Reviewerly/exHarmony huggingface dataset:
Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases

Sentence embeddings are commonly used in text clustering and semantic retrieval tasks. State-of-the-art sentence representation methods are based on artificial neural networks fine-tuned on large collections of manually labeled sentence pairs. Sufficient amount of annotated data is available for high-resource languages such as English or Chinese. In less popular languages, multilingual models have to be used, which offer lower performance. In this publication, we address this problem by proposing a method for training effective language-specific sentence encoders without manually labeled data. Our approach is to automatically construct a dataset of paraphrase pairs from sentence-aligned bilingual text corpora. We then use the collected data to fine-tune a Transformer language model with an additional recurrent pooling layer. Our sentence encoder can be trained in less than a day on a single graphics card, achieving high performance on a diverse set of sentence-level tasks. We evaluate our method on eight linguistic tasks in Polish, comparing it with the best available multilingual sentence encoders.
Heterogeneous Interactive Attention Network for Human Parsing

As a fine-grained semantic segmentation task, human parsing has attracted extensive attention in computer vision. However, without the assistance of heterogeneous information, it is difficult to obtain detailed human parsing directly. At present, although some studies have introduced heterogeneous data to guide human parsing task, such as pose estimation and edge prediction, the correlations between these heterogeneous data has not been effectively utilized. To avoid the distribution gap among heterogeneous data, we proposed a Heterogeneous Interactive Attention Network (HIANet), in which we exploit the attention between heterogeneous data to capture long-distance context dependence. And the supplementary cues with plentiful interaction can mutually guide multi-source features to correct their respective prediction errors, further refine the result of human parsing. Extensive experiments on three human body parsing datasets are conducted, especially on the LIP dataset, where the mean accuracy and mean Intersection-over-Union of the proposed HIANet are improved by 2.86% and 3.90% compared with PGECNet, respectively. Our code has been made available at https://github.com/wangwenjiawj/HIANet.
Natural Handwriting Style Generation with Author Adaptation

Digitization of handwritten documents while preserving the author’s style is important because it maintains the authenticity of the document. This is especially desired in the education field. However, generating personalized digitized text is challenging and is an unsolved problem. In this paper, we propose a novel handwriting generation approach that surpasses previous state-of-the-art results by $\sim$ 16% in the objective study and by $\sim$ 17% in the subjective study. Our contribution is twofold: (1) developing a new objective function based on a novel reparameterization technique for mixture models (2) developing a Fréchet Inception Distance (FID)based metric for evaluating digitized handwriting styles. Moreover, to the best of our knowledge, our work is the first to show the application of optimization-Based Model Agnostic Meta-Learning (MAML), a few shot learning technique, to adapt our model to new author styles with few training examples. Our method presented in this paper establishes the new state of the art in this field.
DASK: Driving-Assisted Secret Key Establishment

Low-cost and easily obtained Global Navigation Satellite System (e.g., GPS) receivers are broadly embedded into various devices for providing location information. In this work, we develop a secret key establishment by utilizing the driving data obtained from GPS. Those data may exhibit randomness as the driver may alternatively step on the accelerator and brake pedals from time to time with varying force in order to adapt to the road traffic during driving. A driving vehicle provides a physically secure boundary as the devices co-located within the vehicle can observe common GPS data, as opposed to devices that do not experience the trip. We implement this key establishment in a real-world environment on top of off-the-shelf GPS-equipped devices as well as widely deployed GPS modules each connected with Raspberry Pi. Extensive experimental results show that when a user drives around 1.36 km for 1.32 minutes on average under moderate traffic conditions, two legitimate GPS-equipped devices in the vehicle can successfully establish a 128-bit secret key. Meanwhile, an attacker following the target vehicle is unable to establish a secret key with the legitimate devices.
Pooling Method Based on Edge Contraction for Graph Convolution Networks

In recent years, various graph pooling methods have been proposed, and the existing edge pooling methods have some problems. Edge pooling aggregates nodes by removing edges while considering some node characteristics. However, edge pooling ignores the surrounding node features and graph topology. We propose a novel graph pooling method to address this problem. To address the problem, we build a reasonable pooling graph topology, consider the structure and feature information of the graph, improve the objectivity of node selection, and use the edge pooling method to select edges after considering the structure and feature information of the graph. Experimental results on the dataset show that our method is effective in graph classification and outperforms state-of-the-art graph pooling methods.
A tool to certify dynamic benchmarks

