Reviewerly/exHarmony huggingface dataset:
Artificial neural network approach for monthly air temperature estimations and maps

Accurate air temperature estimation plays a crucial role in weather planning, and hydrological modeling techniques including water supply, drought analysis, and demand problems of a country. In this study, monthly maximum air temperature (Tmax), minimum air temperature (Tmin), and mean air temperature (T) values for Türkiye were modeled using the Feed-forward neural network (FNN) and Elman neural network (ENN) approaches. In the developed models, monthly Tmax, Tmin, and T values at 81 measurement stations over the period of 1927 through 2021 recorded by the Turkish State Meteorological Service (known as MGM in Türkiye) were used. While the periodicity (month of the year) and geographical variables (latitude, longitude, and altitude) were used as inputs in the developed models, the Tmax, Tmin, and T variables were defined as the output. First of all, long-term monthly air temperature maps were created with the help of the ArcGIS software program using the observed values. Then, maps of long-term air temperatures were created with the ArcGIS software program using the predicted values of the developed artificial neural network models. To prove the accuracy of the models, the predicted long-term air temperature maps were compared with those actual maps. The obtained results showed that the long-term monthly air temperature maps of the studied regions could be created with a small and acceptable error as a function of geographical information and periodicity component. By creating artificial intelligence maps in this way, it will be possible to accurately predict the long-term monthly air temperatures as a function of latitude, longitude, altitude, and the number of months at any point where no measurement is made.
Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models

Large deep learning models have shown great potential with state-of-the-art results in many tasks. However, running these large models is quite challenging on an accelerator (GPU or TPU) because the on-device memory is too limited for the size of these models. Intra-layer model parallelism is an approach to address the issues by partitioning individual layers or operators across multiple devices in a distributed accelerator cluster. But, the data communications generated by intra-layer model parallelism can contribute to a significant proportion of the overall execution time and severely hurt the computational efficiency.
Glign: Taming Misaligned Graph Traversals in Concurrent Graph Processing

In concurrent graph processing, different queries are evaluated on the same graph simultaneously, sharing the graph accesses via the memory hierarchy. However, different queries may traverse the graph differently, especially for those starting from different source vertices. When these graph traversals are ”misaligned”, the benefits of graph access sharing can be seriously compromised. As more concurrent queries are added to the evaluation batch, the issue tends to become even worse.
Violence 4D: Violence detection in surveillance using 4D convolutional neural networks

Abstract As violence has increased around the world, surveillance cameras are everywhere, and they are only going to get more ubiquitous. Due to the massive volume of video footage, automatic activity detection systems must be used to create an online warning in the event of aberrant activity. A deep learning architecture is presented in this study using four‐dimensional video‐level convolution neural networks. The proposed architecture includes residual blocks that are used with three‐Dimensional Convolution Neural Networks 3D (CNNs) to learn long‐term and short‐term spatiotemporal representation from the video as well as record inter‐clip interaction. ResNet50 is used as the backbone for three‐dimensional convolution networks and dense optical flow for the region of interest. The proposed architecture is applied on four benchmarks for violence and non‐violence videos, which are commonly used for violent detection. It obtained test accuracies of 94.67% on RWF2000, 97.29% on Crowd violence, 100% on Movie fight and 100% on the Hockey Fight dataset. These results outperform the previous methods used on RWF2000 datasets.
Green and Pleasant Lands: The Affective and Cerebral Hemodynamic Effects of Presence in Virtual Environments During Exercise

