Reviewerly/exHarmony huggingface dataset:
Anomaly Detection on Roads Using C-ITS Messages

Cooperative Intelligent Transport Network is one of the most challenging issue in networking and computer science. In this area, huge amount of data are exchanged. Smart analysis of this collected data could be achieved for many purposes: traffic prediction, driver profile detection, anomaly detection, etc. Anomaly detection is an important issue for road operators. An anomaly on roads could be caused by various reasons: potholes, obstacles, weather conditions, etc. An early detection of such anomalies will reduce incident risks such as traffic jams, accidents. The aim of this paper is to collect message exchanges between vehicles and analyze trajectories. This analysis becomes difficult since a privacy principle is applied in the case of C-ITS. Indeed, each message sent is generated with an identifier of the sender. This identifier is kept only over a specified time interval thus one vehicle will have multiple identifiers. We first have to solve Trajectory-User Linking problem by chaining anonymous trajectories to potential vehicles by considering similarity in movement patterns. After that we apply various methods to check variations of trajectories from normal ones. When we observe some differences, we can raise an alarm about a potential anomaly. In order to check the validity of this work, we generated a large amount of messages exchanges by many vehicles using the Omnet simulator together with the Artery, Sumo plug-in. We applied various variations on some obtained trajectories. Finally, we ran our detection algorithm on the obtained trajectories using different parameters (angles, speed, acceleration) and obtained very interesting results in terms of detection rate.
Semi-supervised Fine-grained Approach for Arabic dialect detection task

Arabic being a language with numerous different dialects, it becomes extremely important to device a technique to distinguish each dialect efficiently. This paper focuses on the fine-grained country level and province level classification of Arabic dialects. The experiments in this paper are submissions done to the NADI 2020 shared Dialect detection task. Various text feature extraction techniques such as TF-IDF, AraVec, multilingual BERT and Fasttext embedding models are studied. We thereby, propose an approach of text embedding based model with macro average F1 score of 0.2232 for task1 and 0.0483 for task2, with the help of semi supervised learning approach.
Rethinking interactive image segmentation: Feature space annotation

Despite the progress of interactive image segmentation methods, high-quality pixel-level annotation is still time-consuming and laborious - a bottleneck for several deep learning applications. We take a step back to propose interactive and simultaneous segment annotation from multiple images guided by feature space projection. This strategy is in stark contrast to existing interactive segmentation methodologies, which perform annotation in the image domain. We show that feature space annotation achieves competitive results with state-of-the-art methods in foreground segmentation datasets: iCoSeg, DAVIS, and Rooftop. Moreover, in the semantic segmentation context, it achieves 91.5% accuracy in the Cityscapes dataset, being 74.75 times faster than the original annotation procedure. Further, our contribution sheds light on a novel direction for interactive image annotation that can be integrated with existing methodologies. The supplementary material presents video demonstrations. Code available at https://github.com/LIDS-UNICAMP/rethinking-interactive-image-segmentation.
Road Object Detection of YOLO Algorithm with Attention Mechanism

In auto-driving cars, incorrect object detection can lead to serious accidents, so high-precision object detection is the key to automatic driving. This paper improves on the YOLOv3 object detection algorithm, and introduces the channel attention mechanism and spatial attention mechanism into the feature extraction network, which is used to autonomously learn the weight of each channel, enhance key features, and suppress redundant features. Experimental results show that the detection effect of the improved network algorithm is significantly higher than that of the YOLOv3 algorithm.
Urban dual mode video detection system based on fisheye and PTZ cameras

This work presents an artificial vision-based monitoring system for urban environments. It comprises a fisheye camera monitoring the scenes 180ox360o hemisphere and a Pan-Tilt-Zoom camera capturing narrower regions of interest in high-resolution. The ONVIF protocol standard is used to interface both IP-cameras, allowing for the integration of camera control (image acquisition and movement) and geometric calculations on a single device. The events of interest (motion of vehicles and pedestrians) are assumed to happen on the ground plane. This assumption is required to solve the back-projection, the function that maps coordinates in the highly distorted images of the fisheye camera to the ground plane. A calibration strategy estimates the poses of the cameras without placing restrictions on their orientations or relative distance. It optimizes the back-projection error in the ground plane instead of the re-projection error in the image. Finally, a simple pointing and zoom adjustment strategy controls the Pan-Tilt-Zoom camera. The system is tested in controlled laboratory conditions and shows accurate outdoor performance for pedestrian observation.
Motivation Role in Engineering Education in the Transition to a Digital Society

