Reviewerly/exHarmony huggingface dataset:
A High Accuracy Camera Calibration Method for Sport Videos

Camera calibration for sport videos enables precise and natural delivery of graphics on video footage and several other special effects. This in turns substantially improves the visual experience in the audience and facilitates sports analysis within or after the live show. In this paper, we propose a high accuracy camera calibration method for sport videos. First, we generate a homography database by uniformly sampling camera parameters. This database includes more than 91 thousand different homography matrices. Then, we use the conditional generative adversarial network (cGAN) to achieve semantic segmentation splitting the broadcast frames into four classes. In a subsequent processing step, we build an effective feature extraction network to extract the feature of semantic segmented images. After that, we search for the feature in the database to find the best matching homography. Finally, we refine the homography by image alignment. In a comprehensive evaluation using the 2014 World Cup dataset, our method outperforms other state-of-the-art techniques.
Hierarchical Pseudo Labeling Based Embranchment Learning for One-Shot Person Re-Identification

In this paper, one-shot person re-identification (person re-ID) task is brought into focus, where there is only one labeled sample of each pedestrian. Existing one-shot person re-ID researches usually utilize data insufficiently or treat both labeled and pseudo-labeled data in the same way. To solve these problems, we propose a Hierarchical Pseudo Labeling strategy based on Density and Distance and an Embranchment Learning framework. The pseudo labeling strategy can fully exploit the unlabeled data information and generate accurate pseudo labels by multiple clustering based on pairwise feature distances and distribution densities hierarchically. Our Embranchment Learning framework learns data in an individualized way by feeding data with labels from different sources into distinct network branches paired with personalized loss functions. Besides, Batch Distance Loss and Global Center Loss are designed to target the characteristics of distinct input data. They are capable of distinguishing diverse categories and reducing the intra-class distance respectively. Our method outperforms the existing state-of-the-art algorithm by 36.1, 28.7 of mAP and shortens the training time 6 and 10 epochs on Market-1501 and DukeMTMC-reID respectively.
Three Input Exclusive-OR Gate Support for Boyar-Peralta’s Algorithm

The linear layer, which is basically a binary non-singular matrix, is an integral part of cipher construction in a lot of private key ciphers. As a result, optimising the linear layer for device implementation has been an important research direction for about two decades. The Boyar-Peralta's algorithm (SEA'10) is one such common algorithm, which offers significant improvement compared to the straightforward implementation. This algorithm only returns implementation with XOR2 gates, and is deterministic. Over the last couple of years, some improvements over this algorithm has been proposed, so as to make support for XOR3 gates as well as make it randomised. In this work, we take an already existing improvement (Tan and Peyrin, TCHES'20) that allows randomised execution and extend it to support three input XOR gates. This complements the other work done in this direction (Banik et al., IWSEC'19) that also supports XOR3 gates with randomised execution. Further, noting from another work (Maximov, Eprint'19), we include one additional tie-breaker condition in the original Boyar-Peralta's algorithm. Our work thus collates and extends the state-of-the-art, at the same time offers a simpler interface. We show several results that improve from the lastly best-known results.
Using a graph database for the ontology-based information integration of business objects from heterogenous Business Information Systems

This paper reports on findings from a project on information integration from multiple Business Information Systems with the help of a user-specific Enterprise Knowledge Graph. Most ERP systems currently in use store information objects in relational databases. Research in Web Sciences has shown that graph structures present information in a more intuitive way that is easier to interpret for humans. Following a DSR approach, we developed a concept for storing an ontology in a graph database that allows us to map ERP objects and load them at runtime. This allows the end user to navigate through the graph structure, thus providing an intuitive and quick access to essential job-related information. We evaluated the suggested concept with a prototype following the paradigm of polyglot persistence; the prototype was equipped with a graph database to store the company-specific ontology in its native form. The program code was encapsulated into a separate module following a service-oriented software design.
Prediction of Breast Cancer Analysis Using Machine Learning Algorithms and XGBoost Technique

Breast Cancer is one of the most prevalent malignancies amongst men and women. Currently, it has become the common health issue all over the world with its drastic increase in death rate every year. Early detection of breast cancer provides high treatment efficiency and better healing chances. The main contribution of this paper is to find the model which is most accurate for predicting the type of tumour cell (Benign or Malignant). ANOVA f-test Feature Selection is applied to the Wisconsin Breast Cancer dataset to select the subsets of input features that are most relevant to the target variable. We compared various machine learning algorithms like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Logistic Regression, Gaussian Naive Bayes, Random Forest and XG boost Classifier algorithms. We obtained the highest accuracy of 98.25% in XGboost classifier as it uses ensemble techniques and is a very powerful classifier.
Analysing autonomous open-ended learning of skills with different interdependent subgoals in robots