Benchmarks are useful to allow evaluating the usefulness of new algorithms. However, care has to be taken to avoid cheating in case the users know the benchmarks in advance. In this paper, we present a blockchain-based tool that allows the generation of dynamic benchmarks. Moreover, it provides a verifiable certification about the moment when the benchmark was created, the researcher who asked for it, how much time was spent before solving it, etc. By using it, we can safely deal with the use of dynamic benchmarks without requiring a trusted third party.
Accelerating Fireworks Algorithm with Adaptive Scouting Strategy

We propose an adaptive scouting strategy which is a refinement of our previous work to further improve the performance of the fireworks algorithm (FWA). The proposed strategy makes full use of the currently obtained fitness landscape information to avoid inefficient searches. Specifically, we introduce two new modifications to our previously proposed scouting strategy to more quickly adjust the balance between exploration and exploitation in the face of various optimization scenarios. The first modification is that the initial explosion center migrates with better generated spark individual instead of being fixed on initial firework individual, i.e., the next round of the initial explosion center will move to the recently generated spark individual when the current tracing direction has no potential. The other is to actively reduce the explosion amplitude of subsequent explosion operation when a potential spark individual is generated. Otherwise, increase the explosion amplitude to escape from the trapped local area quickly. To evaluate the performance of the new proposed strategy, we designed a series of comparative experiments and used 28 functions from the CEC 2013 test suite as the benchmark. The experimental results confirmed that the adaptive scouting strategy shows better performance and faster convergence speed especially for complex multimodal optimization problems.
The Generation of Software Security Scoring Systems Leveraging Human Expert Opinion

While the existence of many security elements in software can be measured (e.g., vulnerabilities, security controls, or privacy controls), it is challenging to measure their relative security impact. In the physical world we can often measure the impact of individual elements to a system. However, in cyber security we often lack ground truth (i.e., the ability to directly measure significance). In this work we propose to solve this by leveraging human expert opinion to provide ground truth. Experts are iteratively asked to compare pairs of security elements to determine their relative significance. On the back end our knowledge encoding tool performs a form of binary insertion sort on a set of security elements using each expert as an oracle for the element comparisons. The tool not only sorts the elements (note that equality may be permitted), but it also records the strength or degree of each relationship. The output is a directed acyclic ‘constraint’ graph that provides a total ordering among the sets of equivalent elements. Multiple constraint graphs are then unified together to form a single graph that is used to generate a scoring or prioritization system.For our empirical study, we apply this domain-agnostic measurement approach to generate scoring/prioritization systems in the areas of vulnerability scoring, privacy control prioritization, and cyber security control evaluation.
Returning to Port: Efficient Detection of Home Router Devices

The detection of IoT devices has been a popular research topic, mainly because of their omnipresence and appeal as targets of malicious actors. In this paper, we focus specifically on identifying home router devices across the IP address space by using network fingerprints derived from an active probing approach. Unlike existing approaches that rely on open ports and banner grab-bing, our network fingerprints highlight the feasibility of detecting a portion of home routers even when they do not expose any open ports or network services. Furthermore, we propose a flexible scanning strategy that aims to drastically reduce the number of network probes, enabling scaling of the device detection across the entire IPv4 address space. We then evaluate the false and true positives of our network fingerprints against ground truth data derived from a large passive DNS dataset and Censys. The results show that many home routers can be detected efficiently and that the derived fingerprints have high detection accuracy. Finally, we measure what services are typically exposed by users through their home routers, and point out generic patterns as well as some interesting individual cases.
Automated Extraction of Software Names from Vulnerability Reports using LSTM and Expert System

Software vulnerabilities are closely monitored by the security community to timely address the security and privacy issues in software systems. Before a vulnerability is published by vulnerability management systems, it needs to be characterized to highlight its unique attributes, including affected software products and versions, to help security professionals prioritize their patches. Associating product names and versions with disclosed vulnerabilities may require a labor-intensive process that may delay their publication and fix, and thereby give attackers more time to exploit them. This work proposes a machine learning method to extract software product names and versions from unstructured CVE descriptions automatically. It uses Word2Vec and Char2Vec models to create context-aware features from CVE descriptions and uses these features to train a Named Entity Recognition (NER) model using bidirectional Long short-term memory (LSTM) networks. Based on the attributes of the product names and versions in previously published CVE descriptions, we created a set of Expert System (ES) rules to refine the predictions of the NER model and improve the performance of the developed method. Experiment results on real-life CVE examples indicate that using the trained NER model and the set of ES rules, software names and versions in unstructured CVE descriptions could be identified with F-Measure values above 0.95.
A Hand-Gesture Recognition Based Interpretable Type-2 Fuzzy Rule-based System for Extended Reality

