Reviewerly/exHarmony huggingface dataset:
The Role of Blockchain, AI and IoT for Smart Road Traffic Management System

Nowadays vehicles are increasing on the road. Due to this, it is a challenge for society to manage traffic jams and road accidents all over the world. Artificial Intelligence (AI) such as Machine Learning (ML) algorithms are very helpful to improve the performance of the overall road safety management system. AI is used for many real-world applications to make any system be a smart system. The Smart Road Traffic Management System (SRTMS) easily recognizes the influence occurs for random changes on road safety. The SRTMS detects the unsafe driving patterns as well as convey the information to the respective authorities. The Internet of Things (IoT) is a boon technology to observe human activities in real-time. IoT devices or nodes are composed of sensors that are commonly utilized to identify and reply to electrical and other signals. Currently, Blockchain (BC) is the most trending technology to automate transactions, which means sharing or exchange of information between the IoT devices or nodes. BC technology facilitates for sharing of information on the network is decentralized, secure, persistent, anonymity, suitability and trustworthy manner. With consensus algorithms and smart contracts, Blockchain holds to manage communication among nodes without the involvement of a third-party or intermediary body. Simultaneously, AI has the ability to offer intelligent and decision-making machines similar to human beings' minds. This paper proposes the SRTMS model for solving the road accident, traffic jam and disseminate the information to all stakeholders. This proposed model is a combination of most trending technologies such as AI, BC, and IoT. This paper proposes the SRTMS model for solving the road accident, traffic jam and disseminate the information to all stakeholders. This proposed model is a combination of most trending technologies such as AI, BC, and IoT.
ATFaceGAN: Single Face Semantic Aware Image Restoration and Recognition From Atmospheric Turbulence

Degradation due to atmospheric turbulence is common while capturing images at long ranges. To mitigate the degradation due to turbulence which includes deformation and blur, we propose a generative single frame restoration algorithm which disentangles the blur and deformation due to turbulence and reconstructs a restored image. The disentanglement is achieved by decomposing the distortion due to turbulence into blur and deformation components using a deblur generator and a deformation correction generator, respectively. Two paths of restoration are implemented to regularize the disentanglement and generate two restored images from a given degraded image. A fusion function combines the features of the restored images to reconstruct a sharp image with rich details. Adversarial and perceptual losses are added to reconstruct a sharp image and suppress the artifacts respectively. Extensive experiments demonstrate the effectiveness of the proposed restoration algorithm, which achieves satisfactory performance for face restoration and face recognition tasks.
A Dynamic Ensemble Learning Algorithm based on K-means for ICU mortality prediction

This research proposes a Dynamic Ensemble Learning Algorithm based on K-means (DELAK) for intensive care unit (ICU) mortality prediction. Nowadays, the widely applied traditional scoring systems, which predict the mortality risk with some scores reflecting the severity of disease and physiological states of patients in ICU, have shown insufficient predictive performance when faced with large volume of data. Although taking advantage of large volume of data, single machine learning model and ensemble learning methods show an inadequate ability to make personalized predictions for each new patient. Dynamic ensemble selection (DES) methods which are widely studied in pattern recognition field can make personalized prediction but they only select a single classifier instead of ensemble of the results from a pool of classifiers. To overcome the limitations mentioned above, this research proposes an ensemble learning algorithm based on K-means sampling and distance-based dynamic ensemble. K-means sampling helps to achieve the diversity of base classifiers and the distance-based dynamic ensemble is a flexible fusion method which creates a personalized combination of results from base classifiers for each new test sample. To evaluate the performance for mortality prediction of the proposed algorithm, comprehensive experiments are conducted on MIMIC-III dataset. The experimental results show that DELAK achieves outstanding performance in term of AUROC and AUPRC compared with other six fusion strategies, traditional scoring systems, classical ensemble models, i.e. AdaBoost, Bagging and random forest, and dynamic ensemble selection methods in most mortality prediction tasks.
PIPO: A Lightweight Block Cipher with Efficient Higher-Order Masking Software Implementations