Pleasant exercise experiences increase the likelihood of exercise adherence, and innovative strategies to promote consistently pleasant exercise experiences are needed. In this study we compared a novel nature-based virtual reality environment, a nature-based 360° video, and a control condition to test the hypothesis that greater presence in virtual space would promote positive affective experiences during exercise. Moreover, we assessed prefrontal cerebral hemodynamics using near infrared spectroscopy to explore possible neural underpinnings of dissociative strategies during exercise. Twelve participants (M = 26.2, SD = 7.7 years; M BMI = 25.5, SD = 5.2 kg/m2) completed a maximal aerobic test and three exercise conditions (Control, Virtual Reality [VR], and 360° video). The two experimental conditions differed in terms of the participants' sense of presence (VR eliciting greatest presence), and all conditions utilized similar exercise intensity. The VR condition setting was a virtual mountain forest trail, and the 360° video was of a forest road. The 360° video was perceived as the most distracting (p = .023, d = 1.07), pleasant (p = .007, d = .75), and enjoyable (p = .029; d = .82) condition. ΔHbDiff data indicated that the control condition caused the greatest prefrontal brain activation (p = .008, d = .84). Presence was not a salient factor in distracting participants from bodily sensations during exercise, but immersion in a stimulus was. These results provide support for using head-mounted displays during exercise as a strategy to increase pleasure, with practical implications for practitioners, researchers, and individuals.
Design, realization, and user evaluation of the ARCA system for exploring a digital library

This paper presents ARCA, a software system that enables semantic search and exploration over a book catalog. The main purpose of this work is twofold: to propose a general paradigm for a semantic enrichment workflow and to evaluate a visual approach to information retrieval based on extracted information and existing knowledge graphs. ARCA has been designed and implemented following a user-centered design approach. Two different releases of the system have incrementally and iteratively developed and evaluated. The first release has evaluated the quality and usefulness of the extracted data. The second release, whose design was a refinement based on the previous evaluation results, was assessed by several users. Moreover, a comparative test with other information retrieval systems was conducted in order to study the potential added-value of the system. ARCA is employed in a real editorial scenario to visually search and explore the books of a publishing house.
Online course recommendation algorithm based on multilevel fusion of user features and item features

Abstract To meet the demand for accurate recommendation and personalized learning in online education, an online course recommendation algorithm with multilevel fusion of user features and item features is proposed for content‐based recommendation systems applied to online education courses that have weak generalization ability and cannot cope well with data sparsity. The algorithm is improved on the deep learning recommendation algorithm named Wide &amp; Deep Learning (WDL), which is a CTR (Click‐through rate prediction) prediction algorithm. MMF (Multi‐level Fusion Feature) uses collaborative filtering to replace linear methods in the wide part of WDL, introduces feature interactions to model user representations and item representations separately, and uses ResNet (Residual Network) ideas to improve deep neural network (DNN) in the deep part to reduce the performance degradation caused by overfitting. The experimental validation was conducted on the online education data set and the public data set MovieLens‐1M, and the AUC was improved by 1.21% and 1.46%, respectively. Meanwhile, the effect brought by this improved algorithm is interpretable.
An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network

The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer-Convolution fusion dehazing network (TCFDN), which uses Transformer's global modeling ability and convolutional neural network's local modeling ability to improve the dehazing ability. In the Transformer-Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer-Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer-Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.
Three-stage image forgery localization with shallow feature enhancement and attention

With the development of technology, it is becoming easier and easier for people to generate tampered images that are indistinguishable from the human eye, and the malicious use of these photos in news, academic papers, and criminal crimes has brought great harm to society. This paper proposes a deep convolutional neural network-based image forgery localization method to uncover the subtle differences between doctored images and real images. Specifically, the network achieves tampering localization through a three-stage enhancement scheme. First, the dilated convolution in the deep layer of the network is used to keep the feature map resolution constant, and the number of shallow convolutions is decreased to reduce the perceptual field, so the network focuses on local regions. Second, the feature enhancement module is used to fuse shallow features with deep features to effectively filter content information and highlight tampering features, making full use of local and global information to improve the generalization ability. Finally, the attention enhancement module reweights the convolutional feature maps in terms of channels and locations, respectively, to highlight the information regions around the forgery boundaries, thus guiding the network to capture more intrinsic features for image forgery. Extensive experimental results on several public datasets show that this method outperforms other state-of-the-art methods in image forgery localization.
A study of bias mitigation strategies for speaker recognition

