Reviewerly/exHarmony huggingface dataset:
Personalizing Natural Language Understanding using Multi-armed Bandits and Implicit Feedback

Natural Language Understanding (NLU) models on voice-controlled speakers face several challenges. In particular, music streaming services have large catalogs, often containing millions of songs, artists, and albums and several thousands of custom playlists and stations. In many cases there is ambiguity and little structural difference between carrier phrases and entity names. In this work, we describe how we leveraged multi-armed bandits in combination with implicit customer feedback to improve accuracy and personalization of responses to voice request in the music domain. Our models are tested in a large-scale industrial system containing several other components. In particular, we focused on using this technology to correct errors made by upstream NLU models and personalize responses based on customer preferences and music provider functionality. The models resulted in significant improvement of playback rate for Amazon Music and are deployed in systems serving several countries and languages. We further used the implicit feedback of the customers to generate weakly labeled training data for the NLU models. This improved the experience for customers using other music providers on all Alexa devices.
Software Features Prioritization based on Stakeholders’ Satisfaction/Dissatisfaction and Hesitation

In this paper we present a practical method that can be applied to support the prioritization of large sets of candidate software features in a requirements prioritization process. We consider as prioritization criteria the satisfaction/dissatisfaction of users from offering/not offering software features as part of an upcoming software release. There is often an asymmetry between users' satisfaction and dissatisfaction when these two factors are considered as prioritization criteria of features: some features may generate satisfaction to users, if included in the next software release, but do not create the same value of dissatisfaction, if they are not included, and vice versa. This asymmetry may introduce additional hesitation and uncertainty to stakeholders when they adopt satisfaction/dissatisfaction as prioritization criteria. The suggested method initially requires from stakeholders to systematically rank all candidate features based on satisfaction and dissatisfaction criteria. Then, the method is used to quantify the hesitation of stakeholders that is inherent in each features ranking. The final features' priorities are computed by calculating objective weights for all stakeholders' rankings. The method assumes the larger the hesitation (lack of knowledge and indeterminacy) associated with each stakeholder ranking, the smaller will be the weight of that ranking in the calculation of the final features' priorities.
Sharing is Caring: A collaborative framework for sharing security alerts

Collaboration is a keystone of defense in the field of cybersecurity. A collaborative detection system allows multiple collaborators or service providers to share their security-incident-response data, in order to effectively identify and isolate stealthy malicious actors who hide their traffic under the umbrella of legitimate Internet data transmissions. The fundamental challenge in the design of a collaborative system is ensuring the privacy of collaborators in a decentralized setting without incurring substantial computation and communication overheads. In this paper, we use healthcare as a case study and present Sharing Is Caring (SIC), a framework that allows multiple healthcare organizations to share their security defense and attack data with other organizations for the collaborative defense against common attackers without compromising the privacy of their system configurations and user data. The SIC framework ensures two essential properties: (1) it ensures that no party should learn how a particular healthcare organization has reacted to suspected IP addresses, attacks or security incidents; and (2) it performs operations in a decentralized setting, without relying on a trusted third party. We provide an analysis of the privacy and security properties of our framework against honest-but-curious as well as malicious players. We prototype the proposed system and evaluate its performance in terms of computation time and communication bandwidth. The reasonable computation cost and bandwidth overhead make the SIC framework a feasible choice for the privacy-preserving exchange of security information among the collaborating healthcare organizations.
Librarian perspectives on the role of virtual reality in public libraries

Abstract This paper explores librarians’ perception of virtual reality as well as opportunities and challenges associated with implementing VR in public libraries. We interviewed 36 librarians who developed and offered VR programs as part of the research project, before and after the programming. The findings show how VR may be used in the public library as a learning tool and technology that encourages social interactions. Librarians discuss how the unique qualities of VR such as immersion and interactivity of VR makes it different from other digital media and present a different set of potential challenges when offered in the library. Librarians observed that while VR has a lot of potential as a technology for learning and social engagement, the success also largely depends on the VR content as well as the librarian's strategy for recruitment and promotion. In addition, we found that librarians had different understandings of what constitutes learning as well as how marginalized communities might benefit from this technology. The librarians we interviewed faced many challenges, however, our analysis of their experiences offers insight into designing successful VR programming in public libraries.
Argument annotation and analysis using deep learning with attention mechanism in Bahasa Indonesia

Abstract Argumentation mining is a research field which focuses on sentences in type of argumentation. Argumentative sentences are often used in daily communication and have important role in each decision or conclusion making process. The research objective is to do observation in deep learning utilization combined with attention mechanism for argument annotation and analysis. Argument annotation is argument component classification from certain discourse to several classes. Classes include major claim, claim, premise and non-argumentative. Argument analysis points to argumentation characteristics and validity which are arranged into one topic. One of the analysis is about how to assess whether an established argument is categorized as sufficient or not. Dataset used for argument annotation and analysis is 402 persuasive essays. This data is translated into Bahasa Indonesia (mother tongue of Indonesia) to give overview about how it works with specific language other than English. Several deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit) are utilized for argument annotation and analysis while HAN (Hierarchical Attention Network) is utilized only for argument analysis. Attention mechanism is combined with the model as weighted access setter for a better performance. From the whole experiments, combination of deep learning and attention mechanism for argument annotation and analysis arrives in a better result compared with previous research.
Identifying Political Sentiments on YouTube: A Systematic Comparison Regarding the Accuracy of Recurrent Neural Network and Machine Learning Models

