Miracle-dz/UniFAR-FTUs huggingface dataset:
Blindness (Diabetic Retinopathy) Severity Scale Detection

Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness. Timely diagnosis and treatment of DR are critical to avoid total loss of vision. Manual diagnosis is time consuming and error-prone. In this paper, we propose a novel deep learning based method for automatic screening of retinal fundus images to detect and classify DR based on the severity. The method uses a dual-path configuration of deep neural networks to achieve the objective. In the first step, a modified UNet++ based retinal vessel segmentation is used to create a fundus image that emphasises elements like haemorrhages, cotton wool spots, and exudates that are vital to identify the DR stages. Subsequently, two convolutional neural networks (CNN) classifiers take the original image and the newly created fundus image respectively as inputs and identify the severity of DR on a scale of 0 to 4. These two scores are then passed through a shallow neural network classifier (ANN) to predict the final DR stage. The public datasets STARE, DRIVE, CHASE DB1, and APTOS are used for training and evaluation. Our method achieves an accuracy of 94.80% and Quadratic Weighted Kappa (QWK) score of 0.9254, and outperform many state-of-the-art methods.
Unsupervised Energy-based Adversarial Domain Adaptation for Cross-domain Text Classification

Transferring knowledge from a label-rich domain (source domain) to a label-scarce domain (target domain) for pervasive cross-domain Text Classiﬁcation (TC) is a non-trivial task. To overcome this issue, we propose EADA, a novel unsupervised energy-based adversarial domain adaptation framework. First, a deep pre-trained language model (e.g. RoBERTa) is leveraged as a shared feature extractor that maps the text sequences from both source and target domains to a feature space. Since the source features maintain good feature discrim-inability because of the full supervised training, we design a method that encourages target features towards the source ones via adversarial learning. An autoencoder is designed as an energy function that focuses on reconstructing source feature embeddings, while the feature extractor aims to generate source-like target feature embeddings to deceive the autoencoder. In this manner, the target feature embeddings become domain-invariant and inherit great discriminability. Extensive experiments on multi-domain sentiment classiﬁcation (Amazon review dataset) and Yes/No question-answering classiﬁcation (BoolQ and MARCO dataset) are conducted. The experimental results validate that EADA largely alleviates the domain discrepancy while maintaining excellent discriminability and achieves state-of-the-art cross-domain TC performance.
Tailored Military Recruitment through Machine Learning Algorithms

Identifying postal codes with the highest recruiting potential corresponding to the desired profile for a military occupation can be achieved by using the demographics of the population living in that postal code and the location of both the successful and unsuccessful applicants. Selecting N individuals with the highest probability to be enrolled from a population living in untapped postal codes can be done by ranking the postal codes using a machine learning predictive model. Three such models are presented in this paper: a logistic regression, a multi-layer perceptron and a deep neural network. The key contribution of this paper is an algorithm that combines these models, benefiting from the performance of each of them, producing a desired selection of postal codes. This selection can be converted into N prospects living in these areas. A dataset consisting of the applications to the Canadian Armed Forces (CAF) is used to illustrate the methodology proposed.
Uncertainty Aware Review Hallucination for Science Article Classification

The high subjectivity and costs inherent in peer reviewing have recently motivated the preliminary design of machine learning-based acceptance decision methods. However, such approaches are limited in that they: a) do not explore the usage of both the reviewer and area chair recommendations, b) do not explicitly model subjectivity on a per submission basis, and c) are not applicable in realistic settings, by assuming that review texts are available at test time, when these are exactly the inputs that should be considered to be missing in this application. We propose to utilise methods that model the aleatory uncertainty of the submissions, while also exploring different loss importance interpolations between area chair and reviewers’ recommendations. We also propose a modality hallucination approach to impute review representations at test time, providing the ﬁrst realistic evaluation framework for this challenging task.
Post-Quantum Digital Signature Scheme with BB84 Protocol

Data encryption is the classical way of ensuring the various types of the sensitive data. The global release of quantum computers is expected in the near future. Quantum computers have the ability to break the existing classical digital signatures. Because, the classical digital signature schemes are vulnerable to the attacks of quantum computers. This fact involves the different research efforts that look for digital signatures that are secure against quantum computer-based attacks. In the paper, we analyze some digital signature schemes, which are secure against attack of quantum computers. The described schemes have various efficiency problems. Merkle signature scheme is analyzed, it’s great problem is the very big size of the key pair. The paper analyzes the quantum key distribution protocols. BB84 key distribution protocol is described and analyzed. In the paper, we offer the novel scheme with reduced size of the key. The security of the scheme is analyzed. The key of the novel scheme is much less, than in the case of Merkle digital signature scheme.
Marked Attribute Bias in Natural Language Inference

