Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network-Based Image Segmentation Techniques

Image segmentation has proven to be beneficial in many applications, including medical imaging, object detection and scene detection. The future of image segmentation is scene prediction based on the previously segmented images. In this paper, author presents a survey comparing image segmentation architectures based on convolutional neural network (CNN). The paper explains the basic layers of CNN, its depth, general representation and the working of CNN. The paper also discusses the challenges faced by the architectures and their probable solutions. This survey highlights the segmentation network (SegNet). SegNet is a technique of encoder-decoder pair for image segmentation and classification. SegNet architecture is built using CNN and Visual Geometry Group (VGG16) network. VGG16 is a network with 16 deep layers and can classify images into 1000 categories. This architecture resolves the problem of high computational time and high requirement of memory. The comparison of fully convolutional network (FCN) and SegNet is also presented at the end. CamVid dataset is used for testing SegNet for scene segmentation. This data set is captured with sensors shown in SUN RGB-D and implemented using PASCAL VOC challenges on road scene segmentation and indoor scene segmentation. The paper tries to explore how the SegNet architecture works efficiently in case of road scenes and faces accuracy issues with indoor scene segmentation.
WhatsApp or Telegram. Which is the Best Instant Messaging Tool for the Interaction in Teamwork?

Instant Messaging tools are a part of our technified daily life. Tools such as WhatsApp or Telegram have millions of users that send continuously billion of messages. This can be used in different context and one of them is in Education. An important part of education is students’ interaction and specially how they interact when working together as a team. This paper deals with an analysis of which of this tool is better in this specific context, especially when students are developing teamwork competences. To facilitate the analysis of the messages two Learning Analytics tools were developed and applied in the context of a Computer Science degree subject, with an important increment in students’ number of messages regarding previous years but several issues to explore, such as: how to deal with the students that employs other tools different to this for interacting with peers, how to encourage students to employ instant messaging tools as real communication mechanisms, how to measure messages content and not only quantity or how to identify the acquisition of other competences analyzing the conversations.
Towards Optimal Attacks on Reinforcement Learning Policies

Control policies, trained using Deep Reinforcement Learning, have been recently shown to be vulnerable to adversarial attacks introducing even minimal perturbations to the policy input. The attacks proposed so far have been designed using heuristics, based on existing adversarial example crafting techniques used to dupe classifiers in supervised learning. In contrast, this paper investigates the problem of devising optimal attacks, depending on a well-defined attacker's objective, e.g., to minimize the main agent average reward. When the policy and the system dynamics, as well as the rewards, are known to the attacker, a scenario referred to as a white-box attack, designing optimal attacks amounts to solving a Markov Decision Process. For what we call black-box attacks, where neither the policy nor the system is known, optimal attacks can be trained using Reinforcement Learning. We present numerical experiments demonstrating the efficiency of our attacks compared to existing attacks. We further quantify the potential impact of attacks and establish its connection to the smoothness of the policy under attack. Smooth policies are naturally less prone to attacks (e.g. Lipschitz policies, with respect to the state, are more resilient). Finally, we show that from the main agent perspective, the system uncertainties induced by the attack can be modelled using a Partially Observable Markov Decision Process (POMDP) framework. We demonstrate that using Reinforcement Learning methods tailored to POMDP (e.g. using Recurrent Neural Networks) leads to more resilient policies.
Ant colony optimization for mining gradual patterns

Gradual pattern extraction is a field in Knowledge Discovery in Databases that maps correlations between attributes of a data set as gradual dependencies. A gradual dependency may take the form: “the more Attribute $$_{K}$$ , the less Attribute $$_{L}$$ ”. Classical approa-ches for extracting gradual patterns extend either a breath-first search or a depth-first search strategy. However, these strategies can be computationally expensive and inefficient especially when dealing with large data sets. In this study, we investigate 3 population-based optimization techniques (i.e. ant colony optimization, genetic algorithm and particle swarm optimization) that may be employed improve the efficiency of mining gradual patterns. We show that ant colony optimization technique is better suited for gradual pattern mining task than the other 2 techniques. Through computational experiments on real-world data sets, we compared the computational performance of the proposed algorithms that implement the 3 population-based optimization techniques to classical algorithms for the task of gradual pattern mining and we show that the proposed algorithms outperform their classical counterparts.
A comparative study between single and multi-frame anomaly detection and localization in recorded video streams

