Reviewerly/exHarmony huggingface dataset:
Adaptive Feature Analysis in Target Detection and Image Forensics Based on the Dual-Flow Layer CNN Model

With the rapid development of artificial intelligence technology, image editing technology has evolved from relying on software such as Photoshop and GIMP for manual modification to using artificial intelligence technology to achieve intelligent and automated tampering of images. Editing, falsifying, and disseminating digital images have become simple and easy, leading to a crisis of confidence in digital images and reducing their reliability as judicial evidence. Therefore, how to identify falsified images, improve their trustworthiness, and avoid judicial injustice has become a problem that must be overcome in the information age. In this paper, we propose a target detection and adaptive feature analysis in image forensics based on a dual-flow layer CNN model, which can effectively perform image forensics. The results show that our algorithm has a clear theoretical basis, a small operational complexity, and a high detection accuracy.
Graph Kernels Encoding Features of All Subgraphs by Quantum Superposition

Graph kernels are often used in bioinformatics and network applications to measure the similarity between graphs; therefore, they may be used to construct efficient graph classifiers. Many graph kernels have been developed thus far, but to the best of our knowledge there is no existing graph kernel that considers all subgraphs to measure similarity. We propose a novel graph kernel that applies a quantum computer to measure the graph similarity taking all subgraphs into account by fully exploiting the power of quantum superposition to encode every subgraph into a feature. For the construction of the quantum kernel, we develop an efficient protocol that removes the index information of subgraphs encoded in the quantum state. We also prove that the quantum computer requires less query complexity to construct the feature vector than the classical sampler used to approximate the same vector. A detailed numerical simulation of a bioinformatics problem is presented to demonstrate that, in many cases, the proposed quantum kernel achieves better classification accuracy than existing graph kernels.
SemNet: Learning semantic attributes for human activity recognition with deep belief networks

Human Activity Recognition (HAR) is a prominent application in mobile computing and Internet of Things (IoT) that aims to detect human activities based on multimodal sensor signals generated as a result of diverse body movements. Human physical activities are typically composed of simple actions (such as "arm up", "arm down", "arm curl", etc.), referred to as semantic features. Such abstract semantic features, in contrast to high-level activities ("walking", "sitting", etc.) and low-level signals (raw sensor readings), can be developed manually to assist activity recognition. Although effective, this manual approach relies heavily on human domain expertise and is not scalable. In this paper, we address this limitation by proposing a machine learning method, SemNet, based on deep belief networks. SemNet automatically constructs semantic features representative of the axial bodily movements. Experimental results show that SemNet outperforms baseline approaches and is capable of learning features that highly correlate with manually defined semantic attributes. Furthermore, our experiments using a different model, namely deep convolutional LSTM, on household activities illustrate the broader applicability of semantic attribute interpretation to diverse deep neural network approaches. These empirical results not only demonstrate that such a deep learning technique is semantically meaningful and superior to its handcrafted counterpart, but also provides a better understanding of the deep learning methods that are used for Human Activity Recognition.
LiWGAN: A Light Method to Improve the Performance of Generative Adversarial Network

Generative adversarial networks (GANs) gained tremendous growth due to its potential and efficacy in producing realistic samples.This study proposes a light-weight GAN (LiWGAN) to learn the non-image synthesis with minimum computational time for less power computing.Hence, the LiWGAN method enhanced a new skip-layer channel-wise excitation module (SLE) and a self-supervised discriminator design for the non-synthesis performance using the facemask dataset.The facemask is one of the preventative strategies pioneered by the current COVID-19 pandemic.LiWGAN manipulates a nonimage synthesis of facemask that could be beneficial for some researchers to identify an individual using lower power devices, occlusion challenges for face recognition, and alleviate the accuracy challenges due to limited datasets.The performance compared the processing time for a facemask dataset in terms of batch sizes and image resolutions.The Fréchet inception distance (FID) was also measured on the facemask images to evaluate the quality of the augmented image using LiWGAN.The findings for 3000 generated images showed a nearly similar FID score at 220.43 with significantly less processing time per iteration at 1.03s than StyleGAN at 219.97 FID score.One experiment was conducted using the CelebA dataset to compare with GL-GAN and DRAGAN, proving LiWGAN is appropriate for other datasets.The outcomes found LiWGAN performed better than GL-GAN and DRAGAN at 91.31 FID score with 3.50s processing time per iteration.Therefore, LiWGAN could aim to enhance the FID score to be near zero in the future with less processing time by using different datasets.
On Feller continuity and full abstraction

