Reviewerly/exHarmony huggingface dataset:
Applications of Flower Pollination Algorithm in Wireless Sensor Networking and Image Processing: A Detailed Study

In engineering, finding an optimal solution to a problem under highly complex constraints is a very challenging task. Surprisingly, nature has always been playing an important role in solving these complex problems. Nature simulated optimization strategies are the main motivation for development of quality optimization algorithms which can be achieved through the machines. Based on the characteristics of biological systems occur in the nature, many nature inspired metaheuristic algorithms are designed by the researchers in last few decades and flower pollination algorithm (FPA) is one of those widely used optimization algorithms. The biological flower pollination is an optimized process to achieve the optimal reproduction in the flowering plants. Pollination is the process of transferring of pollens between different flowers of the same tree or different flowers from different trees via some pollinators sometimes called pollen vectors say bee, birds, insects, etc. By parameter tuning and hybridization, many variants of FPA have been developed to solve the complex problems. FPA is used in solving problems related to electrical and power system, wireless sensor networking, signal and image processing, clustering and classification, global function optimization, computer gaming, structural and mechanical engineering and many others. In this chapter, we will cover a detailed study of the different applications of FPA in the field of wireless sensor networking and image processing.
Evaluation Strategies for Cybersecurity Training Methods: A Literature Review

The human aspect of cybersecurity continues to present challenges to researchers and practitioners worldwide. While measures are being taken to improve the situation, a vast majority of security incidents can be attributed to user behavior. Security and Awareness Training (SAT) has been available for several decades and is commonly given as a suggestion for improving the cybersecurity behavior of end-users. However, attackers continue to exploit the human factor suggesting that current SAT methods are not enough. Researchers argue that providing knowledge alone is not enough, and some researchers suggest that many currently used SAT methods are, in fact, not empirically evaluated. This paper aims to examine how SAT has been evaluated in recent research using a structured literature review. The result is an overview of evaluation methods which describes what results that can be obtained using them. The study further suggests that SAT methods should be evaluated using a variety of methods since different methods will inevitably provide different results. The presented results can be used as a guide for future research projects seeking to develop or evaluate methods for SAT.
Graph visualization of dark web hyperlinks and their feature analysis

Content regarding various illegal activities, such as weapon and drug trafficking, is shared on the dark web. Most of the illegal content is distributed on anonymous networks that cannot be directly accessed from the World Wide Web. A number of studies have been conducted on the network structure of the World Wide Web since its advent. Similar to the World Wide Web, the dark web is connected by hypertext transfer protocol (http); this makes it possible to use the methods developed for the web in the dark web. Many studies have investigated the dark web and its network structure. However, few studies have focused on the visualization of the dark web network structure, and there have been no studies investigating the temporal changes in the network structure. In this study, to understand the hypertext markup language (html) network structure of the dark web, we created and visualized a graph of the html hyperlink relations of the Tor network, which is popular on the dark web. We then compared the insights gained from graph centrality metrics with those gained from visualizations. The analyzed dataset comprised 25,270,157 pages of html text files crawled from the Tor network by breadth-first search from June 1, 2018, to January 30, 2021. Subsequently, we acquired half-yearly snapshots from the collected data and investigated the change in the dark web network over time using a time-series graph. Then, we derived the centrality metrics from the created graph data and confirmed the differences between the centrality metrics and visualizations. The results obtained in this study provided new insights into the dark web. First, we found that the dark web fluctuated significantly; the structure of the dark web network was more strongly interconnected. Second, most of the nodes that had increased in the past two years may have disappeared rapidly after May 2020. Third, analysis of each snapshot revealed that the proportion of highly volatile domains increased from 40% to 75% during the observation period. Fourth, after calculating the network centrality metrics from each snapshot and comparing the transition of hub nodes in chronological order, we observed that the importance of link-collection sites as the main information retrieval method used in the dark web decreased. Finally, we estimated the size of the dark web based on our observed dark web measurements using the mark-recapture method. To the best of our knowledge, this is the first study to use the mark-recapture method to estimate the size of the dark web network.
DeepTest: How Machine Learning Can Improve the Test of Embedded Systems