In this paper, we introduce a new lightweight 64-bit block cipher PIPO (PIPO stands for "Plug-In" and "Plug-Out", representing its use in side-channel protected and unprotected environments, respectively.) supporting a 128 or 256-bit key. It is a byte-oriented and bitsliced cipher that offers excellent performance in 8-bit AVR software implementations. In particular, PIPO allows for efficient higher-order masking implementations, since it uses a minimal number of nonlinear operations. Our implementations demonstrate that PIPO outperforms existing block ciphers (for the same block and key lengths) in both side-channel protected and unprotected environments, on an 8-bit AVR. Furthermore, PIPO records competitive round-based hardware implementations. For the nonlinear layer of PIPO, we have developed a new lightweight 8-bit S-box that provides an efficient bitsliced implementation including only 11 nonlinear bitwise operations. Furthermore, its differential and linear branch numbers are both 3. This characteristic enables PIPO to thwart differential and linear attacks with fewer rounds. The security of PIPO has been scrutinized with regards to state-of-the-art cryptanalysis.
Hand Gesture Recognition Based on Optimal Segmentation in Human-Computer Interaction

In recent years, hand gesture recognition (HGR) systems have been extensively developed with the technologies of human-computer interaction (HCI), which enables regular interaction with machines. However, the significance of progress in HGR continues to advance, although the problem of hand segmentation and recognition is challenging due to the unfavorable environment, background illumination, hand size, and shape. To overcome this, we propose an optimal segmentation method for identifying hand gestures from input images, improving recognition performance. For segmenting hand gestures, we compared the segmentation methods of YCbCr, SkinMask, and HSV (hue, saturation, and value). The CR component is extracted from YCbCr, then binarization, erosion, and hole filling are performed. Color segmentation is applied to the SkinMask process that detects pixels that match the color of the hand. In the HSV process, threshold masking determines the dominant features. The Softmax classification is used to classify hand gestures where features are extracted through convolutional neural network (CNN). The proposed segmentation methods are applied to a benchmark dataset and the result shows an improvement in recognition accuracy over state-of-the-art systems.
Multi-service model for blockchain networks

Multi-service networks aim to efficiently supply distinct goods within the same infrastructure by relying on a (typically centralised) authority to manage and coordinate their differential delivery at specific prices. In turn, final customers constantly seek to lower costs whilst maximising quality and reliability. This paper proposes a decentralised business model for multi-service networks using Ethereum blockchain features – gas, transactions, and smart contracts – to execute multiple services at different prices. By employing the Ethereum cryptocurrency token, Ether, to quantify the quality of service and reliability of distinct private Ethereum networks, our model concurrently processes streams of services at different gas prices while differentially delivering reliability and service quality. This multi-service business model has been extensively tested on five concurrent Ethereum networks with various combinations of gas prices, miners, and regular nodes using a Proof of Authority consensus algorithm and throughput as the evaluation metric. It has exhibited linear scalability, providing increased throughput in high-quality Ethereum networks, i.e., composed of more validator nodes. The results also indicate that different mining prices do not impact the network performance, but networks with more miners had limited scalability and an increased level of trustworthiness and reliability.
From Conceptualization to Implementation: FAIR Assessment of Research Data Objects

Funders and policy makers have strongly recommended the uptake of the FAIR principles in scientific data management. Several initiatives are working on the implementation of the principles and standardized applications to systematically evaluate data FAIRness. This paper presents practical solutions, namely metrics and tools, developed by the FAIRsFAIR project to pilot the FAIR assessment of research data objects in trustworthy data repositories. The metrics are mainly built on the indicators developed by the RDA FAIR Data Maturity Model Working Group. The tools’ design and evaluation followed an iterative process. We present two applications of the metrics: an awareness-raising self-assessment tool and an automated FAIR data assessment tool. Initial results of testing the tools with researchers and data repositories are discussed, and future improvements suggested including the next steps to enable FAIR data assessment in the broader research data ecosystem.
Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.
Exploring user movie interest space: A deep learning based dynamic recommendation model

Exploring user interest behind massive user behaviors is essential for online recommendations. Although recommendation models have been proposed recently with great success, existing studies ignore not only the timeliness of online users' behaviors in terms of their interest, but also the sequential characteristics of their behaviors. To overcome this limitation, we construct a User Movie Interest Space (UMIS) model based on the sequential ratings of users. We define three indexes to elucidate the features of the interest of users for UMIS, which describe different patterns of behaviors of users related to their interests. Based on UMIS we propose a deep learning model named Dynamic Interest Flow (DIF) to provide dynamic movie recommendations. The DIF model achieves intelligently multi-dimensional observations on a user's interest space and to predict simultaneously a variety of their future interests. Experimental results indicate that DIF outperforms traditional rating-based models and other state-of-the-art deep learning models. Results also demonstrate that modeling a dynamic recommendation as a sequential prediction is supposed to obtain outstanding advantages.
A Theory of Robust API Knowledge