One of the main our time trends are the development of intelligent technologies and their implementation in all activity areas. This direction, along with digitalization, allows to intelligently and rationally manage and optimize processes, including in the education scope. In particular, this applies to engineering education, including in the field of control and computer systems. To ensure the implementation of the digital economy concept, we need socially responsible specialists who understand the principles of sustainable development and respect for the environment, who have competencies in the creation and implementation of projects in the new technologies field. Due to the accelerating digitalization of modern society, there is a growing demand for engineers with creative thinking, therefore, the renewed learning environment should motivate students to pursue engineering professions. The article formulates the requirements for the education system in the transition to the digital economy in terms of increasing the future engineer's motivation who need to be able to represent a task in special industry terms and then resolve it using smart technologies. A suggested method uniqueness is the motivation system creation by applying new methods and a digital environment at the university which similarly that is being applied in professional activity.
Learning to Distill Convolutional Features into Compact Local Descriptors

Extracting local descriptors or features is an essential step in solving image matching problems. Recent methods in the literature mainly focus on extracting effective descriptors, without much attention to the size of the descriptors. In this work, we study how to learn a compact yet effective local descriptor. The proposed method distills multiple intermediate features of a pretrained convolutional neural network to encode different levels of visual information from local textures to non-local semantics, resulting in local descriptors with a designated dimension. Experiments on standard benchmarks for semantic correspondence show that it achieves significantly improved performance over existing models, with up to a 100 times smaller size of descriptors. Furthermore, while trained on a small-sized dataset for semantic correspondence, the proposed method also generalizes well to other image matching tasks, performing comparable result to the state of the art on wide-baseline matching and visual localization benchmarks.
CUNI English-Czech and English-Polish Systems in WMT20: Robust Document-Level Training

We describe our two NMT systems submitted to the WMT 2020 shared task in English Czech and English Polish news translation. One system is sentence level, translating each sentence independently. The second system is document level, translating multiple sentences, trained on multi-sentence sequences up to 3000 characters long.
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT

The goal of Automatic Post-Editing (APE) is basically to examine the automatic methods for correcting translation errors generated by an unknown machine translation (MT) system. This paper describes Alibaba’s submissions to the WMT 2020 APE Shared Task for the English-German language pair. We design a two-stage training pipeline. First, a BERT-like cross-lingual language model is pre-trained by randomly masking target sentences alone. Then, an additional neural decoder on the top of the pre-trained model is jointly fine-tuned for the APE task. We also apply an imitation learning strategy to augment a reasonable amount of pseudo APE training data, potentially preventing the model to overfit on the limited real training data and boosting the performance on held-out data. To verify our proposed model and data augmentation, we examine our approach with the well-known benchmarking English-German dataset from the WMT 2017 APE task. The experiment results demonstrate that our system significantly outperforms all other baselines and achieves the state-of-the-art performance. The final results on the WMT 2020 test dataset show that our submission can achieve +5.56 BLEU and -4.57 TER with respect to the official MT baseline.
IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings

In this paper, we present our approaches for the FinSim 2020 shared task on Learning Semantic Representations for the Financial Domain. The goal of this task is to classify financial terms into the most relevant hypernym (or top-level) concept in an external ontology. We leverage both context-dependent and context-independent word embeddings in our analysis. Our systems deploy Word2vec embeddings trained from scratch on the corpus (Financial Prospectus in English) along with pre-trained BERT embeddings. We divide the test dataset into two subsets based on a domain rule. For one subset, we use unsupervised distance measures to classify the term. For the second subset, we use simple supervised classifiers like Naive Bayes, on top of the embeddings, to arrive at a final prediction. Finally, we combine both the results. Our system ranks 1st based on both the metrics, i.e., mean rank and accuracy.
Super-resolution for a dispersive spectrometer using a tilted area sensor and spectrally varying blur kernel interpolation

