Reviewerly/exHarmony huggingface dataset:
Question Similarity Detection on Stack Overflow Sites

Community-Based Question Answering (CQA) has grown in popularity as a way for people from all backgrounds to share information and knowledge. Stack Overflow is a widespread CQA website that focuses on problems and queries related to programming. Many of the questions posted on Stack Overflow have already been answered. However, two questions that ask the same thing could have vastly different vocabulary and grammatical structures, making determining their semantic equivalence difficult. Automatic duplicate detection saves moderators time before taking action and also assists question issuers in finding solutions rapidly. Also, finding a similar question on two different websites in two different languages is a troublesome task. Thus, the proposed approach focuses on similarity detection on the Stack Overflow website in English and Spanish. It prepares labeled data by collecting the questions from both websites and providing the labels manually. Moreover, it utilizes the Synthetic Minority Oversampling Technique (SMOTE) data augmentation technique for data balancing. This work also uses machine learning techniques such as neural networks, Word Mover Distance (WDM), and Logistic Regression for detecting similar questions on SO and SO-ES sites. The model is evaluated using standard metrics such as the confusion matrix, accuracy, and recall. Logistic Regression outperforms the other three algorithms in terms of accuracy, while WDM performs well in terms of recall.
An Idealized Model for the Formal Security Analysis of the Mimblewimble Cryptocurrency Protocol

Mimblewimble is a privacy-oriented cryptocurrency technology that provides security and scalability properties that distinguish it from other protocols. Mimblewimble’s cryptographic approach is based on Elliptic Curve Cryptography which allows verifying a transaction without revealing any information about the transactional amount or the parties involved. Mimblewimble combines Confidential transactions, CoinJoin, and cut-through to achieve a higher level of privacy, security, and scalability. In our previous work ([2], [26], [25]), we have presented and discussed these security properties and presented a model-driven verification approach in order to guarantee the correctness of the protocol implementations. In particular, we have proposed an idealized model that is essential to the described verification process. In that formal setting, we say that a transaction is valid if it is balanced, all output range proofs are valid and the kernel signature is valid for the excess. However, no formal and precise definition was given to the signature requirement. In this paper, we put forward an extension of our model to enable signatures. We specify a signature scheme that allows us to develop several properties and lemmas we have defined on our initial idealized model. The definition of a valid transaction is extended accordingly.
Dual-Navigator Travel Route Plan Method Based on Game Theory

When multiple navigators on the market each provide different types of services, they need to determine how to provide route planning by analysing the strategies of other navigators. Based on the idea of game theory, this paper studies the problem of user path allocation in the case of dual navigators. The navigator plans the route suitable for its own users by calculating the best response of the other navigator. The numerical analysis results show that the route allocation method obtained is in line with the needs of target users of each navigator.
Anomaly Segmentation Using Class-aware Erosion and Smoothing

Typical semantic segmentation methods focus on classification at the pixel level only for the classes included in the training dataset. However, they tend to fail to classify an unexpected class not included in the train dataset. Recent approaches compare the input and its resynthesized images to handle this problem but cannot accurately localize anomalous objects. In this paper, we propose feeding more precise uncertainty estimation to the dissimilarity module for anomaly predictions. Our approach achieved 61.19% AP and 30.77% FPR95 on Fishyscapes Lost and Found dataset.
Aerial Visual Tracking using Multi-level Temporal Contexts Information

Temporal context of video is important for robust visual tracking under complex conditions such as large variations in objects. Existing transformer-based methods focus on updating temporal context information only in search and response map levels that can cause mismatch between search and template. In this paper, we propose an improved visual tracking method applying the template memory update and the multi-aspect loss training method to the existing method. Evaluation on three benchmarks shows that our method outperforms others.
Data Augmentation for Wildlife Animal Recognition Using Style Transfer

Many military facilities are located in the mountain and the forests, so the wildlife animals easily pass through the facilities. To prevent their invasion, military facilities developed an advanced surveillance system to detect wildlife animals, but the insufficient data for nighttime animals has been a challenging problem. To solve the issue, we design two methods to augment and utilize the training data for nighttime wildlife animals by using a style transfer. The first method is designed to transfer the daytime data to be a style of nighttime data, and the second method exchanges the style of nighttime data with that of daytime data. Through the experiments, we show the effectiveness of the two methods, analyzing the augmented training data.
Saliency detection from subitizing processing: First Approximation

Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification or salient object subitizing. In this paper, we study the problem of salient object subutizing, i.e. predicting the number of salient objects in a synthetic images (SID4VAM and Toronto). This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1-4). This means that the subitized information will tell us the number of featured objects in a given image, and will thus subsequently obtain the location or appearance information of the featured objects, and everything will be done within a weakly supervised configuration.
Analysis of Meta-Features in the Context of Adaptive Hybrid Recommendation Systems

The difficulty in finding the most suitable recommendation algorithm for all requests is a common challenge in the recommendation system context, regardless of the domain. Although the combination of multiple techniques in a hybrid approach is often a good solution, the majority of implementations follow a static hybridization scheme, which does not consider the available data, the user specificities at a given time, or the changing environment. Thus, another challenge is the definition of adaptive hybrid recommenders that are capable to adjust the combination of the recommendation algorithms based on the properties of the dynamic inputs. For that purpose, it is necessary to define meta-features that contain information to effectively differentiate recommendation algorithm behaviors over time, and capture how properties of the user/items are related to their performance. This work studies these meta-features in order to analyze their characteristics, relevance, behavior, redundancy and their abilities to represent the system dynamics over time. The paper presents several experiments that can be used as a meta-feature’s evaluation guide, and briefly propose its utilization in a hybrid fuzzy system for recommendation.
ORIGAMI: Folding Data Structures to Reduce Timing Side-Channel Leakage

Timing channels in a program allow attackers to infer secret information being processed. To avoid introducing timing channels, programmers should follow Constant-Time Programming (CTP) guidelines or rely on repair tools that prevent leakage of information via timing channels. Existing repair tools prevent this leakage when programs have branches or loops whose behaviour depends on secrets; however, these repair tools do not efficiently prevent the leakage that occurs if the program accesses a data structure using secret indices. In this work, we present ORIGAMI, a set of repair rules to enforce constant read/write operations on fixed-size, multidimensional data structures so that accessing them via secret indices does not leak information. We implement ORIGAMI as a series of LLVM optimisation passes and evaluate ORIGAMI with programs from Tomcrypt and GDK libraries. Evaluation with the repaired programs using an accurate simulator (GEM5) confirms that our approach indeed repairs the timing channels in practice.
Deblurring Network for Accurate Facial Landmark Detection

We present a face image deblurring framework for accurate facial landmark detection. In face recognition application, performance is strongly affected by the detection accuracy of facial landmarks. Therefore, when developing a facial image deblurring algorithm, it is important not only to improve the restored image quality, but also to improve the landmark detection accuracy of structural parts of the face. In this work, we propose a simple but efficient deblurring network using Wavelet loss and Landmark-error related losses. Experimental results show that the proposed network has superior objective/subjective performance compared to the existing methods.
Proof Assistant Based on Calculational Logic to Assist the Learning of Propositional Logic and Boolean Algebras

Through a friendly user interface it is possible to bring students closer to the proof assistants, so that they can use this type of software as educational tools. In this work CalcLogic is presented, a proof assistant based on Calculational Logic, to assist the teaching of Propositional Logic and Boolean Algebras. Additionally, the results of the educational experience are shown. The collected data shows a group of students for whom the tool has been useful, it also shows a high correlation with the classroom assessments.
Deep Learning-based Target Following and Obstacle Avoidance Methods in Mobile Robots

In this study, we develop a companion robot function that can recognize people and obstacles, and track the human target while avoiding the obstacles. In implementing the JetBot tracking function, a deep learning-based object detection model is modified and utilized. A transfer learning algorithm with a pretrained mobile model is introduced for a feasible operation in the proposed framework with mobile robots.
Scalability and Reliability Analysis of a Novel Cloud Platform for TSO-DSO Information and Data Exchange

Reliability and performance measurement of the cloud computing platform is significant for future information and data exchange in power systems. This paper presents a novel approach for creating virtual machine clusters based on a service-oriented approach. Cloud computing techniques can be utilized to achieve greater interoperability between Transmission System Operators (TSO) and Distribution System Operators (DSO). One of the key elements of cloud computing systems is virtualization, which allows several virtual machines to be built on a single server. This research leverages cloud computing as the major platform for information and data exchange between TSO and DSO. In this work, we have performed a series of tests on the data exchange platform to test its scalability and reliability. The main objective of the proposed virtual machine clustering is to achieve high scalability and reliability.
Evaluating the performance of web rendering technologies based on JavaScript: Angular, React, and Vue