Video anomaly detection is usually studied by considering the spatial and temporal contexts. This paper focuses first on spatial context and shows that it can be a fast real-time solution. In the first part of this work there are two main contributions: employing a new deep network for reconstruction and introducing a new regularity scoring function. The new deep architecture is based on pyramid of input images and compared to UNet, the proposed architecture boosts AUC by 15% and the new regularity scoring function is based on SSIM. The second part employs a multiframe approach to distinguish temporal behavior anomalies. The second approach enhances the results by 7% compared to spatial anomaly detection. Comparing the two approaches, if computing power is limited and real time anomaly detection is looked for, single frame detection is preferred while multi frame analysis offers a much wider possibility of anomaly detection.
BlockPres: A Novel Blockchain-Based Incentive Mechanism to Mitigate Inequalities for Prescription Management System

The study presents a blockchain-based incentive mechanism intended to encourage those in underserved communities to engage with healthcare services. The smart healthcare system, which is the result of the amalgamation of advanced technologies, has emerged recently and is increasingly seen as essential to meet the needs of modern society. An important part of the healthcare system is the prescription management system, but studies show that prescription affordability and accessibility play a part in creating unequal access for underserved communities. This is a form of unequal access that results in those living in underserved communities to become disengaged from accessing healthcare services. In New Zealand, the prescription management system plays a crucial role and this study seeks to address the issue by presenting the BlockPres framework, which uses a novel incentive mechanism to encourage patients to participate and engage with services in order to be rewarded. The blockchain attribute of immutability in BlockPres enhances equality and participation by providing sophisticated authorisation and authentication capabilities for healthcare providers and patients. BlockPres empowers the patient by assigning ownership or control of some patient information to the patient. A simulation is carried out using the Ethereum blockchain and the evaluation of successful transaction completion and superficial performance assessment demonstrates that the blockchain would be sufficient to cope with the needs of a prescription management system. Furthermore, for the simulation, a BlockPres Smart Contract is developed using solidity and implemented in Remix. The Ropsten network is used as the simulation environment and the initial results show that the proposed incentive mechanism mitigates unequal access.
Infinitesimal reference frames suffice to determine the asymmetry properties of a quantum system

Symmetry principles are fundamental in physics, and while they are well understood within Lagrangian mechanics, their impact on quantum channels has a range of open questions. The theory of asymmetry grew out of information-theoretic work on entanglement and quantum reference frames, and allows us to quantify the degree to which a quantum system encodes coordinates of a symmetry group. Recently, a complete set of entropic conditions was found for asymmetry in terms of correlations relative to infinitely many quantum reference frames. However, these conditions are difficult to use in practice and their physical implications unclear. In the present theoretical work, we show that this set of conditions has extensive redundancy, and one can restrict to reference frames forming any closed surface in the state space that has the maximally mixed state in its interior. This in turn implies that asymmetry can be reduced to just a single entropic condition evaluated at the maximally mixed state. Contrary to intuition, this shows that we do not need macroscopic, classical reference frames to determine the asymmetry properties of a quantum system, but instead infinitesimally small frames suffice. Building on this analysis, we provide simple, closed conditions to estimate the minimal depolarization needed to make a given quantum state accessible under channels covariant with any given symmetry group.
Feature Selection for Machine Learning-based Phishing Websites Detection

Phishing is a social engineering technique that is commonly used to deceive users in an attempt to obtain sensitive information such as username, passwords or credit card details. While there was extensive research on machine learning-based phishing detection, some prior works proposed a large number of features and not all of them are feasible to extract for real-time detection. This work combined two datasets with 30 and 48 features respectively, to identify 18 common features. Moreover, feature selection was conducted to identify 13 optimal features for a more robust model. A comparison with prior research works on the same datasets showed that the best models built on all features using the random forest algorithm scored lower on the 30 feature dataset, and achieved better performance on the 48 features dataset. The best model on the 13 features achieved an accuracy of 0.937.
Social Rules for Agent Systems

When creating (open) agent systems it has become common practice to use social concepts such as social practices, norms and conventions to model the way the interactions between the agents are regulated. However, in the literature most papers concentrate on only one of these aspects at the time. Therefore there is hardly any research on how these social concepts relate. It is also unclear whether something like a norm evolves from a social practice or convention or whether they are complete independent entities. In this paper we investigate some of the conceptual differences between these concepts. Whether they are fundamentally stemming from a single social object or should be seen as different types of objects altogether. And finally, when one should which type of concept in an implementation or a combination of them.
Handwritten Chemical Formulas Classification Model Using Deep Transfer Convolutional Neural Networks

