Reviewerly/exHarmony huggingface dataset:
A Model-Agnostic Feature Selection Technique to Improve the Performance of One-Class Classifiers

One-class classifiers hold promise for applications like fraudulent credit card transaction identification. However, interpreting these models to understand which features drive predictions is challenging. Such an understanding is necessary to avoid brute-force approaches to feature selection. This paper explores SHAP (SHapley Additive exPlanations) for feature selection with one-class classffiers on a credit card fraud dataset. We apply SHAP to select key features and evaluate One-Class Gaussian mixture models and One-Class Support Vector Machines. Statistical analysis tests show Gaussian mixture models built with SHAP-selected features perform significantly better than Gaussian mixture models built without feature selection. To the best of our knowledge, we are the first to show the benefit of SHAP-based feature selection to One-Class Gaussian mixture models. Moreover, we show that robust performance with features of the full dataset may be a prerequisite in order for SHAP feature selection to impart further gains. Our results provide novel evidence that SHAP can identify informative features for one-class classifiers.
Evaluation of Wavelets in Image Camera Source Identification

Application of wavelet transform for image camera source identification has been widely reported in the literature and the written techniques use different wavelets. Due to the wavelets’ diversity and properties, it is beneficial for the research community to identify the best-performing wavelets for this application. This paper presents results for assessing the performance of the conventional wavelet-based image camera source identification technique against forty-one wavelets from Daubechies, Biorthogonal, Symlets, and Coiflets wavelet families. VISION image dataset comprising 34,427 images captured by eleven camera brands of thirty-five models was used to generate experimental results. Hundred plane images from each camera brand dataset were randomly selected and used to generate experimental results, where 70% of each dataset’s images were used to compute the camera brand’s signature, and 30% of the images were used to assess the performance of the method. Normalized cross-correlation of the camera brand signature and calculated image noise were used to find the camera match. To compare the method’s performance when using different wavelets, a new assessment criterion was introduced and used to quantify the method’s performance across images of different camera brands. Results show that the conventional wavelet-based image camera source identification achieves its highest performance when it uses sym2 closely followed by coif1 wavelets.
Analyzing Recent (2019) Kashmir Socio-Political Issue: A Voyant Sentiment Analysis of Tweets

Computational Textual Analysis (CTA) is an effective way to analyze large texts by incorporating computational tools, such as, Voyant. The current study takes computer-assisted textual analysis (mixed method) to investigate the sentiments on tweets through Voyant computational method, in order to grasp the emotions of the global public who have tweeted about the recent (2019) Kashmir issue. This research is based on the conceptual framework of Ortony, Clore, and Collins (OCC) model and the keyword approach. For this purpose, different tweets have been analyzed to check the level of sentiments as either positive, negative or neutral. The findings suggested that sentiments were neutral towards the issue of Kashmir and negative towards the Indian government. Voyant has also presented the word count, density, and correlation of phrases within the larger text context. Voyant procedures like; cirrus, trend and summary showed the results based on quantitative and qualitative measures. These toolsets are easy to use without any programming skills and seem to be the best for researchers of social sciences and humanities who are trying to work in digital humanities. The study also recommends Voyant as an operative tool for textual analysis for Computational Linguists, Postmodernism, Critical Discourse Analysis, English Literature, History, Sociology, Theology, and any other field of knowledge that falls under the domain of digital humanities.
Software Bug Prediction Using Machine Learning Approach

Abstract: Software defect prophecy work focuses on the number of faults remaining in a software system. A prophecy of the number of remaining scars in an audited artefact can be used for decision timber. An accurate prophecy of the number of scars in a software product during system testing contributes not only to the operation of the system testing process but also to the estimation of the product’s demanded conservation. amiss software modules beget software failures, increase development and conservation costs, and drop customer satisfaction. It strives to meliorate software quality and testing effectiveness by constructing predictive models from law attributes to enable a timely identification of fault-prone modules. In this design, we will explore machine knowledge ways for software defect prophecy . This helps the formulators to descry software scars and correct them. Unsupervised ways may be used for defect prophecy in software modules, more so in those cases where defect labels are not available.
Error-mitigated fermionic classical shadows on noisy quantum devices

