly. A series of practical experiments are carried out by using our GCA and the experimental results verify its efficiency.