With the spread of the COVID19 pandemic, blended learning has become one of the most used methods in educational organizations such as universities, community colleges, and schools. In blended learning, the students' practical activities are done in more than one way, including simulation software and the place of study. For chemical experiment programs, the classification of handwritten chemical formulas plays an important role in determining the simulation software's efficiency. Accordingly, in this study, we propose a model for handwritten chemical formula classification. First, this paper describes a handwritten chemical formulas dataset that contains eight classes (HCFD8). Second, convolutional neural networks (CNNs) with pre-trained weights are used as a deep feature extractor to extract features from the images. Third, due to limited training images per class, the proposed model uses data augmentation techniques to expand the training images. Then, an enhanced multilayer perceptron (EMLP) strategy is used to classify the image. Finally, we provide a performance analysis of typical deep learning approaches on HCFD8, which shows that the proposed model performs good accuracy results.
Rapid Response to the Needs of ESL Students of a Technical University in the Time of Emergency Covid-19 Transfer to Online Classes: ITMO University Case Study

The disruption caused by the 2020 COVID-19 pandemic entailing a transfer to the fully online emergency remote teaching (ERT) put many teachers in Russia and across the globe in the role of facilitators, course designers, technical support specialists and psychologists. To address the new challenge, a group of instructors of St. Petersburg University of Information Technologies Mechanics and Optics (ITMO), Russia, attempted to adopt a blended ESL methodology piloted in 2017–2018 and calibrated in the intermittent period to build a Zoom- and LMS-based mostly synchronous upper-intermediate emergency remote General English course for 99 first and second year bachelor's of science students (7 groups). To increase student satisfaction with the design and content of the course, learners' needs were monitored during the fall 2020 semester through a series of surveys. To measure student satisfaction with the emergency ESL course, a summative cross-sectional survey, a series of semi-structured interviews and fall 2020 course completion rate were analyzed.
Side-channel attack on Rainbow post-quantum signature

Rainbow, a layered multivariate quadratic digital signature, is a candidate for standardization in a competition-like process organized by NIST. In this paper, we present a CPA side-channel attack on the submitted 32-bit reference implementation. We evaluate the attack on an STM32F3 ARM microcontroller, successfully revealing the full private key. Furthermore, we propose a simple masking scheme with minimum overhead.
Towards the Internet of Water: Using graph databases for hydrological analysis on the Flemish river system

Abstract The “Internet of Water” project will deploy 2,500 sensors along the Flemish river system, in Belgium. These sensors will be part of a monitoring system. This will produce an enormous amount of data, on which prediction and analysis tasks can be performed. To represent, store, and query river data, relational databases are normally used. However, this choice introduces an “impedance mismatch” between the conceptual representation (typically a graph) and the storage model (relational tables). To solve this problem, this article proposes to use graph databases. The Flemish river system is presented as a use case and the Neo4j graph database and its high‐level query language, Cypher, are used for storing and querying the data, respectively. A relational alternative is implemented over the PostgreSQL database. A collection of representative queries of interest for hydrologists is defined over both database implementations.
A dynamic model for balancing values

We propose an additive model for balancing the impacts of actions on values, where factors intensify or attenuate impacts on values, and values are assigned degrees of relative importance (weights). The balancing model induces axiological rules, consisting in prohibitions or permissions that are justified according to the impacts of the prohibited or permitted action on the values at stake. We also propose eight different revision operators, which shift the balance - and thus induce different norms - by expanding or contracting either the set of factors or the set of values. We provide the construction and prove some success properties of those operators.
Deep Unsupervised Hashing for Large-Scale Cross-Modal Retrieval Using Knowledge Distillation Model

Cross-modal hashing encodes heterogeneous multimedia data into compact binary code to achieve fast and flexible retrieval across different modalities. Due to its low storage cost and high retrieval efficiency, it has received widespread attention. Supervised deep hashing significantly improves search performance and usually yields more accurate results, but requires a lot of manual annotation of the data. In contrast, unsupervised deep hashing is difficult to achieve satisfactory performance due to the lack of reliable supervisory information. To solve this problem, inspired by knowledge distillation, we propose a novel unsupervised knowledge distillation cross-modal hashing method based on semantic alignment (SAKDH), which can reconstruct the similarity matrix using the hidden correlation information of the pretrained unsupervised teacher model, and the reconstructed similarity matrix can be used to guide the supervised student model. Specifically, firstly, the teacher model adopted an unsupervised semantic alignment hashing method, which can construct a modal fusion similarity matrix. Secondly, under the supervision of teacher model distillation information, the student model can generate more discriminative hash codes. Experimental results on two extensive benchmark datasets (MIRFLICKR-25K and NUS-WIDE) show that compared to several representative unsupervised cross-modal hashing methods, the mean average precision (MAP) of our proposed method has achieved a significant improvement. It fully reflects its effectiveness in large-scale cross-modal data retrieval.
Restricted Boltzmann Machines as Models of Interacting Variables