Abstract Efficiently estimating the expectation values of fermionic Hamiltonians, including $k$-particle reduced density matrices ($k$-RDMs) of an $n$-mode fermionic state, is crucial for quantum simulations of a wealth of physical systems from the fields of many-body physics, chemistry, and materials. Classical shadow (CS) algorithms have been proposed as a solution to address this task by substantially reducing the number of copies of quantum states. However, the implementation of these algorithms faces a significant challenge due to the inherent noise in near-term quantum devices, leading to inaccuracies in gate operations. To address this challenge, we propose an error-mitigated CS algorithm for fermionic systems. For $n$-qubit quantum systems, our algorithm, which employs the easily prepared initial state $|0^n\rangle\!\langle 0^n|$ assumed to be noiseless, provably efficiently estimates all elements of $k$-RDMs with $\widetilde{\mathcal O}(kn^k)$ scaled copies of quantum states and $\widetilde{\mathcal O}(\sqrt{n})$ scaled calibration measurements for the gate independent noise with constant average noise fidelities. The algorithm is robust against depolarizing, amplitude damping, and $X$-rotation noises with constant noise strength. Our algorithm exhibits scaling comparable to previous CS algorithms for fermionic systems with respect to the number of quantum state copies, while also demonstrating enhanced resilience to noise. We numerically demonstrate the performance of our algorithm in the presence of these noise sources. Our results underscore the potential utility of implementing our algorithm on near-term quantum devices.
GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis

We present GNNHLS, an open-source framework to comprehensively evaluate GNN inference acceleration on FPGAs via HLS, containing a software stack for data generation and baseline deployment and FPGA implementations of 6 well-tuned GNN HLS kernels. Evaluating on 4 graph datasets with distinct topologies and scales, the results show that GNNHLS achieves up to 50.8× speedup and 423× energy reduction relative to the CPU baselines. Compared with the GPU baselines, GNNHLS achieves up to 5.16× speedup and 74.5× energy reduction.
Review of blockchain technology in government systems: Applications and impacts in the USA

This study examines blockchain applications in the USA's government systems, analyzing its effectiveness in public administration and governance. Blockchain technology, initially introduced as the underlying infrastructure for cryptocurrencies, has garnered substantial attention for its potential to transform various sectors, including government systems. This paper provides a comprehensive review of the applications and impacts of blockchain technology in the United States' governmental landscape. As governments worldwide seek innovative solutions to enhance transparency, efficiency, and security, blockchain emerges as a disruptive force with the capacity to reshape administrative processes. The study elucidates the fundamental principles of blockchain technology, emphasizing its decentralized and tamper-resistant nature. It also explores diverse applications within government systems, ranging from identity management and voting systems to supply chain traceability and public finance. The analysis delves into specific use cases, detailing how blockchain enhances data integrity, reduces fraud, and streamlines bureaucratic processes. Furthermore, the paper assesses the implications of blockchain adoption for government operations, highlighting potential benefits such as cost reduction, increased accountability, and enhanced citizen trust. Challenges and considerations, including scalability, interoperability, and regulatory frameworks, are scrutinized to provide a balanced understanding of the technology's limitations. The review extends to the United States' federal and state-level initiatives, examining pilot projects and ongoing implementations that leverage blockchain in areas such as land registries, healthcare data management, and procurement. Insights into the collaborative efforts between public and private sectors are explored, shedding light on the collaborative frameworks essential for successful blockchain integration. Ethical considerations and privacy concerns are addressed within the context of blockchain applications in government, emphasizing the need for robust safeguards to protect sensitive information while harnessing the transformative potential of the technology. This paper synthesizes the current state of blockchain technology in the United States' government systems, offering a nuanced understanding of its applications and impacts. As blockchain continues to mature and gain traction, this review contributes valuable insights for policymakers, researchers, and practitioners, fostering a deeper appreciation for the transformative potential of blockchain in shaping the future of government operations.
A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines: From Medical to Remote Sensing

We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zero-sum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications. GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to evaluate GANs. Then we present how GAN and its variants can be applied in twelve domains, ranging from STEM fields, such as astronomy and biology, to business fields, such as marketing and finance, and to arts, such as music. As a result, researchers from other fields may grasp how GANs work and apply them to their own study. To the best of our knowledge, this article provides the most comprehensive survey of GAN’s applications in different field.
Analysis of Artificial Intelligence Technology and Its Application in Improving the Effectiveness of Physical Education Teaching