We study the nature of applicative bisimilarity in λ-calculi endowed with operators for sampling from contin- uous distributions. On the one hand, we show that bisimilarity, logical equivalence, and testing equivalence all coincide with contextual equivalence when real numbers can be manipulated through continuous functions only. The key ingredient towards this result is a notion of Feller-continuity for labelled Markov processes, which we believe of independent interest, giving rise a broad class of LMPs for which coinductive and logically inspired equivalences coincide. On the other hand, we show that if no constraint is put on the way real numbers are manipulated, characterizing contextual equivalence turns out to be hard, and most of the aforementioned notions of equivalence are even unsound.
Constraint-based type inference for FreezeML

FreezeML is a new approach to first-class polymorphic type inference that employs term annotations to control when and how polymorphic types are instantiated and generalised. It conservatively extends Hindley-Milner type inference and was first presented as an extension to Algorithm W. More modern type inference techniques such as HM(X) and OutsideIn(X) employ constraints to support features such as type classes, type families, rows, and other extensions. We take the first step towards modernising FreezeML by presenting a constraint-based type inference algorithm. We introduce a new constraint language, inspired by the Pottier/Rémy presentation of HM(X), in order to allow FreezeML type inference problems to be expressed as constraints. We present a deterministic stack machine for solving FreezeML constraints and prove its termination and correctness.
Analyzing binding extent in 3CPS

To date, the most effective approach to compiling strict, higher-order functional languages (such as OCaml, Scheme, and SML) has been to use whole-program techniques to convert the program to a first-order monomorphic representation that can be optimized using traditional compilation techniques. This approach, popularized by MLton, has limitations, however. We are interested in exploring a different approach to compiling such languages, one that preserves the higher-order and polymorphic character of the program throughout optimization. To enable such an approach, we must have effective analyses that both provide precise information about higher-order programs and that scale to larger units of compilation. This paper describes one such analysis for determining the extent of variable bindings. We classify the extent of variables as either register (only one binding instance can be live at any time), stack (the lifetimes of binding instances obey a LIFO order), or heap (binding lifetimes are arbitrary). These extents naturally connect variables to the machine resources required to represent them. We believe that precise information about binding extents will enable efficient management of environments, which is a key problem in the efficient compilation of higher-order programs. At the core of the paper is the 3CPS intermediate representation, which is a factored CPS-based intermediate representation (IR) that statically marks variables to indicate their binding extent. We formally specify the management of this binding structure by means of a small-step operational semantics and define a static analysis that determines the extents of the variables in a program. We evaluate our analysis using a standard suite of SML benchmark programs. Our implementation gets surprisingly high yield and exhibits scalable performance. While this paper uses a CPS-based IR, the algorithm and results are easily transferable to other λ-calculus IRs, such as ANF.
Federated Learning with Channel and Energy Aware Scheduling

In this paper, a federated learning setup in which multiple devices capable of harvesting energy from the environment train a machine learning model based on the intermittent availability of the energy and channel is studied. The main focus is on developing an algorithm that achieves the same convergence as state-of-the-art federated learning methods in a scenario with an error-prone channel and intermittent energy availability. We propose a federated learning algorithm that schedules distributed clients and weighting their local gradients according to the energy and channel profiles of each client. The performance of the proposed algorithm has been demonstrated with the experiments.
Banking Order Classification and Information Extraction

This study presents a system to classify banking orders from customers and to determine the transaction parameters of these orders using machine learning techniques. The presented system uses optical character recognition and shape detection technologies to extract texts and tables from images i.e., scanned email attachments and fax images. Then, in the classification phase, texts are vectorized with the TF-IDF approach after preprocessing and are classified using support vector machines. The orders classified as money transfer are sent to the information extraction module and the parameters of the transaction (sender information, recipient information, amount and description) are determined using named entity recognition methods. Finally, this information is sent directly to an operator&#x2019;s screen for her to check and confirm the parameters and execute the money transfer operation. This system is implemented in a medium-large scale bank in Turkey. This system, which yields high classification and information extraction performance, is expected to save a significant amount of workload for the bank, speed up the order execution process and increase customer satisfaction. The system is currently deployed and being validated online.
Implementation of Single Image Super-Resolution Methods on Ultrasound Images