Reporting and providing test sets for harmful bias in NLP applications is essential for building a robust understanding of the current problem. We present a new observation of gender bias in a downstream NLP application: marked attribute bias in natural language inference. Bias in downstream applications can stem from training data, word embeddings, or be amplified by the model in use. However, focusing on biased word embeddings is potentially the most impactful first step due to their universal nature. Here we seek to understand how the intrinsic properties of word embeddings contribute to this observed marked attribute effect, and whether current post-processing methods address the bias successfully. An investigation of the current debiasing landscape reveals two open problems: none of the current debiased embeddings mitigate the marked attribute error, and none of the intrinsic bias measures are predictive of the marked attribute effect. By noticing that a new type of intrinsic bias measure correlates meaningfully with the marked attribute effect, we propose a new postprocessing debiasing scheme for static word embeddings. The proposed method applied to existing embeddings achieves new best results on the marked attribute bias test set. See https://github.com/hillary-dawkins/MAB.
A System Dynamics Model Approach for Simulating Hyper-inflammation in Different COVID-19 Patient Scenarios

The exceptionally high virulence of COVID-19 and the patients’ precondition seem to constitute primary factors in how pro-inflammatory cytokines production evolves during the course of an infection. We present a System Dynamics Model approach for simulating the patient reaction using two key control parameters (i) virulence, which can be “moderate” or “high” and (ii) patient precondition, which can be “healthy”, “not so healthy” or “serious preconditions”. In particular, we study the behaviour of Inflammatory (M1) Alveolar Macrophages, IL6 and Active Adaptive Immune system as indicators of the immune system response, together with the COVID viral load over time. The results show that it is possible to build an initial model of the system to explore the behaviour of the key attributes involved in the patient condition, virulence and response. The model suggests aspects that need further study so that it can then assist in choosing the correct immunomodulatory treatment, for instance the regime of application of an Interleukin 6 (IL-6) inhibitor (tocilizumab) that corresponds to the projected immune status of the patients. We introduce machine learning techniques to corroborate aspects of the model and propose that a dynamic model and machine learning techniques could provide a decision support tool to ICU physicians. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
EPredictor: An Experimental Platform for Community Evolution Prediction Tests

This paper presents “EPredictor” an experimental platform which enables testing, verifying and validating models related to community evolution prediction in dynamic social networks. Community evolution prediction is one of the most interesting issues in the field of social network analysis. It is usually handled by following four main steps: (1) split the network into timeframes; (2) detect communities in each timeframe; (3) track their evolutionary behavior and (4) build a predictive model to forecast the future events. The main objective of EPredictor is to provide a flexible environment that handles the entire process of community evolution prediction with a rich set of literature methods for each step; thus enabling researchers to make valuable comparisons and consistent analysis.
Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning

Script reasoning infers subsequent events from a given event chain, which involves the ability to understand relations between events. A human-labeled script reasoning dataset is usually of small size with limited event relations, which highlights the necessity to leverage ex-ternal eventuality knowledge graphs (KG) consisting of numerous triple facts to describe the inferential relation between events. Existing methods adopt a retrieval and integration paradigm to focus merely on the graph triples that have event overlap with a script, but ignore much more supportive triples in the KG with similar inferential patterns, leading to under-exploiting. To fully exploit the KG, we pro-pose a knowledge model to learn the inferential relations between events from the whole eventuality KG and then support downstream models by directly capturing the relation be-tween events in a script. We further present a neural script adapter to extend the knowledge model for inferring the associated relations be-tween an event chain and a subsequent event candidate. We evaluate the proposed approach on a popular multi-choice narrative cloze task for script reasoning and achieve new state-of-the-art accuracy, compared with baselines either incorporating external KG or not.
Gaussian Process based Deep Dyna-Q approach for Dialogue Policy Learning