When performing functional tests or stress or runtime tests of embedded systems, a large amount of data is collected. The tests are partly performed by developers and system integrators in suitable test environments, but also in acceptance tests or field trials. These test runs have usually been evaluated, albeit not comprehensively, by testers and/or customers, and can thus serve as a reference for further tests. In a research project, we are training neural networks using this data, with the goal of generating and executing new tests. In addition, they are used to identify patterns in the signal runs, which in turn enable us to perform a more thorough automated evaluation of the huge test data. In doing so, we take 2 approaches. On the one hand, we look at the test runs as time series of signal values, the input and output values over time steps during the test runs. On the other hand, we look at sliding time windows over these runs, similar to oscilloscope images, and analyze these signal images. We show how such an extension can be systematically integrated into a development process.
Study on energy saving robot used for screw hole cleaning of reactor vessel in nuclear power plant

Abstract Reactor holes can become stuck due to high temperature, high pressure, prolonged bonding, and load changes due to pressure changes in the reactor. To seal pressure in the reactor vessel, the threaded surfaces of the bolt holes shall be cleaned to remove any foreign matter that may be present in the bolt holes. The worker may manually enter the stud hole for the stud hole cleaning, but the worker’s radiation exposure is increased. Robots are an effective way to work in dangerous areas. Therefore, we introduced robots to clean the stud holes. The positioning of mobile robot is generally based on the mileage, but with the increase of the mileage, the position error will accumulate continuously. In order to eliminate the accumulated error and ensure the stability of the drive, a laser sensor and a new control algorithm are adopted. The distance between the robot and the wall is measured by the laser sensor, and the control algorithm is implemented. The value measured by the sensor moves along the desired trajectory. The actuation performance and hole sensing performance of the robot are verified by field application.
3. Adverbial semi-insubordination constructions in Swedish: Synchrony and diachrony

The term ‘adverbial semi-insubordination’ refers to constructions with subordinate word order that differ from regular clause combining constructions. In a Swedish sentence such as kanske att han inte kommer ‘maybe that he not comes’, the presence of the subordinator att as well as the position of the sentence adverb inte ‘not’ suggests that we are dealing with a subordinate clause, yet this clause is not preceded by a full matrix, but by an epistemic adverb alone. The construction is found in several European languages and has been puzzling syntacticians for decades. Attempts to account for adverbial semi-insubordination are generally tied to a specific syntactic framework, with the unsurprising result that there is little consensus on the syntactic status of either the epistemic adverb or the att-clause. In this paper, we argue that the phenomenon can only be fully understood from a diachronic perspective. Focusing on Swedish, we will first present corpus data of adverbial semi-insubordination in Modern Swedish. Subsequently, we will zoom in on the historical development of the most frequent epistemic adverb in this construction, kanske ‘maybe’, which derives from the VP kan ske ‘can happen’.
Poiesis, ecology and embodied cognition

Since René Descartes famously separated the concepts of body and mind in the seventeenth century, western philosophy and theory have struggled to conceptualize the interconnectedness of minds, bodies, environments and cultures. While environmental psychology and the cognitive sciences have shown that spatial perception is ‘embodied’ and depends on the aforementioned concepts’ interconnectedness, architectural design practice, for example, has rarely incorporated these insights. The article presents research on the epistemological foundations that frame the communication between design theory and practice and juxtaposes it with scientific research on embodied experience. It further suggests that Asian aesthetics, with its long history in conceiving relations and art as interactive, could create a bridge between recent scientific insights and design practice. The article links Asian aesthetics to a discourse on ecologies in the post-Anthropocene, in dialogue with contemporary conceptions of time. It outlines an approach to the interconnectedness of minds, bodies, environments, the sciences and cultures, in favour of a future that is governed by creative wisdom rather than ‘smart’ efficiency.
Linguistic change and historical periodization of Old Literary Finnish

In this study, we have normalized and lemmatized an Old Literary Finnish corpus using a lemmatization model trained on texts from Agricola.We analyse the error types that occur and appear in different decades, and use word error rate (WER) and different error types as a proxy for measuring linguistic innovation and change.We show that the proposed approach works, and the errors are connected to accumulating changes and innovations, which also results in a continuous decrease in the accuracy of the model.The described error types also guide further work in improving these models, and document the currently observed issues.We also have trained word embeddings for four centuries of lemmatized Old Literary Finnish, which are available on Zenodo.
Designing a Donation Portal to Help Underprivileged Indians