The expansion of online services and the increasing demand for a good experience on the Web have promoted the emergence of different rendering frameworks based on JavaScript that assist the agile development and optimize the application’s performance. This paper presents a methodology to evaluate web rendering frameworks based on virtual and incremental DOM paradigms: Angular, React, and Vue. We conducted a study case based on a generic toy application and analyzed the build size, time to interact, and the DOM manipulation time aspects. Our results show that Vue is 758% faster than React and 595% compared with Angular in manipulation time. Angular occupies 54% more bundle space than Vue and 45% than React. React shows the best time of interactive (300ms), 33% faster than Vue and 50% than Angular. The experiments can support future Web project methodology decisions.
A Reinforcement-Learning Style Algorithm for Black Box Automata

The analysis of hardware and software systems is often applied to a model of a system rather than to the system itself. Obtaining a faithful model for a system may sometimes be a complex task. For learning the regular (finite automata) structure of a black box system, Angluin's <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L^{*}$</tex> algorithm and its successors employ membership and equivalence queries. The regular positive-negative inference (RPNI) family of algorithms use a less powerful capability of collecting observations for learning, with no control on selecting the inputs. We suggest and study here an alternative approach for learning, which is based on calculating utility values, obtained as a discounted sum of rewards, in the style of reinforcement learning. The utility values are used to classify the observed input prefixes into different states, and then to construct the learned automaton structure. We show cases where this classification is not enough to separate the prefixes, and subsequently remedy the situation by exploring deeper than the current prefix: checking the consistency between descendants of the current prefix that are reached with the same sequence of inputs. We show the connection of this algorithm with the RPNI algorithm and compare between these two approaches experimentally.
An Efficiency of Third Party Genetic Algorithms Software Libraries in Mobile Distributed Computing for Financial Time Series Forecasting

Genetic algorithms are very well-known optimization meta-heuristics. They are very well presented in mathematical applications like Matlab, R, and others. Such specific implementations are not proper for industrial software development. Because of its popularity, genetic algorithms have become implemented as third-party software libraries. The popularity of Android OS with its capabilities for running Java source code attracted the usage of external software libraries for achieving mobile distributed computing tasks. Training of artificial neural networks and curve fitting by the usage of genetic algorithms brought financial time series forecasting to the mobile world. In this study, two of the most popular genetic algorithm software libraries are compared in order to be used in mobile distributed computing application.
Advanced Mechanisms of Perception in the Digital Hide and Seek Game Based on Deep Learning

The goal of this paper is to demonstrate the power of machine learning and deep learning combined with powerful tools used in creating virtual simulations. This is made possible by the Unity Game Engine, a software used for the development of 2D and 3D video games and simulations, and the ML-agents plugin, which contains algorithms for training intelligent agents. The basic principles and algorithms of Deep Learning are described. Agent uses these methods to perceive the environment and solve certain tasks. In this paper, a simulation is created in which the agent tries to find hidden objects using machine learning. When the agent finds an item, a new location for the item is randomly generated. It must also complete the task in a specified amount of time. With the right implementation and parameters, the agent could successfully complete the task.
Personal Assistant Supporting Diagnosis of Livestock Poisoning

This paper presents a personal assistant that is able to support veterinarians in diagnosing livestock poisoning. The assistant is implemented as an intelligent agent on top of ZEMELA platform. ZEMELA provides an infrastructure for integration of the physical and virtual worlds that can be used for development of smart agriculture applications. The architecture of the assistant is also presented in the paper. Furthermore, the assistant’s operation is demonstrated by a small example.
Repository Platform for RESTful Web Services

With the growing usage of web services across the globe, demand for advanced repositories has emerged that manage web service discovery and facilitate their use by different domains. The rapid development of mobile applications and the adoption of Internet of Things technologies and serverless computing have led to the popularity of the Representational State Transfer (REST) architecture, a preferable approach. Due to their lightweight nature, the RESTful web services area is preferable over Simple Object Access Protocol (SOAP) web services for the development of distributed applications. Thus, new approaches for the design and development of web service repositories are needed to overcome the limitations of the current web service discovery mechanisms and consider the advanced RESTful technologies. This paper addresses such a need by proposing a repository platform for RESTful web services. It supports the validation of web service definitions against OpenAPI specification, the generation of client SDKs and server stubs, the invocation of web service operations for testing purposes and the assessment of web service availability, customer support level and rating. The architecture, functionality and validation of the platform are presented, giving insight into its features, and proving its feasibility.
Blockchain Support For Time-Critical Self-Healing In Smart Distribution Grids