In recent years, technologies such as Augmented Reality (AR) and Virtual Reality (VR) have become more popular and available to broader audiences, leading to the research and development of a myriad of extended applications. A modality for end-users to interact with these applications is through hand gestures, hence the importance of detecting the different gestures in real time. This paper presents an interval type-2 Fuzzy Rule-based System (FRBS) optimised by the Big Bang-Big Crunch (BB-BC) algorithm that uses the fingers’ position from the hand-tracking technology in extended reality (XR) headsets (namely HoloLens 2 and Oculus Quest 2) to classify the user’s hand gestures. This approach achieved an accuracy of 96.4%, and it is an interpretable model that can be understood and adjusted by end-users. The interval type-2 FRBS was tested against a type-1 FRBS and a k-nearest neighbours (KNN) model. It outperformed the type-1 FRBS and was close to the 98.9% accuracy performance of the KNN model, making our suggested approach a competitive alternative to opaque models.
Expectation Entropy as a Password Strength Metric

The classical combinatorics-based password strength formula provides a result in tens of bits, whereas the NIST Entropy Estimation Suite give a result between 0 and 1 for Min-entropy. In this work, we present a newly developed metric - Expectation entropy that can be applied to estimate the strength of any random or random-like password. Expectation entropy provides the strength of a password on the same scale as an entropy estimation tool. Having an Expectation entropy of a certain value, for example, 0.4 means that an attacker has to exhaustively search at least 40% of the total number of guesses to find the password.
Some reflections on the current PID landscape – with an emphasis on risks and trust issues

The current landscape around persistent identifiers (PIDs) keeps quickly evolving. Some PIDs like Digital Object Identifiers (DOIs) for publications and datasets or ORCIDs (Open Researcher and Contributor ID) for persistent author identification are already well-established, but there is also a whole additional range of emerging identifiers in the research area, often being implemented under competing approaches. These include among others identifiers for organisations (OrgIDs), for research grants (grantIDs), and projects (RAIDs), for research equipment and facilities (PIDINSTs) and for physical samples (IGSNs). This is then a timely moment to explore the risks and trust-related issues associated with an ever wider implementation of PIDs. Following an earlier work on ‘risks and trust in pursuit of a well-functioning Persistent Identifier infrastructure for research’ conducted by the Knowledge Exchange (KE) Task & Finish Group on PIDs, the KE commissioned a study in July 2021 to look deeper into these issues. This work, undertaken by the signatories of this paper, will result in the publication of a report and a series of case studies on specific areas of current PID development. At the time the CRIS2022 Conference takes place the work is still underway, but already advanced enough to describe its methodology, early findings, landscape analysis and early recommendations. The full project results are expected to be published by the KE by the end of 2022.
The preimage distributions of a class of zero-difference balanced functions and their partitioned difference families

By further exploring some ideas in Ding and Yin, the results on preimage distributions of a class of zero-difference balanced functions are extended and the parameters of a class of partitioned difference families are determined by means of self-conjugate Gauss sums over finite fields.
Resilience of Hybrid Casper Under Varying Values of Parameters

Hybrid Casper is the new Ethereum blockchain protocol that uses both Proof of Work and Proof of Stake to reach a consensus between nodes. Here, we analyze the protocol using PRISM+ , an extension of the probabilistic model checker PRISM with primitives for expressing blockchain data types. First, we extend PRISM+ to include data types and operations for modeling and analyzing Proof of Stake based consensus protocols. Then, we model Hybrid Casper in PRISM+ as a parallel composition of stochastic processes, thus precisely describing the behavior of the protocol and highlighting its corner cases. PRISM+ is therefore used to rapidly and automatically analyze the resilience of Hybrid Casper when tuning, up or down, several basic parameters of the protocol, such as the rates of creating blocks, and the strategies for determining penalties. Finally, we study the robustness of Hybrid Casper to two well-known attacks: the Eclipse attack and the majority attack.
Parametric design method of building structured cabling system oriented to data mining

Abstract With the rapid development of the construction industry, structured cabling systems (SCSs) have been widely used in the construction industry. However, the design of structured cabling projects is basically done by hand by designers, it then draws using general‐purpose drawing software such as Autocomputer‐aided design, and this traditional design method can no longer cope with the rapid development of SCS construction technology. Data mining techniques can filter and extract useful information from large amounts of data and create an intelligent analysis system, it draws valuable conclusions. Among them, parametric design is widely used in the field of construction. In order to promote the development of building structured cabling industry, this paper will use data mining technology to study the parametric design method of SCS. This paper puts forward the method of secondary development and design software through the research of building SCS, and uses data mining technology to carry out parametric design of building SCS. Then, the feasibility of the design is tested by comparing the data of the traditional design and the parametric design. Experimental data show that the use of parametric design system for building construction drawing design can shorten the time to 0.29 times the original, greatly reducing the design time and improving work efficiency.
Monitoring System of Natural Disaster from Twitter Messages Using Support Vector Machine