Underprivileged people do not get enough donations in the form of cash, food, clothes, or electronic items, as people are not much aware of the problems faced by underprivileged. Below Poverty Line is a benchmark used by the government of India to indicate very low economic status. Requirement of donation for underprivileged as perception and influence from others are the variable that influences people to donate online. Further people seek for the impact they have made on organization after the donation. Preferably, fund raising, need assistance, notifications regarding the impact donors have made, go live, etc., are some of the features of this web application. The objective of the study is to design user interfaces (UIs) of the web application and human centric validation of it. In the current study, two design concepts of a web application were developed considering the user problems identified through user interviews and survey. Then its prototype was created on Adobe XD and usability testing was conducted. It has been observed that the user interface version 2 of the web application was quite favorable to solve the problems faced by donation seeking organizations as well as donors as the flow of the application is acceptable and users can make donation easily to any organization on a single platform. Therefore, it has been concluded that this donation web portal might be useful to provide a good platform to people willing to make donations online.
Exploring the Acceptability of Graphical Passwords for People with Dyslexia

Alphanumeric passwords are still the most common form of user authentication despite well-known usability issues. These issues, including weak composition and poor memorability, have been well-established across different user groups, yet users with dyslexia have not been studied despite making up approximately 10% of the population. In this paper, we focus on understanding the user authentication experiences of people with dyslexia (PwD) in order to better understanding their attitudes towards a graphical password system that may provide a more inclusive experience. Through interactive interviews, participants were encouraged to try three different knowledge-based authentication systems (PIN, password, and graphical password) and then discuss their strategies behind code composition. We found that PwD employed potentially dangerous workarounds when composing passwords, in particular an over-reliance on pattern-based composition. We report on how PwD do not immediately see the benefits of graphical passwords, but upon experiencing the mechanism we see opportunities for more inclusive authentication.
MSHSCNN: Multi-Scale Hybrid-Siamese Network to Differentiate Visually Similar Character Classes

We address the character recognition challenge of similar-looking character classes. Human Vision System often misinterprets visually similar characters while they are present at singular instances. Often, we pay soft attention to a combination of characters to read and interpret a word. A graphical readout of a display device dashboard shows characters generated from a custom vectored font library. A poorly defined character library or dimension adjustment of fonts in the pre-defined layout can often distort the geometric shape of characters. It requires additional intellectual cues for a human to understand and causes poor accuracy by an automated system responsible for character recognition. Even after generalizing over a large-scale dataset, a well-trained character recognition engine misclassifies these custom character images and exhibits low model confidence. In this paper, we optimize a multi-scale Siamese network using multitask Learning to learn significant discriminative features of visually similar characters from a few labeled samples of a custom vectored English font dataset. Using classification and similarity learning, Multitask Learning improves recognition performance and introduces strong inductive biases. Experiments show that our method can effectively distinguish visually similar characters and improves overall classification accuracy.
Analysis and Enhancement of Biometric-Based Multi-Server Authentication Scheme Using Chebyshev Chaotic Map

Earlier authentication protocols used to ask a user to register with all the servers with whom he/she wanted to access the services. Nowadays, an efficient way is provided by multi-server authentication techniques which remove the burden of individual registration and ask a user to register only with one registration center and access services provided by different servers. So far, many multi-server authentication protocols have been given in the literature. But most of them are having drawbacks. Recently, Chatterjee et al. gave a multi-server authentication protocol that used Chebyshev chaotic map approach for providing authentication. This paper analyzes the abovementioned protocol and found that it suffers from eavesdropping and user impersonation attacks. Besides that, there are flaws in registration, login and authentication phases of the protocol. Thereafter, we propose the modifications in the protocol without increase in the computation cost in order to make it more robust and secure.
The Volctrans Neural Speech Translation System for IWSLT 2021

This paper describes the systems submitted to IWSLT 2021 by the Volctrans team.We participate in the offline speech translation and textto-text simultaneous translation tracks.For offline speech translation, our best end-to-end model achieves 7.9 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution.For text-to-text simultaneous translation, we explore the best practice to optimize the wait-k model.As a result, our final submitted systems exceed the benchmark at around 7 BLEU on the same latency regime.We release our code and model to facilitate both future research works and industrial applications 1 .
Dynamical Uncertainty Propagation with Noisy Quantum Parameters