Open-ended learning is a relevant approach allowing the design of robots able to autonomously acquire goals and motor skills useful for solving users' problems. An important challenge in this field involves the autonomous learning of interdependent tasks, where learning one skill requires the achievement of environment states (goals) representing preconditions for the skill that is being learned. Here we enhance and compare two robotic architectures, based on previously proposed works, to study which features favour the learning of goals in the presence of different types of interdependencies. The architectures are tested with a Baxter robot solving a series of tasks, consisting in learning to turn on some button-lights while respecting increasingly complex relations between them. The results show that dealing with goal interdependencies at the high level of the architectures is advantageous with longer goal chains; instead, dealing with the interdependencies at the lower motor-skill level is advantageous when exploration can cause conditions precluding the accomplishment of desired goals.
VizSmith: Automated Visualization Synthesis by Mining Data-Science Notebooks

Visualizations are widely used to communicate findings and make data-driven decisions. Unfortunately creating bespoke and reproducible visualizations requires the use of procedural tools such as matplotlib. These tools present a steep learning curve as their documentation often lacks sufficient usage examples to help beginners get started or accomplish a specific task. Forums such as StackOverflow have long helped developers search for code online and adapt it for their use. However, developers still have to sift through search results and understand the code before adapting it for their use.We built a tool called VizSmith which enables code reuse for visualizations by mining visualization code from Kaggle notebooks and creating a database of 7176 reusable Python functions. Given a dataset, columns to visualize and a text query from the user, VizSmith searches this database for appropriate functions, runs them and displays the generated visualizations to the user. At the core of VizSmith is a novel metamorphic testing based approach to automatically assess the reusability of functions, which improves end-to-end synthesis performance by 10% and cuts the number of execution failures by 50%.
Exploring the social activity of open research data on ResearchGate: implications for the data literacy of researchers

Purpose Although current research has investigated how open research data (ORD) are published, researchers' behaviour of ORD sharing on academic social networks (ASNs) remains insufficiently explored. The purpose of this study is to investigate the connections between ORDs publication and social activity to uncover data literacy gaps. Design/methodology/approach This work investigates whether the ORDs publication leads to social activity around the ORDs and their linked published articles to uncover data literacy needs. The social activity was characterised as reads and citations, over the basis of a non-invasive approach supporting this preliminary study. The eventual associations between the social activity and the researchers' profile (scientific domain, gender, region, professional position, reputation) and the quality of the ORD published were investigated to complete this picture. A random sample of ORD items extracted from ResearchGate (752 ORDs) was analysed using quantitative techniques, including descriptive statistics, logistic regression and K-means cluster analysis. Findings The results highlight three main phenomena: (1) Globally, there is still an underdeveloped social activity around self-archived ORDs in ResearchGate, in terms of reads and citations, regardless of the published ORDs quality; (2) disentangling the moderating effects over social activity around ORD spots traditional dynamics within the “innovative” practice of engaging with data practices; (3) a somewhat similar situation of ResearchGate as ASN to other data platforms and repositories, in terms of social activity around ORD, was detected. Research limitations/implications Although the data were collected within a narrow period, the random data collection ensures a representative picture of researchers' practices. Practical implications As per the implications, the study sheds light on data literacy requirements to promote social activity around ORD in the context of open science as a desirable frontier of practice. Originality/value Researchers data literacy across digital systems is still little understood. Although there are many policies and technological infrastructure providing support, the researchers do not make an in-depth use of them. Peer review The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2021-0255 .
An ecological lens on the professional development of university teachers

This article presents an ecological frame for reflection on teaching at university. It is suggested here that the process of professional reflection on practice can be better aligned with processes of institutional development by applying the adaptive cycle. This heuristic emerged from the scientific literature on ecosystem maintenance and has been repurposed to allow the consideration of complex social-ecological systems – such as a university. The nature of the adaptive cycle changes over time to accommodate changes in the exterior (the institution and wider society) and interior (e.g. personal experience and wellbeing) professional environments. These cycling changes offer a descriptive tool to support discussion of university teacher development.
Entropy Transformation and Expansion with Quantum Permutation Pad for 5G Secure Networks