Abstract We study the type of distributions that restricted Boltzmann machines (RBMs) with different activation functions can express by investigating the effect of the activation function of the hidden nodes on the marginal distribution they impose on observed binary nodes. We report an exact expression for these marginals in the form of a model of interacting binary variables with the explicit form of the interactions depending on the hidden node activation function. We study the properties of these interactions in detail and evaluate how the accuracy with which the RBM approximates distributions over binary variables depends on the hidden node activation function and the number of hidden nodes. When the inferred RBM parameters are weak, an intuitive pattern is found for the expression of the interaction terms, which reduces substantially the differences across activation functions. We show that the weak parameter approximation is a good approximation for different RBMs trained on the MNIST data set. Interestingly, in these cases, the mapping reveals that the inferred models are essentially low order interaction models.
Distributed Multimodal Aspective on Topic Model Using Sentiment Analysis for Recognition of Public Health Surveillance

Social media is the leading platform to accomplish large data in the field of health discipline tweets everywhere in the world empowerment currently. And it is a prominent data source for searching health terms (topics) and predicts solutions in the direction of health care. Health care has become one of the largest sectors in the world in terms of income and employment. Billions of customers use Twitter daily to enable people to share health-related topics of their views and opinions on various healthcare topics. Topic models commence from natural language processing (NLP) to acquiring immeasurable knowledge on healthcare areas which highly motivated to analyze the topic models (TM). Topic models are addressed intended for the squeezing of health topics for modeling the selective latent tweet documents in the healthcare system. Analyzing the topics in TM is an essential issue and facilitates an unreliable number of topics in TM that address the destitute results in health-related clustering (HRC) in various structured and unstructured data. In this regard, needful visualizations are imperative measurements to clipping the information for identifying cluster direction. So that, to believe and contribute proposed distributed multimodal active topic models such as Hadoop distributed non-negative matrix factorization (HdinNMF), Hadoop distributed latent Dirichlet allocation (HdiLDA), and Hadoop distributed probabilistic latent schematic indexing (HdiPLSI) are reasonable approaches for balancing and clipping to the direction of health topics from various perspective data sources in health statistics clustering. Hadoop DiNNMF distributed model is achieved and covered by cosine metrics when exposed to visual clusters and good performance measures compared to other methods in a series of health conditions. This assistance briefly describes the public health structure (hashtags) in good condition in the country and tracks the evolution of the main health-related tweets for preliminary advice to the public.
AVRNTRU: Lightweight NTRU-based Post-Quantum Cryptography for 8-bit AVR Microcontrollers

Introduced in 1996, NTRUEncrypt is not only one of the earliest but also one of the most scrutinized lattice-based cryptosystems and expected to remain secure in the upcoming era of quantum computing. Furthermore, NTRUEncrypt offers some efficiency benefits over “pre-quantum” cryptosystems like RSA or ECC since the low-level arithmetic operations are less computation-intensive and, thus, more suitable for constrained devices. In this paper we present Avrntru, a highly-optimized implementation of NTRUEncrypt for 8-bit AVR microcontrollers that we developed from scratch to reach high performance and resistance to timing attacks. Avrntru complies with the EESS #1 v3.1 specification and supports product-form parameter sets such as ees443ep1, ees587ep1, and ees743ep1. An entire encryption (including mask generation and blinding-polynomial generation) using the ees443ep1 parameters requires 847973 clock cycles on an ATmega1281 microcontroller; the decryption is more costly and has an execution time of 1051871 cycles. We achieved these results with the help of a novel hybrid technique for multiplication in a truncated polynomial ring, whereby one of the operands is a sparse ternary polynomial in product form and the other an arbitrary element of the ring. A constant-time multiplication in the ring given by the ees443ep1 parameters takes only 192577 cycles, which sets a new speed record for the arithmetic part of a lattice-based cryptosystem on AVR.
Color Image Database HTID for Verification of No-Reference Metrics: Peculiarities and Preliminary Results

The paper describes a new image database HTID for verification and training of no-reference image visual quality metrics. The database contains 2880 color images of size 1536×1024 pixels cropped from the real-life photos produced by the mobile phone cameras with various shooting and post-processing settings. Mean opinion scores for images of the database are obtained. Peculiarities of the database are considered. A comparative analysis of the state-of-the-art no-reference image visual quality metrics is carried out. It is shown that the proposed database takes its own unique place in the existing image databases and can be effectively used for metrics' verification.