Reviewerly/exHarmony huggingface dataset:
Hybrid Models for Learning to Branch

A recent Graph Neural Network (GNN) approach for learning to branch has been shown to successfully reduce the running time of branch-and-bound algorithms for Mixed Integer Linear Programming (MILP). While the GNN relies on a GPU for inference, MILP solvers are purely CPU-based. This severely limits its application as many practitioners may not have access to high-end GPUs. In this work, we ask two key questions. First, in a more realistic setting where only a CPU is available, is the GNN model still competitive? Second, can we devise an alternate computationally inexpensive model that retains the predictive power of the GNN architecture? We answer the first question in the negative, and address the second question by proposing a new hybrid architecture for efficient branching on CPU machines. The proposed architecture combines the expressive power of GNNs with computationally inexpensive multi-layer perceptrons (MLP) for branching. We evaluate our methods on four classes of MILP problems, and show that they lead to up to 26% reduction in solver running time compared to state-of-the-art methods without a GPU, while extrapolating to harder problems than it was trained on. The code for this project is publicly available at this https URL.
Road Traffic Prediction Model Using Extreme Learning Machine: The Case Study of Tangier, Morocco

An efficient and credible approach to road traffic management and prediction is a crucial aspect in the Intelligent Transportation Systems (ITS). It can strongly influence the development of road structures and projects. It is also essential for route planning and traffic regulations. In this paper, we propose a hybrid model that combines extreme learning machine (ELM) and ensemble-based techniques to predict the future hourly traffic of a road section in Tangier, a city in the north of Morocco. The model was applied to a real-world historical data set extracted from fixed sensors over a 5-years period. Our approach is based on a type of Single hidden Layer Feed-forward Neural Network (SLFN) known for being a high-speed machine learning algorithm. The model was, then, compared to other well-known algorithms in the prediction literature. Experimental results demonstrated that, according to the most commonly used criteria of error measurements (RMSE, MAE, and MAPE), our model is performing better in terms of prediction accuracy. The use of Akaike’s Information Criterion technique (AIC) has also shown that the proposed model has a higher performance.
Modern approaches to preprocessing industrial data

In the context of the current technological progress, big data arises as a compelling research topic. This paper presents non-traditional analysis strategies like exploiting data semantics (cycle identification) as well as traditional ones (signal interpolation and correlation) for industrial data within a Big Data paradigm. A general approach of preprocessing operations for exploring and extracting valuable knowledge from a large set of industrial data is defined. The identified strategies are tested and validated on a real industrial data set characterized by a multitude of complexities. The methodology involves both statistic and semantic processing steps including building various data visualization mechanisms and correlating the signals. Each of the presented approaches handles complexity in the real data.
TESLA-based authentication for BeiDou civil navigation message

Due to the civil BeiDou navigation system is open, unauthenticated, and non-encrypted, civilian BeiDou navigation signals may have great security loopholes during transmission or reception. The main security loophole here is spoofing attacks. Spoofing attacks make the positioning or timing results of BeiDou civilian receivers wrong. Such errors may cause a series of security problems, which lays a serious hidden danger for Bei-Dou satellite information security. This article proposes an anti-spoofing method for BeiDou navigation system based on the combination of SM commercial cryptographic algorithm and Timed Efficient Stream Loss-tolerant Authentication (TESLA) for spoofing attacks. In this solution, we use the SM3 algorithm to generate a TESLA key chain with time information, and then use the key in the key chain to generate the message authentication code for the BeiDou D2 navigation message. The message authentication code is inserted into a reserved bit of the D2 navigation message. In addition, this solution uses the SM2 algorithm to protect and encrypt time information in the TESLA key chain to prevent key replay attacks in TESLA. The experimental results tested on the experimental platform built in this paper show that this scheme reduces the possibility of the BeiDou navigation system being deceived and enhances the safety of the BeiDou navigation system.
Fast self-learning modulation recognition method for smart underwater optical communication systems