Users in Indonesia have made a way of exchanging information on social media, particularly Twitter, during natural disasters, especially when the situation is precarious. This situation can be used to convert the data into meaningful natural disaster information. Because not all tweets about natural disasters provide precise information in this scenario, the purpose of this study is to create a system that would automatically map tweets about natural disasters as they happen depending on the location of the tweet users. Text mining was used to sort data from twitter data, which included information on natural disasters that were occurring in real time. The research will crawl Twitter data and analyze it using the Support Vector Machine (SVM) algorithm as a classifier and the POS TF-IDF as a feature extraction. SVM models with parameters C = 0.7 and (gamma) = 2 yielded precision 91.44 %, recall 85.42%, f1-score 83.56%, and accuracy 91.5%.
The Fuzzy PID Controller Performance in BLDC Motor Rotor Speed Variable

This study presents the performance of a fuzzy PID controller on a variable speed of brushless DC (BLDC) motor. BLDC motors are widely applied in industry and transportation. BLDC motor control still mostly uses conventional PID. The weakness of conventional PID control is that when the target input changes, the output performance also changes. Therefore, a controller with a simple structure with high performance is still necessary to be explored. This study uses a simple fuzzy control structure with three inputs and one output from the conversion of conventional PID controls. Membership functions (MFs) operating ranges for input/output are defined through error, integral error, and differential error parameters. Fuzzy PID controller performance was compared to conventional PID controllers. Simulations were performed using Matlab/Simulink. The results showed that the Fuzzy PID controller had superior performance without overshoot, had a rise time of 0.005 seconds, and a settling time of 0.009 seconds. The energy saving of a fuzzy PID controller reached 99.8%.
Systematic Review of Quality in Class Diagrams for Software Engineering Competencies

Class diagrams may be used as learning resources for the generation of software engineers’ competencies. However, when they are open learning resources, they could lack information about the quality they contain. It may represent a drawback in the generation of the competencies since defects included in them can hinder the learning objective that generates the competencies. This work reviews 109 open class diagrams to identify the most common defects; it also analyzes some related work to identify what are the quality attributes that must exist in class diagrams and the metrics used to evaluate them. The review of the class diagrams is performed based on the attributes and values proposed in the related works. As a result, 15 defects and their frequency in Class Diagrams were identified. In addition, four Class Diagram cases are presented and explained according to the evaluation of the quality attributes.
From traditional to home-confined learning: a case study in a school involved with ICT

The SARS-CoV-2 pandemic affected all aspects of daily life overnight, worldwide. In education, academic systems had to frantically switch to an online format. Teachers and students had to face an unknown situation, with difficulties as isolation, technology management and pedagogical approaches. However, this impact can be drastically reduced for schools with strong previous background in information and communication technologies (ICT). We reached this conclusion by objectively quantifying the learning results in a sudden transition from face-to-face (F2F) to distant learning (DL). We focus on the abrupt home confinement experienced by the Maristak Durango school during the COVID-19 outbreak. As revealed by the statistical analysis related to the grades of the 360+ students (VET, High School) who went through this F2F to DL transition (F2F2DL), almost all students were able to retain and improve their grades. The results prove how a good ICT-based background can tangibly alleviate any radical F2F2DL shift brought by an unforeseen chain of events. This paper contributes to this type of analysis for VET, since there are hardly any studies related to this scenario.
Cryptocurrencies and fiat money: The end of a public good?

Quiggin quoted that “Everyone, except an economist, knows what ‘money’ means, and even an economist can describe it in the course of a chapter or so”. In this paper we consider money a public good. Specifically, by money as a public good, it is clear a) that money is strictly considered as a resource, b) it serves the common good and c) the rules for the monetary system are set up in a democratic manner. Today - or maybe until the rise of FinTech- the State's money, a legal monopoly is the dominant form of money, public money. FinTech introduces blockchain digital currencies and possibly bringing private money and old ideas back to the economic sphere. In this article we outline the pros and cons of cryptocurrency and fiat currency, and we are trying to map while setting future arguments of their possible relationship/coexistence in the monetary systems.