Reviewerly/exHarmony huggingface dataset:
"Programming - It’s not for Normal People": A Qualitative Study on User-Empowering Interfaces for Programming Collaborative Robots

Technology can be empowering: It can lift the burden from tasks we find dreadful and help us excel in tasks we enjoy. Equally, a mismatch between the user interface (UI) and our skills can make us feel incompetent and reluctant towards the technology. With the trend to increase the level of automation in fields beyond traditional manufacturing, such as laboratories or small workshops, new technologies such as collaborative robots (cobots) are entering the workplace. The technology literacy levels of professionals in these fields may vary greatly depending on their age, gender, education and personal interests, creating a challenge in designing universal cobot UIs. In this qualitative study, we address the question of how introducing interaction skills and intelligence to cobots may inhibit or encourage users to use them in their work context. The interviews, performed with 15 individuals working in laboratory settings, gave rise to numerous themes relevant to the design of user-empowering cobot interfaces for individuals with varying technology literacy levels: As "programming may not be for a normal person", talking to a robot may not be for another. Incorporating the unique interests, fears, personal and domain experience of the end users can contribute to the design and development of cobot interfacesas diverse as their needs, thus maximizing the likelihood of successful integration across diverse work environments.
The Use of Videoconferencing in Higher Education

This chapter will explore the impact of the COVID-19 pandemic on the higher education industry. Specifically, there will be a discussion of the exponential growth of videoconferencing tools for higher education purposes. The effects of the communication technology tool on the college classroom will be explored, including how faculty and students experience the synchronous, virtual classroom. Parts of the communication process and how they differ in face-to-face and videoconference class will be outlined. The concept of Zoom fatigue will be defined and examined, in relation to the communication process. Recommendations for effective use of videoconferencing in the college classroom will be provided.
LogExtractor: Extracting digital evidence from android log messages via string and taint analysis

Mobile devices are increasingly involved in crimes. Therefore, digital evidence on mobile devices plays a more and more important role in crime investigations. Existing studies have designed tools to identify and/or extract digital evidence in the main memory or the file system of a mobile device. However, identifying and extracting digital evidence from the logging system of a mobile device is largely unexplored. In this work, we aim to bridge this gap.Specifically, we design, prototype, and evaluate LogExtractor, the first tool to automatically identify and extract digital evidence from log messages on an Android device. Given a log message, LogExtractor first determines whether the log message contains a given type of evidentiary data (e.g., GPS coordinates) and then further extracts the value of the evidentiary data if the log message contains it. Specifically, LogExtractor takes an offline-online approach. In the offline phase, LogExtractor builds an App Log Evidence Database (ALED) for a large number of apps via combining string and taint analysis to analyze the apps' code. Specifically, each record in the ALED contains 1) the string pattern of a log message that an app may write to the logging system, 2) the types of evidentiary data that the log message includes, and 3) the segment(s) of the string pattern that contains the value of a certain type of evidentiary data, where we represent a string pattern using a deterministic finite-state automaton. In the online phase, given a log message from a suspect's Android device, we match the log message against the string patterns in the ALED and extract evidentiary data from it if the matching succeeds. We evaluate LogExtractor on 65 benchmark apps from DroidBench and 12.1 K real-world apps. Our results show that a large number of apps write a diverse set of data to the logging system and LogExtractor can accurately extract them.
SRH-Net: Stacked Recurrent Hourglass Network for Stereo Matching

The cost aggregation strategy shows a crucialrole in learning-based stereo matching tasks, where 3D convolutional filters obtain state of the art but require intensive computation resources, while 2D operations need less GPU memory but are sensitive to domain shift. In this letter, we decouple the 4D cubic cost volume used by 3D convolutional filters into sequential cost maps along the direction of disparity instead of dealing with it at once by exploiting a recurrent cost aggregation strategy. Furthermore, a novel recurrent module, Stacked Recurrent Hourglass (SRH), is proposed to process each cost map. Our hourglass network is constructed based on Gated Recurrent Units (GRUs) and down/upsampling layers, which provides GRUs larger receptive fields. Then two hourglass networks are stacked together, while multi-scale information is processed by skip connections to enhance the performance of the pipeline in textureless areas. The proposed architecture is implemented in an end-to-end pipeline and evaluated on public datasets, which reduces GPU memory consumption by up to 56.1% compared with PSMNet using stacked hourglass 3D CNNs without the degradation of accuracy. Then, we further demonstrate the scalability of the proposed method on several high-resolution pairs, while previously learned approaches often fail due to the memory constraint. The code is released at https://github.com/hongzhidu/SRHNet.
AutoML