This paper proposes a quantum safe solution for 5G networks using a quantum permutation pad (or QPP) algorithm, originated from quantum computing logic gates or quantum permutation gates. All permutation gates form a unique permutation space, just like a classical key space. An n-bit permutation space consists of the entire 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ! permutation gates, or permutation matrices over its computational basis. The huge equivalent Shannon entropy of this permutation space would be a nice entropy source for information security. Kuang and Bettenburg in 2020 first proposed QPP and proved it to be the Shannon type of perfect secrecy. Here, we extend its capability of entropy transformations for distribution over the Internet to entropy expansions for 5G networks. We analyze the randomnesses following transformations and expansions with QPP, using industry randomness testing suites. Testing results confirm that QPP can maintain the original randomness of QRNG random numbers for transformations and expansions. Leveraging its strong diffusion capability, QPP may also improve the byte-level randomness of input random numbers.
Fast Surface Topography Reconstruction Method for Profilometer Measurement based on Neural Continuous Representation

Surface profilometers are indispensable for the measurements on precise machining and optical manufacturing industries. However, to acquire accurate surface topography, existing equipment requires line-by-line sampling to cover the target surface, which extremely limits the measurement efficiency. To address this problem, this paper proposes a learning-based high-efficient measurement approach to accelerate the surface topography measurement, which adopts the sparse sampling to reduce the measurement time and learns continue surface representation to reconstruct dense-accurate surface topography. Specifically, a neural network model extracts the features from sparse sampling data and learns an implicit continuous function of the target surface; the dense accurate surface topography can be reconstructed from the continuous representation. The proposed approach greatly reduces sampling time consumption and preserves the accuracy of topography. To facilitate the training process, we synthesize a large-scale machined surface topography dataset to train the baseline model and capture a moderate real-world dataset to fine-tune the model performance in practical situations. Both the simulation and real workpiece experiments verified the performance of the proposed method.
3-D Poststack Seismic Data Compression With a Deep Autoencoder

We approach the problem of 3-D poststack seismic data compression by training a model based on a deep autoencoder. Our network architecture is trained to consider the similarity between 3-D seismic sections drawn from one or multiple seismic volumes. A whole seismic volume is compressed with the latent representations of each of its composing volumetric sections. The goal is to compress the seismic data at very low bit rates with high-quality reconstruction. Our model is suitable for training general compressors from multiple seismic surveys or for specialized compression of a single seismic volume. Results show that our method can compress seismic data with extremely low bit rates, below 0.3 bits-per-voxel (bpv) while yielding peak signal-to-noise ratio (PSNR) values over 40 dB.
Quantum Machine Learning and Fraud Detection

One of the most common problems in cybersecurity is related to the fraudulent activities that are performed in various settings and predominantly through the Internet. Securing online card transactions is a tough nut to crack for the banking sector, for which fraud detection is an essential measure. Fraud detection problems involve huge datasets and require fast and efficient algorithms. In this paper, we report on the use of a quantum machine learning algorithm for dealing with this problem and present the results of experimenting on a case study. By enhancing statistical models with the computational power of quantum computing, quantum machine learning promises great advantages for cybersecurity.
Multi-Label Classification of Indonesian Hate Speech Detection Using One-vs-All Method

Hate speech is an act of communication carried out against specific targets, categories, and levels related to abusive language, even though abusive language is not necessarily. Currently, text mining technology is one approach that can overcome hate speech. Conventional Hate speech expressed in Indonesian Language on Twitter tranform single label to multi-label. The novelty of this research to classifying multi-label hate speech in Indonesian Language using One-vsAll (OvA) that testing on several models of machine learning. Multi-Label on hate speech using in this research is Abusive, HS_Weak, HS_Moderate, and HS_Strong, then transform into new label OvA. There are two scenarios of feature extraction: Bag of Word (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). The experimental results showed that the Artificial Neural Network (ANN) classifier with the One-vs-All method, BoW and Chi-square feature selection got the best accuracy of 86.96%
Human Position Detection Based on Depth Camera Image Information in Mechanical Safety

The devices used for human position detection in mechanical safety mainly include safety light curtain, safety laser scanner, safety pad, and vision system. However, these devices may be bypassed when used, and human or equipment cannot be distinguished. To solve this problem, a depth camera is proposed as a human position detection device in mechanical safety. The process of human position detection based on depth camera image information is given; it mainly includes image information acquisition, human presence detection, and distance measurement. Meanwhile, a human position detection method based on Intel RealSense depth camera and MobileNet-SSD algorithm is proposed and applied to robot safety protection. The result shows that the image information collected by the depth camera can detect the human position in real time, which can replace the existing mechanical safety human position detection device. At the same time, the depth camera can detect only human but not mobile devices and realize the separation and early warning of people and mobile devices.
A Survey on Deep Learning Methods for Cancer Diagnosis Using Multimodal Data Fusion