Creating modern software inevitably requires using application programming interfaces (APIs). While software developers can sometimes use APIs by simply copying and pasting code examples, a lack of robust knowledge of how an API works can lead to defects, complicate software maintenance, and limit what someone can express with an API. Prior work has uncovered the many ways that API documentation fails to be helpful, though rarely describes precisely why. We present a theory of robust API knowledge that attempts to explain why, arguing that effective understanding and use of APIs depends on three components of knowledge: (1) the domain concepts the API models along with terminology, (2) the usage patterns of APIs along with rationale, and (3) facts about an API’s execution to support reasoning about its runtime behavior. We derive five hypotheses from this theory and present a study to test them. Our study investigated the effect of having access to these components of knowledge, finding that while learners requested these three components of knowledge when they were not available, whether the knowledge helped the learner use or understand the API depended on the tasks and likely the relevance and quality of the specific information provided. The theory and our evidence in support of its claims have implications for what content API documentation, tutorials, and instruction should contain and the importance of giving the right information at the right time, as well as what information API tools should compute, and even how APIs should be designed. Future work is necessary to both further test and refine the theory, as well as exploit its ideas for better instructional design.
Endpoint Communication Contention-Aware Cloud Workflow Scheduling

Cloud platforms have recently become a popular target execution environment for numerous workflow applications. Hence, effective workflow scheduling strategies in cloud environments are in high demand. However, existing scheduling algorithms are grounded on an idealized target platform model where virtual machines are fully connected, and all communications can be performed concurrently. A significant aspect neglected by them is endpoint communication contention when executing workflows, which has a large impact on workflow makespan. This article investigates how to incorporate contention awareness into cloud workflow scheduling and proposes a new practical scheduling model. Endpoint communication contention-aware List Scheduling Heuristic (ELSH) is designed to minimize workflow makespan. It uses a novel task ranking property and schedules data communications to communication resources besides scheduling tasks to computing resources. Moreover, a rescheduling technique is employed to improve the schedule. In experiments, ELSH is evaluated against the traditional contention-oblivious list scheduling algorithm, which is adapted to address contention during execution in practice. The experimental results reveal that ELSH performs more efficaciously compared with the adapted traditional ones. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article aims to advance the state of the art for workflow scheduling in clouds by taking into account endpoint communication contention that can occur in practice but has largely been neglected in existing investigations. A scheduling method called Endpoint communication contention-aware List Scheduling Heuristic (ELSH) is then proposed to optimize workflow makespan. Experimental results based on synthetic and realistic workflows show that ELSH performs better than traditional scheduling algorithms that fail to consider endpoint communication contention, especially for the workflow with a large communication-to-computation-cost ratio. The proposed approach can be readily put into use and help cloud service providers to offer their customers high-quality services when executing the latter’s workflows.
Robotic Instrument Segmentation With Image-to-Image Translation

The semantic segmentation of robotic surgery video and the delineation of robotic instruments are important for enabling automation. Despite major recent progresses, the majority of the latest deep learning models for instrument detection and segmentation rely on large datasets with ground truth labels. While demonstrating the capability, reliance on large labelled data is a problem for practical applications because systems would need to be re-trained on domain variations such as procedure type or instrument sets. In this letter, we propose to alleviate this problem by training deep learning models on datasets that are synthesised using image-to-image translation techniques and we investigate different methods to perform this process optimally. Experimentally, we demonstrate that the same deep network architecture for robotic instrument segmentation can be trained on both real data and on our proposed synthetic data without affecting the quality of the output models' performance. We show this for several recent approaches and provide experimental support on publicly available datasets, which highlight the potential value of this approach.
Passthrough+

We present an end-to-end system for real-time environment capture, 3D reconstruction, and stereoscopic view synthesis on a mobile VR headset. Our solution allows the user to use the cameras on their VR headset as their eyes to see and interact with the real world while still wearing their headset, a feature often referred to as Passthrough. The central challenge when building such a system is the choice and implementation of algorithms under the strict compute, power, and performance constraints imposed by the target user experience and mobile platform. A key contribution of this paper is a complete description of a corresponding system that performs temporally stable passthrough rendering at 72 Hz with only 200 mW power consumption on a mobile Snapdragon 835 platform. Our algorithmic contributions for enabling this performance include the computation of a coarse 3D scene proxy on the embedded video encoding hardware, followed by a depth densification and filtering step, and finally stereoscopic texturing and spatio-temporal up-sampling. We provide a detailed discussion and evaluation of the challenges we encountered, as well as algorithm and performance trade-offs in terms of compute and resulting passthrough quality.;AB@The described system is available to users as the Passthrough+ feature on Oculus Quest. We believe that by publishing the underlying system and methods, we provide valuable insights to the community on how to design and implement real-time environment sensing and rendering on heavily resource constrained hardware.
An Improved Lightweight Network MobileNetv3 Based YOLOv3 for Pedestrian Detection