To promote the construction of public physical education online courses in colleges and universities and the evaluation of the effectiveness of course teaching, this article combines 3D reconstruction techniques in computer vision to construct a set of human body shape reconstruction models and apply them to physical training exercises and teaching effectiveness assessment tasks. Specifically, first, the joint point location information of the human body in the input image is extracted using the human skeleton analysis algorithm, and modeling the foreground and background pose information of the target region using the Pix2Pix image transformation algorithm; second, multi-scale features such as nodal location features, foreground and background features, high-resolution detail features, and low-resolution global features are fused and the extracted multi-scale features are also decoded with the help of pixel-aligned implicit functions to generate a 3D model of the human body representing the human form.
Misalignment-Free Relation Aggregation for Multi-Source-Free Domain Adaptation

In multi-source-free domain adaptation (MSFDA), it is important to effectively fuse latent features from multiple source models to improve adaptation performance on target domain. Existing works weightedly sum source-model features for fusion, which cannot fully leverage the discriminativity of features due to misaligned semantics, and is not applicable to source models with non-identical feature dimensionalities. To mitigate these issues, we propose the idea of misalignment-free relation aggregation (MFRA): instead of directly summing the features, we aggregate the similarity relationships between target samples in each source-model feature space. Specifically, for each source model, we first compute the similarities between the target sample of interest and all the other target samples. The resulting similarities are then summed along the source models to produce the aggregated similarity. To leverage the aggregated similarity in adaptation, a peer-supervised contrastive learning and an adversarial training scheme are designed to transfer discriminative information among models. The method not only effectively preserves discriminativity from each source model after summation, but also is applicable to source models with non-identical feature dimensionalities. The proposed method achieves accuracies higher or comparable to existing MSFDA methods on various cross-domain object recognition tasks. Further studies are also conducted to verify the effectiveness of aggregating inter-sample relationships, as well as the applicability of proposed method under non-identical source-model feature dimensionalities.
Identification of Novel Classes for Improving Few-Shot Object Detection

Conventional training of deep neural networks requires a large number of the annotated image which is a laborious and time-consuming task, particularly for rare objects. Few-shot object detection (FSOD) methods offer a remedy by realizing robust object detection using only a few training samples per class [16], [37], [36], [8], [48], [15]. A challenge for FSOD is that instances from unlabeled novel classes that do not belong to the fixed set of training classes appear in the background. These objects behave similarly to label noise, leading to FSOD performance degradation. We develop a semi-supervised algorithm to detect and then utilize these unlabeled novel objects as positive samples during training to improve FSOD performance. Specifically, we propose a hierarchical ternary classification region proposal network (HTRPN) to localize the potential unlabeled novel objects and assign them new objectness labels. Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception ability of the object detection model for large objects. Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods https://github.com/zshanggu/HTRPN
The Change You Want to See (Now in 3D)

The goal of this paper is to detect what has changed, if anything, between two "in the wild" images of the same 3D scene acquired from different camera positions and at different temporal instances. The open-set nature of this problem, occlusions/dis-occlusions due to the shift in viewpoint, and the lack of suitable training datasets, presents substantial challenges in devising a solution.To address this problem, we contribute a change detection model that is trained entirely on synthetic data and is class-agnostic, yet it is performant out-of-the-box on real world images without requiring fine-tuning. Our solution entails a "register and difference" approach that leverages self-supervised frozen embeddings and feature differences, which allows the model to generalise to a wide variety of scenes and domains. The model is able to operate directly on two RGB images, without requiring access to ground truth camera intrinsics, extrinsics, depth maps, point clouds, or additional before-after images. Finally, we collect and release a new evaluation dataset consisting of real-world image pairs with human-annotated differences and demonstrate the efficacy of our method. The code, datasets and pre-trained model can be found at: https://github.com/ragavsachdeva/CYWS-3D
ECO: Ensembling Context Optimization for Vision-Language Models

Image recognition has recently witnessed a paradigm shift, where vision-language models are now used to perform few-shot classification based on textual prompts. Among these, the CLIP model has shown remarkable capabilities for zero-shot transfer by matching an image and a custom textual prompt in its latent space. This has paved the way for several works that focus on engineering or learning textual contexts for maximizing CLIP’s classification capabilities. In this paper, we follow this trend by learning an ensemble of prompts for image classification. We show that learning diverse and possibly shorter contexts improves considerably and consistently the results rather than relying on a single trainable prompt. In particular, we report better few-shot capabilities with no additional cost at inference time. We demonstrate the capabilities of our approach on 11 different benchmarks.
Developing Robust and Lightweight Adversarial Defenders by Enforcing Orthogonality on Attack-Agnostic Denoising Autoencoders