Applying reinforcement learning to dialogue policy learning requires prohibitively large rounds of human-machine interactions. To improve the learning performance, the Deep Dyna-Q framework with a world model that imitates real users is widely used in recent years. Unfortunately, how to build an effective world model and how to evaluate the experiences generated by the world model efﬁ-ciently have not been well studied. In order to further improve the effectiveness and efﬁ-ciency of dialogue policy learning, we present a novel Gaussian Process based Deep Dyna-Q approach in this paper. The Gaussian Process model, which is analytically tractable and ﬁts for small-sample problems, is introduced to build the world model. In addition, we design a highly efﬁcient Kullback-Leibler divergence based discriminator to evaluate the quality of experiences generated by the world model. Extensive experiments validate the effectiveness and robustness of our proposed approach. The task-completion success rate can be improved by about 20% with fewer human-machine interactions.
Ransomware Detection using Markov Chain Models over File Headers

: In this paper, a new approach for the detection of ransomware based on the runtime analysis of their behaviour is presented. The main idea is to get samples by using a mini-ﬁlter to intercept write requests, then decide if a sample corresponds to a benign or a malicious write request. To do so, in a learning phase, statistical models of structured ﬁle headers are built using Markov chains. Then in a detection phase, a maximum likelihood test is used to decide if a sample provided by a write request is normal or malicious. We introduce new statistical distances between two Markov chains, which are variants of the Kullback-Leibler divergence, which measure the efﬁciency of a maximum likelihood test to distinguish between two distributions given by Markov chains. This distance and extensive experiments are used to demonstrate the relevance of our method.
Wearable MIMUs for the Identification of Upper Limbs Motion in an Industrial Context of Human-Robot Interaction

The automation of human gestures is gaining increasing importance in manufacturing. Indeed, robots support operators by simplifying their tasks in a shared workspace. However, human-robot collaboration can be improved by identifying human actions and then developing adaptive control algorithms for the robot. Accordingly, the aim of this study was to classify industrial tasks based on accelerations signals of human upper limbs. Two magnetic inertial measurement units (MIMUs) on the upper limb of ten healthy young subjects acquired pick and place gestures at three different heights. Peaks were detected from MIMUs accelerations and were adopted to classify gestures through a Linear Discriminant Analysis. The method was applied firstly including two MIMUs and then one at a time. Results demonstrated that the placement of at least one MIMU on the upper arm or forearm is suitable to achieve good recognition performances. Overall, features extracted from MIMUs signals can be used to define and train a prediction algorithm reliable for the context of collaborative robotics.
Towards a Neural Network based Reliability Prediction Model via Bugs and Changes

: Nowadays, software systems have become larger and more complex than ever. A system failure could threaten the safety of human life. Discovering the bugs as soon as possible during the software development and investigating the effect of a change in the software system are two main concerns of the software developers to increase system’s reliability. Our approach employs a neural network to predict reliability via post-release defects and changes applied during the software development life cycle. The CK metrics are used as predictors variables, whereas the target variable is composed of both bugs and changes having different weights. This paper empirically investigates various prediction models considering different weights for the components of the target variable using ﬁve open source projects. Two major perspectives are explored: cross-project to identify the optimum weight values for bugs and changes and cross-project to discover the best training project for a selected weight. The results show that for both cross-project experiments, the best accuracy is obtained for the models with the highest weights for bugs (75% bugs and 25% changes) and that the right ﬁtted project to be used as training is the PDE project.
Named Entity Recognition through Deep Representation Learning and Weak Supervision

Weakly supervised methods estimate the labels for a dataset using the predictions of several noisy supervision sources. Many machine learning practitioners have begun using weak supervision to more quickly and cheaply annotate data compared to traditional manual labeling. In this paper, we focus on the spe-ciﬁc problem of weakly supervised named entity recognition (NER) and propose an end-to-end model to learn optimal assignments of latent NER tags using observed tokens and weak labels provided by labeling functions. To capture the sequential dependencies between the latent and observed variables, we propose a sequential graphical model where the components are approximated using neural networks. State-of-the-art contextual embeddings are used to further discriminate the quality of noisy weak labels in various contexts. Results of experiments on four public weakly supervised named entity recognition datasets show a signiﬁcant improvement in F1 score over recent approaches.
Optimizing the Usability of User Interfaces based on Multi-objective Evolutionary Algorithms