Machine learning methods have been adopted for various real-world applications, ranging from social networks, online image/video-sharing platforms, and e-commerce to education, healthcare, etc. However, several components of machine learning methods, including data representation, hyperparameter and model architecture, can largely affect their performance in practice. Moreover, the explosions of data scale and model size make the optimization of these components more and more time-consuming for machine learning developers. To tackle these challenges, Automated Machine Learning (AutoML) aims to automate the process of applying machine learning methods to solve real-world application tasks, reducing the time of tuning machine learning methods while maintaining good performance. In this tutorial, we will introduce the main research topics of AutoML, including Hyperparameter Optimization, Neural Architecture Search and Meta-Learning. Two emerging topics of AutoML, DNN-based Feature Generation and Machine Learning Guided Database, will also be discussed as they are important components for real-world applications. For each topic, we will motivate it with examples from industry, illustrate the state-of-the-art methods, and discuss their pros and cons from both perspectives of industry and academy. We will also discuss some future research directions based on our experience from industry and the trends in academy.
Building Open Source Cyber Range To Teach Cyber Security

This paper deals with the use of cyber range in education to teach cybersecurity. Particular attention is paid exclusively to open-source solutions, as such solutions are available to the general public, which is essential in raising awareness of cyber defense. First,the available open-source cyber ranges are described, their advantages and disadvantages. Subsequently, it presents our selected solution, the procedure of implementation in the Brno University of Technology laboratory, our use in our study programe and stress testing of selected cyber range. Last but not least, it provides a unique guide to designing and building own open-source cyber range LAB from scratch.
Topology-Aware Differential Privacy for Decentralized Image Classification

Image classification is a fundamental artificial intelligence task that labels images into one of some predefined classes. However, training complex image classification models requires a large amount of computation resources and data in order to reach state-of-the-art performance. This demand drives the growth of distributed deep learning, where multiple agents cooperatively train global models with their individual datasets. Among such learning systems, decentralized learning is particularly attractive, as it can improve the efficiency and fault tolerance by eliminating the centralized parameter server, which could be the single point of failure or performance bottleneck. Although the agents do not need to disclose their training image samples, they exchange parameters with each other at each iteration, which can put them at the risk of data privacy leakage. Past works demonstrated the possibility of recovering training images from the exchanged parameters. One common defense direction is to adopt Differential Privacy (DP) to secure the optimization algorithms such as Stochastic Gradient Descent (SGD). Those DP-based methods mainly focus on standalone systems, or centralized distributed learning. How to enforce and optimize DP protection in decentralized learning systems is unknown and challenging, due to their complex communication topologies and distinct learning characteristics. In this paper, we design TOP- DP, a novel solution to optimize the differential privacy protection of decentralized image classification systems. The key insight of our solution is to leverage the unique features of decentralized communication topologies to reduce the noise scale and improve the model usability. (1) We enhance the DP-SGD algorithm with this <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topology-aware</i> noise reduction strategy, and integrate the time-aware noise decay technique. (2) We design two novel learning protocols (synchronous and asynchronous) to protect systems with different network connectivities and topologies. We formally analyze and prove the DP requirement of our proposed solutions. Experimental evaluations demonstrate that our solution achieves a better trade-off between usability and privacy than prior works. To the best of our knowledge, this is the first DP optimization work from the perspective of network topologies.
A Case for Sampling-Based Learning Techniques in Coflow Scheduling