Adversarial attacks have become a critical threat to the security and reliability of machine learning models. We propose a solution to the problem of defending against adversarial attacks using a deep Denoising Auto Encoder (DAE). The proposed DAE is trained to enforce orthogonality between the noise and the range space of its output in each layer of the encoder’s chain. Furthermore, the pseudoinverse decoder of the DAE is designed to ensure that the reconstructed image and the null space of its intermediate representations in each layer of the chain maintain orthogonality as it progresses from the target space to the latent space. The denoising problem is formulated as an equality constrained optimization problem, which is solved by finding the stationary points of the Lagrangian function. The noisy data are generated by adding realizations of multiple random noise distributions to pristine data during DAE training, resulting in excellent denoising performance. We compare the performance of our full weights and tied-weights DAEs, showing that the latter not only has half the complexity of the former, but also outperforms the former in denoising and in strong adversarial attacks. To demonstrate the effectiveness of the proposed solution we evaluate our networks against recent works in the literature, specifically those focusing on defending against adversarial attacks.
UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer

Text-to-image models (T2I) such as StableDiffusion have been used to generate high quality images of people. However, due to the random nature of the generation process, the person has a different appearance e.g. pose, face, and clothing, despite using the same text prompt. The appearance inconsistency makes T2I unsuitable for pose transfer. We address this by proposing a multimodal diffusion model that accepts text, pose, and visual prompting. Our model is the first unified method to perform all person image tasks-generation, pose transfer, and mask-less edit. We also pioneer using small dimensional 3D body model parameters directly to demonstrate new capability - simultaneous pose and camera view interpolation while maintaining the person’s appearance.
A Comprehensive Framework for Evaluating Deepfake Generators: Dataset, Metrics Performance, and Comparative Analysis

Assessing the realism and accuracy of deepfake generators, especially in cross-reenactment situations, is a major challenge. This challenge is primarily attributed to the absence of ground-truth data, which restricts the application of metrics that rely on explicit ground-truth, such as SSIM and LPIPS. To overcome this challenge, this paper introduces a novel protocol for quantitatively assessing images generated by face-reenactment techniques. To address the scarcity of suitable datasets, two video datasets are generated: the Real Head and the synthesized Metahuman datasets. Furthermore, user studies are conducted to evaluate the efficacy of our proposed protocol. The results demonstrate a strong correlation between subjective evaluations and quantitative metrics obtained within our protocol. Comparative analysis with existing evaluation protocols further validates the effectiveness of our proposed approach. Notably, our protocol exhibits superior performance in analyzing identity preservation, head pose, and facial expression replication. The source code and datasets are made publicly available at https://github.com/SaharHusseini/deepfake_evaluation.git
Learning Cluster Patterns for Abstractive Summarization

Nowadays, pre-trained sequence-to-sequence models such as PEGASUS and BART have shown state-of-the-art results in abstractive summarization. In these models, during fine-tuning, the encoder transforms sentences to context vectors in the latent space and the decoder learns the summary generation task based on the context vectors. In our approach, we consider two clusters of salient and non-salient context vectors, using which the decoder can attend more over salient context vectors for summary generation. For this, we propose a novel cluster generator layer between the encoder and the decoder, which first generates two clusters of salient and non-salient vectors, and then normalizes and shrinks the clusters to make them apart in the latent space. Our experimental results show that the proposed model outperforms the state-of-the-art models such as BART and PEGASUS by learning these distinct cluster patterns, improving up to 2~30% in ROUGE and 0.1~0.8% in BERTScore in CNN/DailyMail and XSUM data sets.
Prediction of Rainfall in North Sumatera Using Machine Learning

Due to the large number of variables involved, weather prediction is one field with a wealth of data but also a higher degree of difficulty. Probabilistic models are used to generate forecasts, which are often not particularly accurate because they have a margin of error. In this article, we report on an exploratory study that looked into using machine learning to predict the rainy season. We employed some of the most significant machine learning methods (multinomial NB, decision tree, random forest, logistic regression, and SGD) in conjunction with a set of data detailing rainfall observations gathered in Northern Sumatera's major cities during the previous ten years. The approach based on logistic regression is the best, according to the data.