Reviewerly/exHarmony huggingface dataset:
Bipolar morphological U-Net for document binarization

Deep neural networks are widely used in various AI systems. Many such systems rely on the edge computing concept and try to perform computations on end devices while still being energy and memory efficient. Therefore, substantial time and memory requirements are imposed on neural networks. One way to improve neural network efficiency is to simplify computations inside a neuron. A bipolar morphological neuron uses only addition, subtraction, and maximum operations inside the neuron and exponent and logarithm as activation functions for the network layers. These operations allow fast and compact gate implementation for FPGA and ASIC. In the paper, we consider the usage of bipolar morphological (BM) networks for document binarization. We examine the DIBCO 2017 binarization challenge and train the bipolar morphological convolutional neural network of U-Net architecture. Despite some accuracy decrease for a model with all BM convolutional layers, one can flexibly control the accuracy by using the partially converted model. It should be noted that even the fully BM model is suitable for solving the problem in practice.
Ring Signature With Unconditional Anonymity in the Plain Model

Ring signature allows a user to sign messages as a member of a set of users, which is called the ring. In addition to an unforgeability, ring signature ensures that nobody can detect which member in the ring signs the message. In this paper, we propose the first generic construction of ring signature with unconditional anonymity in the plain model based on the standard assumption. The plain model means that we do not need any setup procedure by trusted third party and not depends on a random oracle methodology. Our scheme is constructed based on a statistical ZAP argument, a lossy encryption scheme, and a standard signature scheme. It is known that all of our building blocks can be instantiated based on the quasi-polynomial learning with errors assumption.
The Effects of Adaptive Control on Learning Directed Locomotion

This study is motivated by evolutionary robot systems where robot bodies and brains evolve simultaneously. In such systems robot `birth' must be followed by `infant learning' by a learning method that works for various morphologies evolution may produce. Here we address the task of directed locomotion in modular robots with controllers based on Central Pattern Generators. We present a bio-inspired adaptive feedback mechanism that uses a forward model and an inverse model that can be learned on-the-fly. We compare two versions (a simple and a sophisticated one) of this concept to a traditional (open-loop) controller using Bayesian optimization as a learning algorithm. The experimental results show that the sophisticated version outperforms the simple one and the traditional controller. It leads to a better performance and more robust controllers that better cope with noise.
CapGen: A Neural Image Caption Generator with Speech Synthesis

Computer vision and natural language processing play a crucial role in automatic depiction of contents in an image. Image captioning is a fundamental problem in the field of artificial intelligence. Machines cannot imitate the human intelligence or the ways humans interact with one another, therefore it stays a continuous undertaking. Automizing the process of understanding and processing the large quantity of multimedia inclusive of images and videos generated everyday have become a major concern in today’s market. In this paper, we anticipate present a generative model based on a deep recurrent neural network structure that joins the latest advances in computer vision and natural language translation so as to generate coherent sentences describing an image. In this endeavor, a hybrid framework is proposed utilizing the multilayer convolutional neural network model to produce human apprehensible descriptions of the pictures using a long short-term memory to precisely structure coherent sentences utilizing the partial captions produced from the considered dataset. The convolutional neural network model looks at the objective picture to an enormous dataset of preparing pictures, to produce a precise portrayal utilizing the prepared inscriptions of data. The model is prepared to amplify the probability of the target words based on maximum likelihood estimation using the given training set. We feature the effectiveness of our proposed model utilizing the Flickr8K dataset. Apart from human evaluation, we were able achieve a cumulative score of 0.693 in the BLEU metric using 200 images at random from the validation and test dataset.
Gaze Estimation with Multi-Scale Channel and Spatial Attention

Gaze estimation is well established as a significant research topic in computer vision given its importance for different applications. Recent studies demonstrate that other regions of the face beyond the two eyes contain valuable information for gaze estimation. Motivated by these works, we propose a novel and powerful deep convolutional network with multi-scale channel and spatial attention, which only takes the full-face image as input without additional modules to detect the eyes and estimate the head pose, to handle the gaze estimation task. It uses multi-scale channel and spatial information to adaptively select and increase important features and suppress some unnecessary facial regions which may not contribute to estimate gaze. By rigorously evaluating our module, we show that our method significantly outperforms the state-of-the-art for 3D gaze estimation on multiple public datasets.
Formalizing Graphical Modularization Approaches for Ontologies and the Knowledge Loss