Automatic modulation recognition (AMR) is an integral part of an intelligent transceiver for future underwater optical wireless communications (UOWC). In this paper, an orthogonal frequency division multiplexing (OFDM) based progressive growth meta-learning (PGML) AMR scheme is proposed and analyzed over UOWC turbulence channels. The novel PGML few-shot AMR framework, mainly suffering from the severe underwater environments, can achieve fast self-learning for new tasks with less training time and data. In the PGML algorithm, the few-shot classifier, which works in the presence of Poisson noise, is fed with constellations of noisy signals in bad signal-to-noise ratio (SNR) scenarios directly. Moreover, the data augmentation (DA) operation is adopted to mitigate the impact of light-emitting diode (LED) distortion, yielding further classification accuracy improvements. Simulation results demonstrate that the proposed PGML scheme outperforms the classical meta-learning (ML) approach in training efficiency, robustness against Poisson noise and generalization performance on a new task.
Arabic duplicate questions detection based on contextual representation, class label matching, and structured self attention

Question Answering Systems (QAS) are rising solutions providing exact and precise answers to natural questions. Duplicate Question Detection (DQD), which aims to reuse previous answers, has shown its ability to improve user experience and reduce significantly the response time. However, few Arabic QAS integrate solutions able to detect duplicate questions in their workflow. In this paper, we build a DQD method based on contextual word representation, question classification and forward/backward structured self attention. First, we extract contextual word representation Embeddings from Language Models (ELMo) to map questions into a vector space. Next, we train two models to classify question embedding according to two taxonomies: Hamza et al. and Li & Roth. Then, we introduce a class label matching step to filter out questions that have different class labels. Finally, we propose a Bidirectional Attention Bidirectional LSTM (BiAttention BiLSTM) model that focuses only on keywords to predict whether a question pair is a duplicate or not. We also apply a data augmentation strategy based on symmetry, reflexivity, and transitivity relations to improve the generalization of our model. Various experimentations are performed to evaluate the impact of question classification and pre-processing step on DQD model. The obtained results show that our model achieves good performances as compared to the baseline results.
Multigeophysical data integration using cluster analysis: assisting geological mapping in Trøndelag, Mid-Norway

SUMMARY Modern geophysical data acquisition technology makes it possible to measure multiple geophysical properties with high spatial density over large areas with great efficiency. Instead of presenting these co-located multigeophysical data sets in separate maps, we take advantage of cluster analysis and its pattern exploration power to generate a cluster map with objectively integrated information. Each cluster in the resulting cluster map is characterized by multigeophysical properties and can be associated with certain geological attributes or rock types based on existing geological maps, field data and rock sample analysis. Such a cluster map is usually high in resolution and proven to be more helpful than single-attribute maps in terms of assisting geological mapping and interpretation. In this paper, we present the workflow and technical details of applying cluster analysis to multigeophysical data of a study area in the Trøndelag region in Mid-Norway. We address the importance of carefully designed pre-processing procedures regarding the input data sets to ensure an unbiased data integration using cluster analysis. Random forest as a supervised machine learning method for classification/regression is strategically employed post-clustering for quality evaluation of the results. The multigeophysical data used for this study include airborne magnetic, frequency electromagnetic and radiometric measurements, together with ground gravity measurements. Due to the nature of these input data, the resulting cluster map carries multidepth information. When associated with available geological information, the cluster map can help interpret not only bedrock outcrops but also rocks underneath the sediment cover.
Robustness of Rhythmic-Based Dynamic Hand Gesture with Surface Electromyography (sEMG) for Authentication