Solving the software system problems using optimization algorithms stands for an intrinsic area of research whose aim is to find an optimal solution according to a set of conflicting objectives. One of the most prominent problems is optimizing the software quality such as usability of user interfaces following the model-driven engineering (MDE). One of the main challenges of MDE process is identifying the highly-usable model according to a set of desired usability aspects. Although models may be equivalent from the functional viewpoint, they may differ from the non-functional perspectives. Besides, they do not fulfil the same usability properties. In this context, we adressed this issue by combining the power of model engine and the optimization algorithms. In this study, we propose to integrate a multi-objective evolutionary algorithm at the conceptual level of the MDE process. It allows to find an optimal (or near-optimal) model from a large search space according to a set of usability aspects and taking into account the context of use.
Mobile Family Detection through Audio Signals Classification

Nowadays smartphones, and generically speaking mobile devices, allow users a plethora of tasks in total mobility for instance, from checking the balance on the bank account to distance learning. In this context it is really critical the detection of malicious behaviours, considering the weaknesses of the current antimalware mechanisms. In this paper we propose a method for malicious family detection exploiting audio signal processing: in fact, an application is converted into an audio file and then is processed to generate a feature vector to input several classifiers. We perform a real-world experimental analysis by considering a set of malware targeting the Android platform i.e., 4746 malware belonging to 10 families, showing the effectiveness of the proposed approach for Android malicious family detection.
Structure-Aware Pre-Training for Table-to-Text Generation

Table-to-text generation is a subtask of data-to-text generation which aims to generate nal-tural language text based on input table. Pre-training techniques have achieved great success on table-to-text generation. However, the pre-trained models used in previous works are typically trained on free-form natural language text while the input of table-to-text task is structured table. In this paper, we propose STTP, a pre-trained model that is trained with tables and their contexts. The STTP model can understand the structured input table and generate ﬂuent text. Experiments on two datasets show the efﬁcacy of our model.
Language-based General Action Template for Reinforcement Learning Agents

Prior knowledge plays a critical role in decision-making, and humans preserve such knowledge in the form of natural language (NL). To emulate real-world decision-making, artiﬁcial agents should incorporate such generic knowledge into their decision-making framework through NL. However, since policy learning with NL-based action representation is intractable due to NL’s combinatorial complexity, previous studies have limited agents’ expressive power to only a speciﬁc environment, which sacriﬁced the generalization ability to other environments. This paper proposes a new environment-agnostic action framework, the language-based general action template ( L-GAT ). We design action templates on the basis of general semantic schemes (FrameNet, VerbNet, and WordNet), facilitating the agent in ﬁnding a plausible action in a given state by using prior knowledge while covering broader types of actions in a general manner. Our experiment using 18 text-based games showed that our proposed L-GAT agent which uses the same actions across games, achieved a performance competitive with agents that rely on game-speciﬁc actions. We have published the code at https://github.com/kohilin/lgat .
Learning-based Optimal Control of Constrained Switched Linear Systems using Neural Networks

This work considers (deep) artificial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.
MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction

Document-level relation extraction aims to detect the relations within one document, which is challenging since it requires complex reasoning using mentions, entities, local and global contexts. Few previous studies have distinguished local and global reasoning explicitly, which may be problematic because they play different roles in intra-and inter-sentence relations. Moreover, the interactions between local and global contexts should be considered since they could help relation reasoning based on our observation. In this paper, we pro-pose a novel mention-based reasoning (MRN) module based on explicitly and collaboratively local and global reasoning. Based on MRN, we design a co-predictor module to predict entity relations based on local and global entity and relation representations jointly. We evaluate our MRN model on three widely-used benchmark datasets, namely DocRED, CDR, and GDA. Experimental results show that our model outperforms previous state-of-the-art models by a large margin.
Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation

In Natural Language Understanding (NLU), to facilitate Cross-Lingual Transfer Learning (CLTL), especially CLTL between distant languages, we integrate CLTL with Machine Translation (MT), and thereby propose a novel CLTL model named Translation Aided Language Learner (TALL). TALL is constructed as a standard transformer, where the encoder is a pre-trained multilingual language model. The training of TALL includes an MT-oriented pre-training and an NLU-oriented ﬁne-tuning. To make use of unannotated data, we implement the recently proposed Unsupervised Machine Translation (UMT) technique in the MT-oriented pre-training of TALL. The experimental results show that the application of UMT enables TALL to consistently achieve better CLTL performance than our baseline model, which is the pre-trained multilingual language model serving as the encoder of TALL, without using more annotated data, and the performance gain is relatively prominent in the case of distant languages.