The grating, lens, and linear sensor determine a spectrometer’s wavelength resolution and measurement range. While conventional methods have tried to improve the optical design to obtain a better resolution, they have a limitation caused by the physical property. To improve the resolution, we introduce a super-resolution method from the computer vision field. We propose tilting an area sensor to realize accurate subpixel shifting and recover a high-resolution spectrum using interpolated spectrally varying kernels. We experimentally validate that the proposed method achieved a high spectral resolution of 0.141nm in 400–800nm by just tilting the sensor in the spectrometer.
Post-Quantum Variants of ISO/IEC Standards: Compact Chosen Ciphertext Secure Key Encapsulation Mechanism from Isogenies

ISO/IEC standardizes several chosen ciphertext-secure key encapsulation mechanism (KEM) schemes in ISO/IEC 18033-2. However, all ISO/IEC KEM schemes are not quantum resilient. In this paper, we introduce new isogeny-based KEM schemes (i.e., CSIDH-ECIES-KEM and CSIDH-PSEC-KEM) by modifying Diffie-Hellman-based KEM schemes in ISO/IEC standards. The main advantage of our schemes are compactness. The key size and the ciphertext overhead of our schemes are smaller than these of SIKE, which is submitted to NIST's post-quantum cryptosystems standardization, for current security analyses. Moreover, though SIKE is proved in the classical random oracle model, CSIDH-PSEC-KEM is proved in the quantum random oracle model. Finally, we discuss difficulty to construct isogeny-based KEM from ISO/IEC KEM schemes in the standard model (i.e., ACE-KEM and FACE-KEM).
Voronoi Shaping for Lattices With Efficient Encoding

In this letter, we propose a Voronoi shaping method for shaping lattices, Λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> , and coding lattices, Λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> , satisfying Λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ⊆ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</b> \mathbb Z <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ⊆ Λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> ⊆ \mathbb Z <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , with K being an integer diagonal matrix. This assumption is easily satisfied for lattices obtained from error-correcting codes. For these lattices, using the proposed strategy, lattice encoding complexity is reduced to the encoding complexity of the underlying error-correcting code. Our proposal is illustrated for Construction-D lattices with Gosset ( E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sub> ) and Leech ( Λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">24</sub> ) lattices as shaping lattices.
Gender-Aware Reinflection using Linguistically Enhanced Neural Models

In this paper, we present an approach for sentence-level gender reinflection using linguistically enhanced sequence-to-sequence models. Our system takes an Arabic sentence and a given target gender as input and generates a gender-reinflected sentence based on the target gender. We formulate the problem as a user-aware grammatical error correction task and build an encoder-decoder architecture to jointly model reinflection for both masculine and feminine grammatical genders. We also show that adding linguistic features to our model leads to better reinflection results. The results on a blind test set using our best system show improvements over previous work, with a 3.6% absolute increase in M2 F0.5.
A <i>De Lege Ferenda</i> Perspective on Artificial Intelligence Systems Designated as Inventors in the European Patent System

Abstract The European patent system was designed around a paradigm of human inventorship. This paper will analyse in depth and from a de lege ferenda perspective the rather general arguments against and in favour of a possible designation of artificial intelligence (AI) systems as inventors. For the sake of a more concrete discussion, it will also outline a potential reform of the European patent system to implement AI inventorship and allocate the right to the European patent for such inventions by default to the machine’s operator. In the process, it will highlight the major specific issues associated with a reform that acknowledges AI inventorship and touch upon possible alternative approaches to addressing the growing autonomy of machines within the R&amp;D process. The study must not be understood as a call for a reform to recognise AI systems as inventors but rather as a manner of laying the foundations for a more concrete, critical and fruitful discussion on non-human inventorship and its alternatives. The analysis will show that the more general, highly conceptional reservations advanced in the current discussion against AI inventorship are somewhat unfounded, e.g. the alleged break with the functions of the current patent system or the alleged need to endow AI with legal personality. More convincing arguments against a reform that allows for the designation of AI systems as inventor might instead relate to the specific difficulties associated with such reform.
Artificial Intelligence and Machine Learning

Abstract The uses of Artificial Intelligence (AI) and Machine Learning (ML) are topics of presentations at most conferences today across diverse professional disciplines. Why? The following quote says it all:
Clairvoyance: A Pipeline Toolkit for Medical Time Series

Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At the same time, medical time-series problems in the wild are challenging due to their highly *composite* nature: They entail design choices and interactions among components that preprocess data, impute missing values, select features, issue predictions, estimate uncertainty, and interpret models. Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support. In particular, orchestrating a real-world project lifecycle poses challenges in engineering (i.e. hard to build), evaluation (i.e. hard to assess), and efficiency (i.e. hard to optimize). Designed to address these issues simultaneously, Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a (i) software toolkit, (ii) empirical standard, and (iii) interface for optimization. Our ultimate goal lies in facilitating transparent and reproducible experimentation with complex inference workflows, providing integrated pathways for (1) personalized prediction, (2) treatment-effect estimation, and (3) information acquisition. Through illustrative examples on real-world data in outpatient, general wards, and intensive-care settings, we illustrate the applicability of the pipeline paradigm on core tasks in the healthcare journey. To the best of our knowledge, Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
The Fine Print in Smart Contracts

One of the purported benefits of blockchain technologies is the ability to house what have been termed ‘smart’ contracts. Such contracts are potentially self-executing depending on the state of information recorded on a blockchain ledger. This paper examines the capabilities of smart contracts from an economic perspective. It is demonstrated that by improving observability and reducing the costs of verification of contract obligation performance, the space of feasible contracts can be enlarged. Moreover, by providing commitments to various monetary payments, a blockchain can potentially create a foundation to house certain mechanisms that have been shown to overcome difficulties of contractual incompleteness. This is demonstrated using a simple international trade environment. Thus, even though smart contracts must respect the incentives of decision-makers in their obligations, they have the potential to use easily verifiable elements to create incentives to reduce hold-up and other contractual difficulties.
Digital core image registration based on SIFT features

Abstract In this paper, SIFT features and FLANN matching are applied to digital core image registration to achieve the purpose of fast digital core image registration. In this paper, three groups of digital core images data are used to carry out experiments from different aspects of rotation, brightness, translation and the combination of the three. The experimental results show that the method in this paper can effectively perform digital core image registration.
A brief analysis of the research scheme of the cyclotron radiation from a single electron

The experiments of Project 8 have been excellent, but the expected goals still difficult to achieve. So much so that some of the results at your fingertips were also missing. In view of this, the focus of this article is to clarify several easy confused concepts. Only in this way, we can reasonably explain the experimental data. The main points are as follows: 1. The value c of the light speed in vacuum and a particle with zero static mass, both of which do not exist in the reality. That is to say, the so-called a photon’s static mass is equal to zero but has energy, which is a paradox that confuses two different definitional domains. 2. In the reality, photons are high-speed particles generated by electromagnetic radiation. They must have the characteristics of (static) mass, energy and wave, in order to describe the main body to aim at photons from different angles. 3. After any main body comes into being electromagnetic radiation, its static mass will inevitably decrease accordingly. 4. The charge-mass ratio of an electron is a physical constant, which is the ratio of its charge to the amount of matter, and is not affected by relativistic effects and electromagnetic radiation. 5. The uncertainty of moving electrons is caused by random electromagnetic radiations. Finally, it is pointed out that if the cyclotron frequency of a single electron is measured, and at the same time, its de Broglie wavelength or frequency can also be measured, then its static mass can be obtained. Even so, the expected goals are still difficult to achieve, because random electromagnetic radiations are always taking away continually the matter composition of the single electron being measured. However, this was precisely a result of the research obtained by Project 8, and had universality, which should be reflect on.
Bitcoin: An Impossibility Theorem for Proof-of-Work based Protocols

Bitcoin’s main innovation lies in allowing a decentralized system that relies on anonymous, proﬁt driven miners who can freely join the system. We formalize these properties in three axioms: anonymity of miners, no incentives for miners to consolidate, and no incentive to assuming multiple fake identities. This novel axiomatic formalization allows us to characterize which other protocols are feasible: Every protocol with these properties must have the same reward scheme as Bitcoin. This implies an impossibility result for risk-averse miners: no protocol satisﬁes the aforementioned constraints simultaneously without giving miners a strict incentive to merge. Furthermore, any protocol either gives up on some degree of decentralization or its reward scheme is equivalent to Bitcoin’s.