Recent advances in data collecting methods have enabled life scientists to collect multimodal data from a variety of biological application fields. Multimodal deep learning is becoming more widespread as the capacity of multimodal data streams and deep learning algorithms grows. This leads to the creation of models capable of consistently processing and interpreting multimodal data. The fusion of multimodal data, such as genetic data and histopathological images, is important to better understand cancer heterogeneity and complexity for specific therapies, as well as improving predictions for cancer research. In this brief overview, we present several pioneering deep learning models where we focus on different architectures that has seen use in the medical field, shedding some light on the importance of deep learning in cancer diagnosis considering its immense results in recent years. We particularly set our eyes on multimodal deep learning approaches fusing histopathological images and genomic data to achieve better results than a single modality.
Wasserstein Generative Adversarial Network for Depth Completion With Anisotropic Diffusion Depth Enhancement

The objective of depth completion is to generate a dense depth map by upsampling a sparse one.However, irregular sparse patterns or the lack of groundtruth data caused by unstructured data make depth completion extremely challenging.Sensor fusion using both RGB and LIDAR sensors can help produce a more reliable context with higher accuracy.Compared with previous approaches, this method takes semantic segmentation images as additional input and develops an unsupervised loss function.Thus, when combined with supervised depth loss, the depth completion problem is considered as semi-supervised learning.We used an adapted Wasserstein Generative Adversarial Network architecture instead of applying the traditional autoencoder approach and post-processing process to preserve valid depth measurements received from the input and further enhance the depth value precision of the results.Our proposed method was evaluated on the KITTI depth completion benchmark, and its performance in depth completion was proven to be competitive.
Distributed Ledgers and the Governance of Money

Blockchain technology breathes new life into the classical analysis of money as a substitute for a ledger of all past transactions. While it involves updating the ledger through a decentralized consensus on the unique truth, the robustness of the equilibrium that supports this consensus depends on who has access to the ledger and how it can be updated. To find the optimal solution, Buterin’s “scalability trilemma” needs to be addressed, so that a workable balance can be found between decentralization, security (i.e. a robust consensus), and scale (the efficient volume of transactions). Using a global game analysis of an exchange economy with credit, we solve for the optimal ledger design that balances the three objectives of this trilemma. We characterize the optimal number of validators, supermajority threshold, fees and transaction size. When intertemporal incentives are strong, a centralized ledger is always optimal. Otherwise, decentralization may be optimal, and validators need to be selected from the set of users of the system.
Identifying the Behaviors of an Industrial Plant: Application to Industry 4.0

We go deeper and deeper in automation: for decades, and even centuries, we have been dreaming of a completely automated world with the capacity to self-adapt to any imaginable context. A very promising paradigm to do so is Machine Learning, which allows an automatic and often reliable modeling of any unknown system, by analyzing its data and by deriving knowledge from them. The first tasks of such an approach are data preparation and knowledge extraction, which are areas of research on themselves; as such, experts are generally required in order to manually identify and characterize the studied systems, what can be a long, exhaustive and expensive task. Wouldn't be great if this step could be executed automatically? In this paper, we will present some avenues of reflection in order to achieve such a cognitive system, by digging into the data through clustering approaches and by extracting any knowledge possible.
Dynamic Vehicle Drifting With Nonlinear MPC and a Fused Kinematic-Dynamic Bicycle Model

In this letter we present a versatile trajectory optimization framework that leverages a fused kinematic-dynamic bicycle model for highly dynamic vehicle drifting maneuvers. Our framework can be used online to generate drifting maneuvers, offline to plan drift parking, and additionally enables online tracking of the offline computed parking maneuvers. Importantly, neither individual kinematic nor dynamic bicycle models alone can be used straightforwardly in an optimization framework to plan nor execute the presented motions, as the former cannot model drifting, and the latter becomes ill-defined at low speeds. We validate our framework in a Gazebo simulation of the MIT RACECAR, where we show several drifting scenarios such as steady-state drifting with a range of desired yaw rates as well as a dynamic drift parking maneuver under noisy conditions, and video results can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/MF1_fS6CQQs</uri> .