Speaker recognition is increasingly used in several everyday applications including smart speakers, customer care centers and other speech-driven analytics. It is crucial to accurately evaluate and mitigate biases present in machine learning (ML) based speech technologies, such as speaker recognition, to ensure their inclusive adoption. ML fairness studies with respect to various demographic factors in modern speaker recognition systems are lagging compared to other human-centered applications such as face recognition. Existing studies on fairness in speaker recognition systems are largely limited to evaluating biases at specific operating points of the systems, which can lead to false expectations of fairness. Moreover, there are only a handful of bias mitigation strategies developed for speaker recognition systems. In this paper, we systematically evaluate the biases present in speaker recognition systems with respect to gender across a range of system operating points. We also propose adversarial and multi-task learning techniques to improve the fairness of these systems. We show through quantitative and qualitative evaluations that the proposed methods improve the fairness of ASV systems over baseline methods trained using data balancing techniques. We also present a fairness-utility trade-off analysis to jointly examine fairness and the overall system performance. We show that although systems trained using adversarial techniques improve fairness, they are prone to reduced utility. On the other hand, multi-task methods can improve the fairness while retaining the utility. These findings can inform the choice of bias mitigation strategies in the field of speaker recognition.
Learning deep texture-structure decomposition for low-light image restoration and enhancement

A great many low-light image restoration methods have built their models according to Retinex theory. However, most of these methods cannot well achieve image detail enhancement. To achieve simultaneous restoration and enhancement, we study deep low-light image enhancement from a perspective of texture-structure decomposition, that is, learning image smoothing operator. Specifically, we design a low-light restoration and enhancement framework, in which a Deep Texture-Structure Decomposition (DTSD) network is introduced to estimate two complementary constituents: Fine-Texture (FT) and Prominent-Structure (PS) maps from low-light image. Since these two maps are leveraged to approximate FT and PS maps obtained from normal-light image, they can be combined as the restored image in a manner of pixel-wise addition. The DTSD network has three parts: U-attention block, Decomposition-Merger (DM) block, and Upsampling-Reconstruction (UR) block. To better explore multi-level informative features at different scales than U-Net, U-attention block is designed with intra group and inter group attentions. In the DM block, we extract high-frequency and low-frequency features in low-resolution space. After obtaining informative feature maps from these two blocks, these maps are fed into the UR block for the final prediction. Numerous experimental results have demonstrated that the proposed method can achieve simultaneous low-light image restoration and enhancement, and it has superior performance against many state-of-the-art approaches in terms of several objective and perceptual metrics.
High-speed and energy-efficient crypto-processor for post-quantum cryptography CRYSTALS-Kyber

In recent years, public-key cryptography has already been integrated into everyday life, as the majority of Web communications are now encrypted. Although data center traffic increased tenfold between 2010 and 2018, the increase in overall data center power consumption has been largely controlled by the lower power consumption of equipment and more efficient operations, contributing to the development of the Web [1]. Moreover, in recent years, most Web communications have become encrypted. It is increasingly important to perform cryptographic computations at high speed and low energy consumption.
An efficient and revocable attribute-based data sharing scheme with rich expression and escrow freedom

Attribute-based encryption effectively realizes fine-grained access control by using attributes. It provides an ideal access control strategy for new computing environments with large-scale, strong dynamics and strong privacy characteristics, such as cloud computing and the Internet of things. In this paper, we propose an efficient and provably secure cloud data sharing scheme (ABEDS-RR) using ciphertext policy attributed-based encryption. The ABEDS-RR scheme needs a semi-trusted proxy party to transform part of the ciphertext with a conversion key. Compared with most existing schemes, when changing the attributes of a user, only the private key and conversion key of the user need to be modified. In order to better adapt to occasions with limited computing capacity, such as ad hoc network, smart sensor, mobile device, the scheme can be extended to the case of outsourcing decryption. In addition, the ABEDS-RR scheme overcomes key escrow issues by co-generating user keys between two entities, and enriches the expression ability of attributes by introducing weight attributes.
Reducing the systematic error of DIC using gradient filtering

The inverse compositional Gauss-Newton (IC-GN) DIC algorithm is now the most popular DIC algorithm. The error analysis of the algorithm is necessary. However, the effect of the gradient operator on the error cannot be systematically analysed due to the different dimensions of the gradient operator. In this paper, the 1-D and 2-D gradient operators are incorporated into the same framework by decomposing the gradient operator into two parts: gradient acquisition and gradient filtering. Based on the above analysis, a DIC method based on gradient filtering is constructed and the simulation analysis results show that the systematic error is reduced to less than 10% of the original ICGN-DIC algorithm, enhancing the robustness to noise variations. Finally, validation is performed using an open-source dataset. It is demonstrated that the proposed method can reduce the system error to less than 15%.
An improved GEV boosting method for imbalanced data classification with application to short-term rainfall prediction