Smart distribution grids have new protection concepts known as fault self-healing whereby Intelligent Electronic Devices (IEDs) can automatically reconfigure the power circuits to isolate faults and restore power to the relevant sections. This is typically implemented with IEDs exchanging IEC 61850 Generic Object Oriented Substation Event (GOOSE) messages in a peer-to-peer communication network. However, a self-healing application may be faced by challenges of emerging cyber-physical security threats. These can result in disruption to the applications’ operations thereby affecting the power system reliability. Blockchain is one technology that has been deployed in several applications to offer security and bookkeeping. In this paper, we propose a novel concept using blockchain as a second-tier security mechanism to support time-critical self-healing operations in smart distribution grids. We show through a simulation study the impact of our proposed architecture when compared with a normal self healing architecture. The results show that our proposed architecture can achieve significant savings in time spent in no-power state by portions of the grid during cyber-physical attacks.
Intelligent Real-Time Face Mask And Temperature Recognition Using Deep Learning On Raspberry Pi

There has been a steady and significant growth of the advancement in computer vision systems for face masks and temperature tracking. The World Health Organization introduce strict measures to prevent the spread of the coronavirus disease. This paper attempts to create a highly accurate and real-time approach that can effectively detect non-mask trying to enforce to wear mask in order to contribute to community health. For the purpose of detecting face masks, a hybrid model combining deep and regular machine learning will be utilized. We will use OpenCV to recognize faces in real time from a live feed via the Camera module using a dataset that includes images with and without masks and send the data to the cloud for visualization and further analysis. As a main part of the solution, we proposed embedded system with tools utilizing Python, OpenCV, and Tensor Flow with using computer vision and deep learning. To make it cost efficient, quick, scalable, and effective the whole process for detection of face mask is carried out on Raspberry Pi. This project enables improved control over the information already provided and strongly points out the deployment of our method to stop the local transmission from spreading and decrease the possibility of human coronavirus disease carriers.
Memes in Brazilian Digital Culture

This chapter demonstrates an approach to studying digital culture within Brazilian cultural studies that both plays to the existing repertoires of scholars in this area and incorporates the agilities and sensitivities required to do justice to digital culture. It presents a case study of the famous Brazilian internet meme “Cala boca Galvão” of 2010, a joke played by Brazilian internet users at the expense of foreigners, which mushroomed from a catchphrase shared on Twitter during the FIFA World Cup, protesting the verbosity of well-known television presenter Galvão Bueno, into a spoof fundraising campaign to save an endangered parrot in the Amazon. Adopting and adapting a framework put forward by Brian T. Edwards, the chapter analyzes the “meaning,” or content, of the meme (a dispersed collection of texts) and its “movement,” or circulation, revealing how both touch on issues of national identity (including the ongoing importance of television) and the relationship between Brazil and the world. The chapter argues that the continued reverberation and sedimentation of “Cala boca Galvão” and the proliferation of memes emerging on a daily basis in Brazil make digital culture – and the intersections between it and other cultural arenas – essential topics for research in Brazilian cultural studies.
Augmented reality collaborative medical displays (ARC-MeDs) for multi-user surgical planning and intra-operative communication

Digital Displays are integral to modern systems and serve as the primary visual human-computer interface. A physical monitor is a typical example for showing computer-generated information to a person – as long they are located directly in front of them. In an operating room, however, people may be unable to move freely or communicate only based on verbal communication due to noise pollution. Furthermore, mobile and connected editing and sharing of the same content on the go is currently not readily available. Therefore, we propose Augmented Reality Collaborative Medical Displays for duplicating any displayed content as a virtual monitor hovering in space in the real world. Our method replicates any user input on the virtual representation onto the original physical monitor. We put forward a use case in planning a surgical intervention of the abdomen with intra-operative CT imaging. Moreover, we demonstrate how a medical immersive teleconsultation system utilises our method for meaningful interactions. Finally, we evaluate our method with multiple display resolutions. Based on our observations from the use cases and quantitative evaluation, we believe our proposed concept facilitates the integration of future collaborative medical applications.