Coflow scheduling improves data-intensive application performance by improving their networking performance. State-of-the-art online coflow schedulers in essence approximate the classic Shortest-Job-First (SJF) scheduling by learning the coflow <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">size</i> online. In particular, they use multiple priority queues to simultaneously accomplish two goals: to sieve long coflows from short coflows, and to schedule short coflows with high priorities. Such a mechanism pays high overhead in learning the coflow size: moving a large coflow across the queues delays small and other large coflows, and moving similar-sized coflows across the queues results in inadvertent round-robin scheduling. We propose Philae, a new online coflow scheduler that exploits the spatial dimension of coflows, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> a coflow has many flows, to drastically reduce the overhead of coflow size <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning</i> . Philae pre-schedules sampled flows of each coflow and uses their sizes to estimate the average flow size of the coflow. It then resorts to Shortest Coflow First, where the notion of shortest is determined using the learned coflow sizes and coflow contention. We show that the sampling-based learning is robust to flow size skew and has the added benefit of much improved scalability from reduced coordinator-local agent interactions. Our evaluation using an Azure testbed, a publicly available production cluster trace from Facebook shows that compared to the prior art Aalo, Philae reduces the coflow completion time (CCT) in average (P90) cases by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.50\times $ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.00\times $ </tex-math></inline-formula> ) on a 150-node testbed and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.72\times $ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.78\times $ </tex-math></inline-formula> ) on a 900-node testbed. Evaluation using additional traces further demonstrates Philae’s robustness to flow size skew.
Meta-Information in Conversational Search

The exchange of meta-information has always formed part of information behavior. In this article, we show that this rule also extends to conversational search. Information about the user’s information need, their preferences, and the quality of search results are only some of the most salient examples of meta-information that are exchanged as a matter of course in a search conversation. To understand the importance of meta-information for conversational search, we revisit its definition and survey how meta-information has been taken into account in the past in information retrieval. Meta-information has gone by many names, about which a concise overview is provided. An in-depth analysis of the role of meta-information in search and conversation theories reveals that they provide significant support for the importance of meta-information in conversational search. We further identify conversational search datasets are suitable for a deeper inspection with regard to meta-information, namely, Spoken Conversational Search and Microsoft Information-Seeking Conversations. A quantitative data analysis demonstrates the practical significance of meta-information in information-seeking conversations, whereas a qualitative analysis shows the effects of exchanging different types. Finally, we discuss practical applications and challenges of meta-information in conversational search, including a case study of VERSE, an existing search system for the visually impaired.
HAMLET

Abstract Automatic term extraction (ATE) is an important task within natural language processing, both separately, and as a preprocessing step for other tasks. In recent years, research has moved far beyond the traditional hybrid approach where candidate terms are extracted based on part-of-speech patterns and filtered and sorted with statistical termhood and unithood measures. While there has been an explosion of different types of features and algorithms, including machine learning methodologies, some of the fundamental problems remain unsolved, such as the ambiguous nature of the concept “term”. This has been a hurdle in the creation of data for ATE, meaning that datasets for both training and testing are scarce, and system evaluations are often limited and rarely cover multiple languages and domains. The ACTER Annotated Corpora for Term Extraction Research contain manual term annotations in four domains and three languages and have been used to investigate a supervised machine learning approach for ATE, using a binary random forest classifier with multiple types of features. The resulting system (HAMLET Hybrid Adaptable Machine Learning approach to Extract Terminology) provides detailed insights into its strengths and weaknesses. It highlights a certain unpredictability as an important drawback of machine learning methodologies, but also shows how the system appears to have learnt a robust definition of terms, producing results that are state-of-the-art, and contain few errors that are not (part of) terms in any way. Both the amount and the relevance of the training data have a substantial effect on results, and by varying the training data, it appears to be possible to adapt the system to various desired outputs, e.g., different types of terms. While certain issues remain difficult – such as the extraction of rare terms and multiword terms – this study shows how supervised machine learning is a promising methodology for ATE.
A lexicographic cooperative co-evolutionary approach for feature selection

This paper starts with two hypotheses. The first one is that the simultaneous optimization of the hyperparameters regulating the classifier within a wrapper method, while the best subset of features is being determined, should improve the results with respect to those obtained with a pre-parameterized classifier. The second one is that solving these two problems can be formulated as a lexicographic optimization problem, allowing the use of a simple single-objective evolutionary algorithm to solve this multi-objective problem. The fitness function is of key importance for such wrapper methods. It is responsible for guiding the search towards potentially good solutions and it also consumes most of the runtime. Having these issues in mind, this paper also proposes a new lexicographic fitness function, designed to minimize the runtime of the algorithm and also to avoid over-fitting. Furthermore, the execution time and the quality of the results obtained by the wrapper procedure also depend on some algorithmic hyperparameters: the similarity thresholds used when comparing two different solutions lexicographically and the percentage of data samples used for validation during the training process. Thus, an experimental analysis has been carried out to find adequate values for these hyperparameters. Finally, the lexicographic cooperative co-evolutionary wrapper approach, using the new fitness function proposed in this paper, has been tested with several datasets belonging to the University of California, Irvine (UCI) repository and also with some real high-dimensional datasets, obtaining quite good results, compared to other state-of-the-art wrapper methods. The comparison has also been made lexicographically, with a new methodology proposed in this paper.
Deep Attentional Bidirectional LSTM for Arabic Sentiment Analysis In Twitter