This paper formalizes the graphical modularization technique view traversal for an ontology component of a Domain Information System (DIS). Our work is motivated by developing the ability to dynamically extract a module (called a view traversal module) based on an initial set of concepts. We show how the ability to quantify the knowledge that is preserved (or lost) in a view traversal module is significant for a multi-agent setting, which is a setting that requires provable privacy. To ensure partial knowledge preservation, we extend the view traversal module to a principal ideal subalgebra module. The cost of this extension is that the obtained knowledge is coarser, as the atoms of the associated lattice are composite yet considered atomic. The presented work constitutes a foundational step towards theories related to reasoning on partial domain knowledge.
The MUCOW word sense disambiguation test suite at WMT 2020

This paper reports on our participation with the MUCOW test suite at the WMT 2020 news translation task. We introduced MUCOW at WMT 2019 to measure the ability of MT systems to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense. MUCOW is created automatically using existing resources, and the evaluation process is also entirely automated. We evaluate all participating systems of the language pairs English -> Czech, English -> German, and English -> Russian and compare the results with those obtained at WMT 2019. While current NMT systems are fairly good at handling ambiguous source words, we could not identify any substantial progress - at least to the extent that it is measurable by the MUCOW method - in that area over the last year.
Understanding the failure modes of out-of-distribution generalization

Empirical studies suggest that machine learning models often rely on features, such as the background, that may be spuriously correlated with the label only during training time, resulting in poor accuracy during test-time. In this work, we identify the fundamental factors that give rise to this behavior, by explaining why models fail this way even in easy-to-learn tasks where one would expect these models to succeed. In particular, through a theoretical study of gradient-descent-trained linear classifiers on some easy-to-learn tasks, we uncover two complementary failure modes. These modes arise from how spurious correlations induce two kinds of skews in the data: one geometric in nature and another, statistical. Finally, we construct natural modifications of image classification datasets to understand when these failure modes can arise in practice. We also design experiments to isolate the two failure modes when training modern neural networks on these datasets.
Distance education as an alternative form of learning during a pandemic

The article proposes the development of information technologies, in particular the use of distance learning in higher education. A systematic analysis of the peculiarities of the use of distance education with the disclosure of objective and subjective aspects of participation. Peculiarities of distance learning of disciplines in higher educational establishments are determined. The benefits of distance learning during a global constraint (pandemic) have been identified. Structuring the principles of modern distance education in higher educational institutions of Ukraine using the associative method.
KFU NLP Team at SMM4H 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions

This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2020 shared tasks. Specifically, we participated in two tasks. We investigate the use of a language representation model BERT pretrained on a large-scale corpus of 5 million health-related user reviews in English and Russian. The ensemble of neural networks for extraction and normalization of adverse drug reactions ranked first among 7 teams at the SMM4H 2020 Task 3 and obtained a relaxed F1 of 46%. The BERT-based multilingual model for classification of English and Russian tweets that report adverse reactions ranked second among 16 and 7 teams at two first subtasks of the SMM4H 2019 Task 2 and obtained a relaxed F1 of 58% on English tweets and 51% on Russian tweets.
Hand Gesture Recognition Control for Computers Using Arduino

With the change in time, new technologies are coming into existence. One of them is hand gesture control used by robots. Hand gestures are used to give commands as an input to these robots. These gesture controls are used as input devices to computer systems. These existing systems are costly and unaffordable by general uses. The idea proposed in the article is to make a cheaper version of a hand gesture controller using the Arduino board. The input is given with the help of a pair of ultrasonic sensors. The sensors are used to calculate the changes in the distance of our hand movements while giving the gesture. The Arduino will calculate the changes in distance and recognize the action done by the user. The Arduino will request the system processor to perform the requested action by the user. The time taken to perform basic operations like open, minimize, maximize, play, save, etc., operations are reduced because of this proposed model.
Genetic Programming Multitasking

In this paper, we present a new multitasking algorithm for Genetic Programming (GP). Our proposed algorithm (referred to as “GP-Tasking”) evolves population using multifaceted strategy. Each individual is trained with different training sets and evaluated with multiple fitness functions (where each fitness function represents one task). At the beginning of the run, GP-Tasking, randomly uses crossover operator to facilitate knowledge transfer between different tasks and store probability of constructive crossover operators between different tasks. This information is used to bias the crossover between tasks that have higher probability of producing fitter offspring. The novelty of GP Tasking, is that it uses one population in the same phenotype space but with different interpretations to explore multiple genotype spaces. GP-Tasking was evaluated with 3 sets of experiments where in each set we tested GP-Tasking ability to solve 5 different tasks simultaneously. Results showed that GPTasking evolved smaller solutions and consume significantly less computational time.
Human-Artificial Systems Collaboration in Service Innovation and Social Inclusion