Recently, most object detection under videos have increasingly relied on the Unmanned Aerial Vehicle (UAV) platforms because of UAVs' timeliness, pertinence, and high flexibility in data acquisition. Convolution neural networks, especially for YOLO v3, have proved to be effective in intelligent pedestrian detection. However, two problems need to be solved in pedestrian detection of UAV images. One is more small pedestrian objects in UAV images; the other is the complex structure of Darknet53 in YOLO v3, which requires massive computation. To solve these problems, an improved lightweight network MobileNetv3 based on YOLO v3 is proposed. First, the improved MobileNetv3 takes place of the Darknet53 for feature extraction to reduce algorithm complexity and model simplify. Second, complete IoU loss by incorporating the overlap area, central point distance and aspect ratio in bounding box regression, is introduced into YOLO v3 to lead to faster convergence and better performance. Moreover, a new attention module SESAM is constructed by channel attention and spatial attention in MobileNetv3. It can effectively judge long-distance and small-volume objects. The experimental results have shown that the proposed model improves the performance of pedestrian detection of UAV images.
Cryptographically strong permutations from the butterfly structure

Boomerang connectivity table is a new tool to characterize the vulnerability of cryptographic functions against boomerang attacks. Consequently, a cryptographic function is desired to have boomerang uniformity as low as its differential uniformity. Based on generalized butterfly structures recently introduced by Canteaut, Duval and Perrin, this paper presents infinite families of permutations of $${\mathbb {F}}_{2^{2n}}$$ for a positive odd integer n, which have the best known nonlinearity and boomerang uniformity 4. Both open and closed butterfly structures are considered. The open butterflies, according to experimental results, appear not to produce permutations with boomerang uniformity 4. On the other hand, from the closed butterflies we derive a condition on coefficients $$\alpha , \beta \in {\mathbb {F}}_{2^n}$$ such that the functions $$\begin{aligned} V_i(x,y) := (R_i(x,y), R_i(y,x)), \end{aligned}$$ where $$R_i(x,y)=(x+\alpha y)^{2^i+1}+\beta y^{2^i+1}$$ and $$\gcd (i,n)=1$$ , permute $${{\mathbb {F}}}_{2^n}^2$$ and have boomerang uniformity 4. In addition, experimental results for $$n=3, 5$$ indicate that the proposed condition seems to cover all such permutations $$V_i(x,y)$$ with boomerang uniformity 4.
Cerebrovascular segmentation from TOF-MRA based on multiple-U-net with focal loss function

Accurate cerebrovascular segmentation plays an important role in the diagnosis of cerebrovascular diseases. Considering the complexity and uncertainty of doctors' manual segmentation of cerebral vessels, this paper proposed an automatic segmentation algorithm based on Multiple-U-net (M-U-net) to segment cerebral vessel structures from the Time-of-flight Magnetic Resonance Angiography (TOF-MRA) data. First, the TOF-MRA data was normalized by volume and then divided into three groups through slices of axial, coronal and sagittal directions respectively. Three single U-nets were trained by divided dataset. To solve the problem of uneven distribution of positive and negative samples, the focal loss function was adopted in training. After obtaining the prediction results of three single U-nets, the voting feature fusion and the post-processing process based on connected domain analysis would be performed. 95 volumes of TOF-MRA provided by the MIDAS platform were applied to the experiment, among which 20 volumes were treated as the training dataset, 5 volumes were used as the validation dataset and the remaining 70 volumes were divided into 10 groups to test the trained model respectively. Experiments showed that the proposed M-U-net based algorithm achieved 88.60% and 87.93% Dice Similarity Coefficient (DSC) on the verification dataset and testing dataset, which performed better than any single U-net. Compared with other existing algorithms, our algorithm reached the state of the art level. The feature fusion of three single U-nets could effectively complement the segmentation results.