Along with the emergence of user reviews, emotions, feedback, and opinions in social networks towards a specific topic, product, event, or such a service. Sentiment Analysis has recently considered one of the fundamental research areas which lie at the intersection of numerous fields of research include data mining, computational linguistics, and Natural Language Processing (NLP). It concerns classifying a given piece of writing into sentiment polarity. Furthermore, deep learning has shown rich data modelling abilities to deal with complex and large datasets, in addition, it is recognized as the state-of-the-art based approach for different research fields. Although, the current state-of-the-art sentiment analysis tailored to the Arabic language still needs improvements because of its morphological richness, ambiguity, and lack of its resources. To advance this task, a novel Attentional Bidirectional LSTM architecture was proposed in order to determine richer semantic information and to extract the contextual information in both directions. We also investigated the effect of the word2vec pre-trained model to produce the word embeddings representation and to capture semantic information from Arabic tweets. To validate the performance of the proposed architecture, we assessed it in a holistic setting across three benchmark Arabic sentiment tweets datasets. Thus, the experimental results demonstrate that the proposed architecture outperforms the current state-of-the-art deep learning-based methods. Besides, it performs well compared with the baseline classical machine learning methods.
KYC Optimization by Blockchain Based Hyperledger Fabric Network

in financial institutions, the traditional Know Your Customer (KYC) process is insecure, and costly. The adoption of disruptive technology is a necessary condition for the coming future of financial institutions. This study proposed a Hyperledger Fabric network for KYC optimization. The Performance of the proposed system was tested by using the Hyperledger Composer. The experiment results confirm that due to the strong and identity features of Hyperledger Fabric, the proposed system can speed up the KYC clearing transfers, challenge the inefficiencies that arise from duplicated conduct of similar tasks, secure data sharing, cost-effective, and ultimately bring transparency in the traditional KYC system.
Homomorphic Two Tier Reversible Data Hiding In Encrypted 3D Objects

Today, 3D objects are an increasingly popular form of media.It has become necessary to secure them during their transmission or archiving.In this paper, we propose a two tier reversible data hiding method for 3D objects in the encrypted domain.Based on the homomorphic properties of the Paillier cryptosystem, our proposed method embeds a first tier message in the encrypted domain which can be extracted in either the encrypted domain or the clear domain.Indeed, our method produces a marked 3D object which is visually very similar to the original object.It seeks to be format compliant and to preserve the original size of the data, without the need for an auxiliary file.Moreover, large keys are used, rending our method secure for real life applications.
Developing and Validating a Behavioural Model of Cyberinsurance Adoption

Business disruption from cyberattacks is a growing concern, yet cyberinsurance uptake remains low. Using an online behavioural economics experiment with 4800 participants across four EU countries, this study tests a predictive model of cyberinsurance adoption, incorporating elements of Protection Motivation Theory (PMT) and the Theory of Planned Behaviour (TPB) as well as factors in relation to risk propensity and price. During the experiment, participants were given the opportunity to purchase different cybersecurity measures and cyberinsurance products before performing an online task. Participants likelihood of suffering a cyberattack was dependent upon their adoption of cybersecurity measures and their behaviour during the online task. The consequences of any attack were dependent upon the participants insurance decisions. Structural equation modelling was applied and the model was further developed to include elements of the wider security ecosystem. The final model shows that all TPB factors, and response efficacy from the PMT, positively predicted adoption of premium cyberinsurance. Interestingly, adoption of cybersecurity measures was associated with safer behaviour online, contrary to concerns of “moral hazard”. The findings highlight the need to consider the larger cybersecurity ecosystem when designing interventions to increase adoption of cyberinsurance and/or promote more secure online behaviour.
Ransomware: a growing geopolitical threat

Ransomware attacks have become a major geopolitical issue. The recent 2021 G7 summit in Cornwall issued a final communiqué calling on Russia to, "hold to account those within its borders who conduct ransomware attacks," while also committing the G7 to collaborate in order, "to urgently address the escalating shared threat".