Today, image quality has become an important factor in many areas. Especially in the field of medical imaging, high-resolution images are needed. In this study, EDSR (Enhanced Deep Residual Networks for Single Image Super-Resolution) and DCSCN (Fast and Accurate Image Super-Resolution by Deep Convolutional Neural Network with Skip Connection and Network in Network) methods were trained using the ultrasound dataset and the results were compared, considering the need for high resolution images in the medical field.
Solar Irradiance forecasting using Recurrent Neural Networks

Solar irradiance being the chief constituent of the solar power extraction is dominated by the atmospheric conditions. Prediction of irradiance data is highly sought after in the field of forecasting and predictive maintenance. For this purpose various machine learning methods are being used to improve the accuracy of the forecasted value. This paper aims at prediction of solar irradiance using Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) architecture. Using different combinations of input in the supervised learning method the accuracy for single as well as multiple time steps are determined. The results are shown in the form of evaluation metric as well as the forecasted values and actual value comparison. It is seen that for single time step prediction the LSTM RNN puts out highly accurate values but error for higher time steps prediction accumulates in a compounded manner. It is also observed that using time based models along with the inputs increases the accuracy of the forecasted values.
Indian Classical Dance Forms Classification Using Transfer Learning

Human activity analysis is useful in a variety of domains, including video surveillance, biometrics, and home health monitoring systems. In computer vision field, extraction and recognition of complex human movements from images/videos are a great complex task. In this present work, we propose the Indian classical dances (ICD) classification using the concept of transfer learning. ICD form is a combination of gesticulation of all body parts. It comes in a variety of shapes and sizes, but the most common features include single/double hand mudras, eye movement, legs alignment, hip movements, facial expressions, and legs posture. Each dance has its own gestures and clothes worn by the dancers. India classical dances are categorized into 8 categories. In this work, we used the dataset consists of eight dance classes includes Bharatnatyam, odissi, manipuri, kuchipudi, mohiniyattam, sattriya, kathakali, and kathak. Those images were collected from Internet. While image processing using CNN model training with less data does not give accurate result that leads to over-fitting problem. To overcome this problem, we propose a concept, transfer learning by this use the knowledge that was learned from some problem that can be applied to solve the problem related to the target task. It reduces both time and space complexity. In our proposed work, we use a pre-trained model VGG16. It results high accuracy of 85.4% compared to earlier methods.
Object Detection and Tracking Using DeepSORT

The rapid development of algorithms for image detection has resulted in broad safety applications, for example, face recognition and monitoring. However, monitoring in real-time is quite difficult, particularly in busy areas, where the individual may be partially or fully concealed for a period of time. Therefore, this chapter aims to build the object tracking system with the DeepSORT architecture for crowd surveillance. This system not only detects a human being in real time but also uses the information it has learnt, in contrast to the object detection frameworks like CNN, to monitor a human's path until they leave the frame. Using the DeepSORT framework, the trajectory of an item may either be detected in real time or even in a background based on an existing record. This framework will be trained using a huge dataset to track people's motion depending on the individual's speed, distance and fitness. The DeepSORT algorithm, the top algorithm in object identification and tracking, is very powerful and quick. Many issues have been identified with the security mechanism for object tracking. Occlusion is one of the main problems in the pursuit of objects. This problem occurs in a busy location with another object moving extremely quickly into and out of the picture (i.e., a bustling Zebra crossing). If an item is present inside a frame, the trajectory of an individual may be incorrectly identified.
Unpacking content moderation: The rise of social media platforms as online civil courts

Social media platforms create and amplify conflicts but fail to ensure adequate access to justice through content moderation when harms arise. This chapter focuses on a gap in current scholarship on platform governance, by addressing content moderation from the procedural perspective of dispute resolution. We trace the emergence of content moderation as a form of digital dispute resolution, proposing a theoretical framework for the understanding of social media platforms as private adjudicators, and illustrating how platforms have progressively embraced this role. This framework is further complemented by an empirical overview of the content reporting mechanisms of four social media platforms (Facebook, TikTok, Twitch and Twitter). Subsequently, we explore the current shortcomings of content moderation, pointing out three problems which currently affect the transparency and legitimacy of the dispute resolution activities carried out by platforms through content moderation. How can these shortcomings be corrected? The chapter identifies two complementary avenues, through which legitimacy and due process guarantees could potentially be injected into content moderation. On the one hand, social media platforms themselves could ‚Äòjudicialize' themselves, setting up structures and procedures that enhance the legitimacy of content moderation, and afford sufficient procedural rights to those affected by content moderation decisions. From this point of view, we analyse Facebook's recent decision to institute a quasi-judicial body, the Oversight Board, to deal with delicate and significant content moderation disputes. On the other hand, legitimacy could be enhanced "from without", by way of regulation. In order to explore this avenue, we conclude the chapter with some normative reflections relating to the possible future regulation of content moderation, with specific reference to the European Union and the proposal for a Digital Services Act.
The Financial Stability Implications of Digital Assets