Human and Artificial intelligence (AI) collaborations have contributed to improve our daily lives. AI can be used to promote employment, social inclusion and service innovation. To improve social inclusion, the integration of foreign origin workers is crucial for economic growth. In Finland, the MALVA project 'preparing immigrants for working life' aims to develops Human-Artificial services that promote social inclusion and service innovation. One important goal of the project is to create a chatbot for companies and develop mobile guiding practices to enhance employment solutions for foreign origin workers. This work is in progress, the present article reports the initial development stages and literature review related to the collaboration of human and artificial intelligence systems (HAS). The paper contributes to the understanding of the AI-based chatbots and their use in the integration of skilled migrant workers to promote social inclusion and economic growth.
Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation

Clustering is one of the most fundamental tasks in machine learning. Recently, deep clustering has become a major trend in clustering techniques. Representation learning often plays an important role in the effectiveness of deep clustering, and thus can be a principal cause of performance degradation. In this paper, we propose a clustering-friendly representation learning method using instance discrimination and feature decorrelation. Our deep-learning-based representation learning method is motivated by the properties of classical spectral clustering. Instance discrimination learns similarities among data and feature decorrelation removes redundant correlation among features. We utilize an instance discrimination method in which learning individual instance classes leads to learning similarity among instances. Through detailed experiments and examination, we show that the approach can be adapted to learning a latent space for clustering. We design novel softmax-formulated decorrelation constraints for learning. In evaluations of image clustering using CIFAR-10 and ImageNet-10, our method achieves accuracy of 81.5% and 95.4%, respectively. We also show that the softmax-formulated constraints are compatible with various neural networks.
A multi‐label cascaded neural network classification algorithm for automatic training and evolution of deep cascaded architecture

Abstract Multi‐label classification algorithms deal with classification problems where a single datapoint can be classified (or labelled) with more than one class (or label) at the same time. Early multi‐label approaches like binary relevance consider each label individually and train individual binary classifier models for each label. State‐of‐the‐art algorithms like RAkEL, classifier chains, calibrated label ranking, IBLR‐ML+, and BPMLL also consider the associations between labels for improved performance. Like most machine learning algorithms, however, these approaches require careful hyper‐parameter tuning, a computationally expensive optimisation problem. There is a scarcity of multi‐label classification algorithms that require minimal hyper‐parameter tuning. This paper addresses this gap in the literature by proposing CascadeML, a multi‐label classification method based on the existing cascaded neural network architecture, which also takes label associations into consideration. CascadeML grows a neural network architecture incrementally (deep as well as wide) in a two‐phase process as it learns network weights using an adaptive first‐order gradient descent algorithm. This omits the requirement of preselecting the number of hidden layers, nodes, activation functions, and learning rate. The performance of the CascadeML algorithm was evaluated using 13 multi‐label datasets and compared with nine existing multi‐label algorithms. The results show that CascadeML achieved the best average rank over the datasets, performed better than BPMLL (one of the earliest well known multi‐label specific neural network algorithms), and was similar to the state‐of‐the‐art classifier chains and RAkEL algorithms.
Privacy-preserving Computation over Encrypted Vectors

Cloud computing allows users to outsource massive amounts of data to a cloud server for storage and analysis, which breaks the bottleneck of limited local resources. However, it makes user data exposed and possibly be accessed by unauthorized entities. Owing to privacy concern, users are inclined to upload encrypted data to a cloud server, but encryption limits operations over original data and affects access to a processing result. Though lots of schemes have been proposed to achieve some basic operations over encrypted data, it still lacks the research on the dot product of encrypted vectors. In this paper, we propose two privacy-preserving dot product schemes based on a dual server model, which can flexibly support single-user access and multiuser access to a final data processing result. Furthermore, we extend them to achieve privacy-preserving Support Vector Machine (SVM) prediction algorithm. Finally, we give security analysis of our proposed schemes and demonstrate their availability and practicality through simulation and comparison with existing works.
Network Virtualization: Proof of Concept for Remote Management of Multi-Tenant Infrastructure