Authentication has three basic factors—knowledge, ownership, and inherence. Biometrics is considered as the inherence factor and is widely used for authentication due to its conveniences. Biometrics consists of static biometrics (physical characteristics) and dynamic biometrics (behavioral). There is a trade-off between robustness and security. Static biometrics, such as fingerprint and face recognition, are often reliable as they are known to be more robust, but once stolen, it is difficult to reset. On the other hand, dynamic biometrics are usually considered to be more secure due to the constant changes in behavior but at the cost of robustness. In this paper, we proposed a multi-factor authentication—rhythmic-based dynamic hand gesture, where the rhythmic pattern is the knowledge factor and the gesture behavior is the inherence factor, and we evaluate the robustness of the proposed method. Our proposal can be easily applied with other input methods because rhythmic pattern can be observed, such as during typing. It is also expected to improve the robustness of the gesture behavior as the rhythmic pattern acts as a symbolic cue for the gesture. The results shown that our method is able to authenticate a genuine user at the highest accuracy of 0.9301 ± 0.0280 and, also, when being mimicked by impostors, the false acceptance rate (FAR) is as low as 0.1038 ± 0.0179.
Blockchain Technology in Education

The main goal of this research is to analyze the applications of Blockchain technologies to Education for both learners and teachers. To achieve this goal, a literature review was done on the concept, and a bibliometric analysis was also done to identify studies already done on blockchain applied to Education. The research question of this study is: What kind of research has been made on blockchain in Education? The main findings are related to the innovative applications of blockchain technology to getting a diploma, to the assessment, and formative evaluation, but also related to the learning activities design and implementation, and how to keep tracking the whole learning processes in a distributive way, preventing frauds and facilitating the educational management process.
Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.
Long-term target tracking combined with re-detection

Abstract Long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art learning adaptive discriminative correlation filters (LADCF) tracking algorithm with a re-detection component based on the support vector machine (SVM) model. The LADCF tracking algorithm localizes the target in each frame, and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to-correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.
Stego-chain: A framework to mine encoded stego-block in a decentralized network

Traditional image steganography conceals secret data into the cover image and then transmits the resulting stego-image to the intended recipient via an insecure channel. The intruders try to sense the channel but cannot easily find any clues regarding the concealed data since both cover and stego images are seemingly indistinguishable. However, the key concern is the integrity of the stego image as an intruder has the ability to intercept image so as to hamper the secret communication. To address this issue, we have introduced a blockchain based framework named “Stego-chain” which utilizes an efficient steganographic scheme based on dilated Robert’s edge detection. The aim of the said scheme is to expand the edge area so as to increase the embedding payload. The resulting stego image is encrypted through a shared secret key and then broadcasted in the form of chunk of frames across the valid nodes. The receiver accepts the tampered free frames, re-constructs the stego image through the shared secret key and then recovers the secret data through extraction procedure. Simulation results ensured that the proposed scheme offers higher payload than the state-of-the-art methods with considerable distortion in the stego image.
HeLju@VarDial 2020: Social Media Variety Geolocation with BERT Models

This paper describes the Helsinki-Ljubljana contribution to the VarDial shared task on social media variety geolocation. Our solutions are based on the BERT Transformer models, the constrained versions of our models reaching 1st place in two subtasks and 3rd place in one subtask, while our unconstrained models outperform all the constrained systems by a large margin. We show in our analyses that Transformer-based models outperform traditional models by far, and that improvements obtained by pre-training models on large quantities of (mostly standard) text are significant, but not drastic, with single-language models also outperforming multilingual models. Our manual analysis shows that two types of signals are the most crucial for a (mis)prediction: named entities and dialectal features, both of which are handled well by our models.
Fact vs. Opinion: the Role of Argumentation Features in News Classification

A 2018 study led by the Media Insight Project showed that most journalists think that a clear marking of what is news reporting and what is commentary or opinion (e.g., editorial, op-ed) is essential for gaining public trust.We present an approach to classify news articles into news stories (i.e., reporting of factual information) and opinion pieces using models that aim to supplement the article content representation with argumentation features.Our hypothesis is that the nature of argumentative discourse is important in distinguishing between news stories and opinion articles.We show that argumentation features outperform linguistic features used previously and improve on fine-tuned transformer-based models when tested on data from publishers unseen in training.Automatically flagging opinion pieces vs.
Using Big Data Tools to Analyze Digital Footprint in the COVID-19 Pandemic: Some Public Health Ethics Considerations