The value of assets in the digital ecosystem has grown rapidly, amid periods of high volatility. Does the digital financial system create new potential challenges to financial stability? This paper explores this question using the Federal Reserve’s framework for analyzing vulnerabilities in the traditional financial system. The digital asset ecosystem has recently proven itself highly fragile. However adverse digital asset markets shocks have had limited spillovers to the traditional financial system. Currently, the digital asset ecosystem does not provide significant financial services outside the ecosystem, and it exhibits limited interconnections with the traditional financial system. The paper describes emerging vulnerabilities that could present risks to financial stability in the future if the digital asset ecosystem becomes more systemic, including: run risks among large stablecoins, valuation pressures in crypto-assets, fragilities of DeFi platforms, growing interconnectedness, and a general lack of regulation.
Quantitative methods students’ perception during a pandemic: e-learning support and course satisfaction

The pandemic caused by COVID-19 removed the resistance to the digitalization of Higher Education (HE), which allowed the remaking of universities from innovation, flexibility, and hybrid and inclusive educational models. This research aimed to study the perception of e-learning support and students’ satisfaction with Quantitative Methods subjects. For this purpose, we applied an online survey with 315 participants, through the second semester of 2021, from the University Center for Economic and Administrative Sciences (CUCEA) at the University of Guadalajara. Using exploratory factor analysis, we studied the survey responses. Three categories of support were identified: teacher support, peer support, and technical support. The empirical evidence showed that the support received is significantly related to student satisfaction, especially from their professor during e-learning through a pandemic. Likewise, many students considered that higher participation should have been during their online classes. In contrast, others believed greater involvement of their classmates could have improved the class dynamics. The results found are helpful for planners and decision-makers in HE because the educational model cannot go back to how it was before the coronavirus pandemic.
NeuroBlu: a natural language processing (NLP) electronic health record (EHR) data analytic tool to generate real-world evidence in mental healthcare

Introduction EHRs contain a rich source of real-world data that can support evidence generation to better understand mental disorders and improve treatment outcomes. However, EHR datasets are complex and include unstructured free text data that are time consuming to manually review and analyse. We present NeuroBlu, a secure, cloud-based analytic tool that includes bespoke NLP software to enable users to analyse large volumes of EHR data to generate real-world evidence in mental healthcare. Objectives (i) To assemble a large mental health EHR dataset in a secure, cloud-based environment. (ii) To apply NLP software to extract data on clinical features as part of the Mental State Examination (MSE). (iii) To analyse the distribution of NLP-derived MSE features by psychiatric diagnosis. Methods EHR data from 25 U.S. mental healthcare providers were de-identified and transformed into a common data model. NLP models were developed to extract 241 MSE features using a deep learning, long short-term memory (LSTM) approach. The NeuroBlu tool ( https://www.neuroblu.ai/ ) was used to analyse the associations of MSE features in 543,849 patients. Results The figure below illustrates the percentage of patients in each diagnostic category with at least one recorded MSE feature. Conclusions Delusions and hallucinations were more likely to be recorded in people with schizophrenia and schizoaffective disorder, and cognitive features were more likely to be recorded in people with dementia. However, mood symptoms were frequently recorded across all diagnoses illustrating their importance as a transdiagnostic clinical feature. NLP-derived clinical information could enhance the potential of EHR data to generate real-world evidence in mental healthcare. Disclosure This study was funded in full by Holmusk.
Properly Learning Decision Trees in almost Polynomial Time

We give an n O (log log n ) -time membership query algorithm for properly and agnostically learning decision trees under the uniform distribution over { ± 1} n . Even in the realizable setting, the previous fastest runtime was n O (log n ) , a consequence of a classic algorithm of Ehrenfeucht and Haussler. Our algorithm shares similarities with practical heuristics for learning decision trees, which we augment with additional ideas to circumvent known lower bounds against these heuristics. To analyze our algorithm, we prove a new structural result for decision trees that strengthens a theorem of O’Donnell, Saks, Schramm, and Servedio. While the OSSS theorem says that every decision tree has an influential variable, we show how every decision tree can be “pruned” so that every variable in the resulting tree is influential.