Many quantum technologies rely on high-precision dynamics, which raises the question of how these are influenced by the experimental uncertainties that are always present in real-life settings. A standard approach in the literature to assess this is Monte Carlo sampling, which suffers from two major drawbacks. First, it is computationally expensive. Second, it does not reveal the effect that each individual uncertainty parameter has on the state of the system. In this Letter, we evade both these drawbacks by incorporating propagation of uncertainty directly into simulations of quantum dynamics, thereby obtaining a method that is orders of magnitude faster than Monte Carlo simulations and directly provides information on how each uncertainty parameter influences the system dynamics. Additionally, we compare our method to experimental results obtained using the IBM quantum computers.
Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders

It has been long known that sparsity is an effective inductive bias for learning efficient representation of data in vectors with fixed dimensionality, and it has been explored in many areas of representation learning.Of particular interest to this work is the investigation of the sparsity within the VAE framework which has been explored a lot in the image domain, but has been lacking even a basic level of exploration in NLP.Additionally, NLP is also lagging behind in terms of learning sparse representations of large units of text e.g., sentences.We use the VAEs that induce sparse latent representations of large units of text to address the aforementioned shortcomings.First, we move in this direction by measuring the success of unsupervised state-of-the-art (SOTA) and other strong VAE-based sparsification baselines for text and propose a hierarchical sparse VAE model to address the stability issue of SOTA.Then, we look at the implications of sparsity on text classification across 3 datasets, and highlight a link between performance of sparse latent representations on downstream tasks and its ability to encode taskrelated information.
Automatic Skill Generation for Knowledge Graph Question Answering

Knowledge Graphs are a critical source for Question Answering, but their potential may be threatened due to the complexity of their query languages, such as SPARQL. On the opposite side, Virtual Assistants have witnessed an extraordinary interest as they enable users to pose questions in natural language. Many companies and researchers have combined Knowledge Graphs and Virtual Assistants, but no one has provided end-users with a generic methodology to generate extensions for automatically querying knowledge graphs. Thus, we propose a community shared software framework to create custom extensions to query knowledge graphs by virtual assistants, unlocking the potentialities of the Semantic Web technologies by bringing knowledge graphs in the “pocket” of everyone, accessible from smartphones or smart speakers.
Exploiting Transitivity for Entity Matching

The goal of entity matching in knowledge graphs is to identify sets of entities that refer to the same real-world object. Methods for entity matching in knowledge graphs, however, produce a collection of pairs of entities claimed to be duplicates. This collection that represents the sameAs relation may fail to satisfy some of its structural properties such as transitivity. We show that an ad-hoc enforcement of transitivity on the set of identified entity pairs may decrease precision. We therefore propose a methodology that starts with a given similarity measure, generates a set of entity pairs, and applies cluster editing to enforce transitivity, leading to overall improved performance.
A Reproducible IT-Blog Corpus

The dataset comprises text and metadata extracted from several hundred IT-blogs and websites, along with a method to duplicate the data by updating its contents and downloading it to the user’s local machine. The targets have been hand-picked with the intention to represent the discourse on blogs and websites dedicated to questions at the intersection of technology and society from Germany and the United States of America. The texts have been retrieved by web crawling techniques. The resulting corpus is accessible through a search platform and also reproducible with freely accessible descriptors and software.
Hardness and Optimality in QBF Proof Systems Modulo NP

In this paper we show that extended Q-resolution is optimal among all QBF proof systems that allow strategy extraction modulo an NP oracle. In other words, for any QBF refutation system f where circuits witnessing the Herbrand functions can be extracted in polynomial time from f-refutations, f can be simulated by extended Q-resolution augmented with an NP oracle as described by Beyersdorff et al. We argue that using NP oracles and strategy extraction gives a natural framework to study QBF systems as they have relations to SAT calls and game instances, respectively, in QBF solving. A weaker version of QBF extension variables also put forward by Jussila et al. does not have this optimality result, and we show that under an NP oracle there is no improvement of weak extended Q-Resolution compared to ordinary Q-Resolution.