While many freedoms became halted by city lockdowns and restrictive travel bans amid coronavirus crisis, some countries and regions reopened with public health monitoring and surveillance measures in place. Technology applications such as real-time location data, geofencing technology, video camera footage, and credit card history are now used in novel and poorly understood ways to track movement patterns to stem viral spread. The use of big data analytics, which sometimes involve involuntary and unconsented data access and disclosure, raise public unease about data protection. The result is a balance between public health safety and ethical use of personal data that pushes the limits of privacy rights. Is it ethically permissible to use big data analytics instantiating the goal of public health by infringing on personal privacy in exchange for maximizing public security? Demonstrating the effectiveness of public health measures is difficult as scientific uncertainties and social complexities are presented. This article provides some public health ethics considerations in balancing benefits of public security and personal privacy infringement, supported with examples drawn from Asian countries and regions.
Design of Museum Educational Content based on Mobile Augmented Reality

In the digital age, museums are becoming increasingly integrated with media technology.The interactive mode of museum education brought about by new digital communication technology can increase the audience's participation and interest in museum education content.This paper attempts to use Mobile Augment Reality (MAR) technology to design museum education content under the guidance of the principle of abstraction hierarchy from the theory of education optimization.With the help of MAR technology, museum education content can move up and down at different levels of information abstraction.The purpose is to help the audience move between perceptual knowledge and rational knowledge and improve their ability to understand.The evaluation results show that this style of interactive teaching content can promote visitors' understanding, thinking and interest in the cultural content of museum exhibits.It is also can helps visitors use a more professional-level vocabulary and more detailed descriptions about the cultural content of museum exhibits.The design and implementation method detailed in this paper has a certain guiding value for museum education development as well as utilization of MAR technology.
Deep Learning for Assessing Unhealthy Lettuce Hydroponic Using Convolutional Neural Network based on Faster R-CNN with Inception V2

The hydroponic system is a development of traditional farming that substitute soil as a medium plant due to land limitation. Lettuce is the most popular hydroponic vegetable product in the market. However, during harvesting, there are huge challenges to ensure product quality especially for mass production has a better quality. In this research, we utilized Deep Learning as objection detection to recognize the disease in Hydroponic vegetables by using Faster R-CNN with Inception V2 algorithm and compare the performance by divided the ratio of training and validation dataset into 3 categories i.e. 78/9, 70/17, and 61/26 with the standard testing ratio for all categories is 13%. From this study we obtain a result that ratio 78/9 have a better performance with Accuracy 70%; Precision 97%; Recall 68% and F1 Score 80% however, ratio 61/26 has the lowest performance with Accuracy 40%; Precision 24%; Recall 100% and F1 Score 38,5% from 412 images dataset with 53 testing images with default learning rate setting 0.0002. As the result shown that the testing and validation ratio was affected by the deep learning model performances.
PRELEARN @ EVALITA 2020: Overview of the Prerequisite Relation Learning Task for Italian

The Prerequisite Relation Learning (PRELEARN) task is the EVALITA 2020 shared task on concept prerequisite learning, which consists of classifying prerequisite relations between pairs of concepts distinguishing between prerequisite pairs and non-prerequisite pairs. Four sub-tasks were defined: two of them define different types of features that participants are allowed to use when training their model, while the other two define the classification scenarios where the proposed models would be tested. In total, 14 runs were submitted by 3 teams comprising 9 total individual participants.
Probing Multilingual BERT for Genetic and Typological Signals

We probe the layers in multilingual BERT (mBERT) for phylogenetic and geographic language signals across 100 languages and compute language distances based on the mBERT representations.We 1) employ the language distances to infer and evaluate language trees, finding that they are close to the reference family tree in terms of quartet tree distance, 2) perform distance matrix regression analysis, finding that the language distances can be best explained by phylogenetic and worst by structural factors and 3) present a novel measure for measuring diachronic meaning stability (based on cross-lingual representation variability) which correlates significantly with published ranked lists based on linguistic approaches.Our results contribute to the nascent field of typological interpretability of cross-lingual text representations.