This paper considers the imbalanced binary classification problem by focusing on the application of the short-term rainfall forecasting in arid and semi-arid regions. Specifically, we present a novel boosting-type method by utilizing the generalized extreme value (GEV) distribution as the link function and applying a gradient tree boosting algorithm to capture complex interactions among covariates. The proposed method has several appealing advantages such as, it can identify rare rainfall events as well as quantifying the uncertainties; it is data-driven that without any assumption on the relationship between the covariates and the rainfall event; the fitted model is highly interpretable, making it a useful tool for studying the rainfall mechanisms in arid and semi-arid regions. Experiments on two real-world datasets show that our approach outperforms its competing methods.
Complex self-driving behaviors emerging from affordance competition in layered control architectures

The deployment of autonomous driving technology is hindered by "corner cases": unusual nuanced conditions that the self-driving software cannot understand and act fully. We argue that some corner cases originate from a "narrow AI" approach, which lacks the general knowledge that humans exploit when dealing with these cases. We propose an alternative that can be seen as a step toward features of Artificial General Intelligence. We exploit the biological principle of affordance competition in layered control architectures to create an artificial agent that realizes emergent, adaptive, and logical behaviors without programming case-specific rules or algorithms. We give six different examples of simple and complex emergent behaviors. For the case study of merge scenarios, we contrast the approach of this paper with an algorithmic solution of the literature. The ideas presented here (if not the whole agent's sensorimotor organization) could be used to improve the robustness and flexibility of self-driving technology.
A systematic literature review on the use of machine learning in code clone research

Research related to code clones includes detection of clones in software systems, analysis, visualization and management of clones. Detection of semantic clones and management of clones have attracted use of machine learning techniques in code clone related research. The aim of this study is to report the extent of machine learning usage in code clone related research areas. The paper uses a systematic review method to report the use of machine learning in research related to code clones. The study considers a comprehensive set of 57 articles published in leading conferences, workshops and journals. Code clone related research using machine learning techniques is classified into different categories. Machine learning and deep learning algorithms used in the code clone research are reported. The datasets, features used to train machine learning models and metrics used to evaluate machine learning algorithms are reported. The comparative results of various machine learning algorithms presented in primary studies are reported. The research will help to identify the status of using machine learning in different code clone related research areas. We identify the need of more empirical studies to assess the benefits of machine learning in code clone research and give recommendations for future research.
A hand motion capture method based on infrared thermography for measuring fine motor skills in biomedicine

Many biomedical applications require fine motor skill assessments; however, real-time and contactless fine motor skill assessments are not typically implemented. In this study, we followed the 2D-to-3D pipeline principle and proposed a transformer-based spatial-temporal network to accurately regress 3D hand joint locations by inputting infrared thermal video for eliminating need of multiple cameras or RGB-D devices. We also developed a dataset composed of infrared thermal videos and ground truth annotations for training. The label represents a set of 3D joint locations from infrared optical trackers, which is considered the gold standard for clinical applications. To demonstrate their potential, the proposed method was used to measure the finger motion angle, and we investigated its accuracy by comparing the proposal with the Azure Kinect system and Leap Motion system. On the proposed dataset, the proposed method achieved a 3D hand pose mean error of less than 14 mm and outperforms the other deep learning methods. When the error thresholds were larger than approximately 35 mm, our method first to achieved excellent performance (>80%) in terms of the fraction of good frames. For the finger motion angle calculation task, the proposed and commercial systems had comparable inter-system reliability (ICC2,1 ranging from 0.81 to 0.83) and excellent validity (Pearson's r-values ranging from 0.82 to 0.86). We believe that the proposed approaches can capture hand motion and measure finger motion angles and can be used in different biomedicine scenarios as an effective evaluation tool for fine motor skills.