Recently, container and cloud-based orchestration technologies are important requirements for data and functional integration of new control and automation services in distributed networks. The industrial adoption of Docker Swarm and Kubernetes based orchestrators are due to their abilities to support large-scale deployment, reduce capital cost, enable interoperability, introduce flexibility, minimize vendor lock-in and identify scalable means of deploying new functions with minimal performance and monitoring related issues. In this study, we performed a comprehensive study of the requirements and strategies in a Containerized Docker Swarm Orchestration Framework (CDSOF) as an efficient, cost-effective, secure and resilience device management tool for remote administration, configuration and supervision of virtualized large-scale Ethernet connected multi-tenanted assets. In a performance and security comparative analysis to Kubernetes and Apache Mesos, Docker Swarm is the leading solution with support features for overlay networks such as Flannel and Weave. Finally, we established the design and test specifications to guide our substation Virtual Remote Terminal Units (vRTUs) implementation.
A New Method for Constructing the Basic Probability Assignment Function in D-S Evidence Theory

Abstract The basic probability assignment function is the basis of D-S evidence theory and the premise to obtain accurate evaluation conclusions. A new method to construct the basic probability assignment function is proposed in this paper, which uses nonlinear function to construct the basic probability assignment function, and takes into account the relationship between the change trend of index and the change trend of the basic probability assignment value. This construction method can be applied to the construction of the basic probability assignment of the index, with the decrease of whose value, the condition will degrade gradually.
Predictions of Miscommunication in Verbal Communication During Collaborative Joint Action

Purpose The purpose of the current study was to examine the lexical and pragmatic factors that may contribute to turn-by-turn failures in communication (i.e., miscommunication) that arise regularly in interactive communication. Method Using a corpus from a collaborative dyadic building task, we investigated what differentiated successful from unsuccessful communication and potential factors associated with the choice to provide greater lexical information to a conversation partner. Results We found that more successful dyads' language tended to be associated with greater lexical density, lower ambiguity, and fewer questions. We also found participants were more lexically dense when accepting and integrating a partner's information (i.e., grounding) but were less lexically dense when responding to a question. Finally, an exploratory analysis suggested that dyads tended to spend more lexical effort when responding to an inquiry and used assent language accurately-that is, only when communication was successful. Conclusion Together, the results suggest that miscommunication both emerges and benefits from ambiguous and lexically dense utterances.
Risk-Averse Offline Reinforcement Learning

Training Reinforcement Learning (RL) agents in high-stakes applications might be too prohibitive due to the risk associated to exploration. Thus, the agent can only use data previously collected by safe policies. While previous work considers optimizing the average performance using offline data, we focus on optimizing a risk-averse criteria, namely the CVaR. In particular, we present the Offline Risk-Averse Actor-Critic (O-RAAC), a model-free RL algorithm that is able to learn risk-averse policies in a fully offline setting. We show that O-RAAC learns policies with higher CVaR than risk-neutral approaches in different robot control tasks. Furthermore, considering risk-averse criteria guarantees distributional robustness of the average performance with respect to particular distribution shifts. We demonstrate empirically that in the presence of natural distribution-shifts, O-RAAC learns policies with good average performance.
Measuring Students’ Source Code Quality in Software Development Projects Through Commit-Impact Analysis

Evaluating the quality of software engineering projects in university courses is challenging because it evolves over time and is a time consuming task. Students applying software quality principles need early and constant feedback on their projects to improve their technical competence as software developers. We conducted a case study to explore whether student’ changes have an impact on the project quality by mining a Git repository. We analyzed a total of 2253 changes (commits) from an undergraduate software engineering project to understand the impact on quality of each change measuring metrics (complexity, duplication, maintainability, and security) mined from the repository. This analysis allowed us to identify from students’ contributions challenges and improvement opportunities in engineering practices. As future work, we plan to analyze more projects and extend our analysis with more software metrics.
An ambiguity function transformation characteristic for Radar signal sorting

Abstract The sorting rate of current methods is not high and too sensitive to the signal noise ratio (SNR). In order to solve this problem, a novel characteristic for sorting radar emitter signal is proposed. The ambiguity function Radon curve of received signal is extracted, and then a rectangle pulse sequence and a triangle pulse sequence are constructed. Furthermore, resemblance coefficients of the ambiguity function Radon curve with two pulse sequences are extracted, and are used as the sorting parameters. The advantage of this new characteristic is validated by simulation result, and the lowest sorting rate is 90%at SNR=10dB.