Abstract Since social media have increasingly become forums to exchange personal opinions, more and more approaches have been suggested to analyze those sentiments automatically. Neural networks and traditional machine learning methods allow individual adaption by training the data, tailoring the algorithm to the particular topic that is discussed. Still, a great number of methodological combinations involving algorithms (e.g., recurrent neural networks (RNN)), techniques (e.g., word2vec), and methods (e.g., Skip-Gram) are possible. This work offers a systematic comparison of sentiment analytical approaches using different word embeddings with RNN architectures and traditional machine learning techniques. Using German comments of controversial political discussions on YouTube, this study uses metrics such as F1-score, precision and recall to compare the quality of performance of different approaches. First results show that deep neural networks outperform multiclass prediction with small datasets in contrast to traditional machine learning models with word embeddings.
Knowledge is power: An analysis of discussions on hacking forums

There remains a lack of understanding on the social factors that influence the behaviours and beliefs of people who have an interest in hacking. This research sought to address that gap by exploring the conversations that take place on hacking forums and subreddits. Text in hacking related threads was collected from these sites over a period of several months. Linguistic Inquiry and Word Count (LIWC) software was used to determine the linguistic characteristics of each forum/ subreddit. Thematic analysis was then conducted on a sub-set of text from each source. The results of the LIWC analysis indicated that there are variations in several psychologically relevant factors between these forums and subreddits, including the degree to which users used language that indicated they were being honest, confident, analytical and emotional. There were several results that were inconsistent with stereotypes of hackers, such as a relative absence of language indicating anger. The thematic analysis identified several themes relating to knowledge, skills acquisition, honesty legality and risk. Overall, this research demonstrates that there exists an established online community of hackers, which are likely to be encountered by any young person who becomes interested in cybersecurity and hacking. These communities may potentially act as an important source of social support and social identity for their members. Understanding the dynamics of these communities may better help us steer people towards legitimate cybersecurity careers, where their passion and skills can be used for societal good.
Twitter Sentiment Data Analysis of User Behavior on Cryptocurrencies

Social networks such as Twitter contain billions of data of users, and in every second, a large number of tweets trade through Twitter. Sentiment analysis is the way toward deciding the emotional tone behind a series of words that users utilize to understand the attitudes, thoughts, and emotions that are enunciated in online references on Twitter. This chapter aims to determine the user preference of Bitcoin and Ethereum, which are the two most popular cryptocurrencies in the world by using the Twitter sentiment analysis. It proposes a powerful and fundamental approach to identify emotions on Twitter by considering the tweets of these two distinctive cryptocurrencies. One hundred twenty thousand (120,000) tweets were extracted separately from Twitter for each keyword Bitcoin/BTC and Bitcoin/ETC between the period from 12/09/2018 to 22/09/2018 (10 days).
Kernelized dual regression incorporating local information for image set classification

In image set classification, dual linear regression classification (DLRC) has shown the excellent performance on face image data without the interference of the complex background. However, DLRC could not well identify the data set with the complex background. The complex background means that the background is cluttered and the viewpoint is unusual or the object is partially occluded. This paper proposes a new model, kernelized dual regression (KDR), based on DLRC and the kernel trick which is a useful technique in image classification. Different from DLRC, KDR adopts a block partitioning strategy to extract the local information, which is able to conquer the shortcoming of DLRC. To capture the nonlinear relationship between the training set and test set, KDR tactfully maps these image sets into a high-dimensional feature space by adopting the nonlinear mapping associated with the Gaussian kernel function. In the reproducing kernel Hilbert space (RKHS), KDR can find the joint coefficients by minimizing the distance between training set and test set, and has a closed-form solution. Extensive experiments on four datasets show that KDR could achieve better classification performance than that of DLRC and other existing methods.
Incremental migration to software product line engineering

Current market developments require organizations to produce high-quality tailored software variants while simultaneously coping with increased software complexity. Software Product Line Engineering (SPLE) is a well-known approach to manage this variability and complexity, however no step-wise migration process is available allowing a co-existence of SPLE along with established development processes. In this paper, we introduce an incremental SPLE migration strategy and process starting from using the feature model as synchronized product and variant documentation. They can be applied as a first step of SPLE migration along with the continuous software development cycle. We performed initial steps of the process on industrial low voltage drive embedded firmware spanning around few millions lines of code using a commercial SPLE tool and validated short-term benefits by means of stakeholder feedback.
The FESS Algorithm: A Feature Based Approach to Single-Agent Search

This paper introduces the Feature Space Search algorithm (FESS) that projects a single-agent application search space onto an abstract space that is defined by features of the domain. FESS works in this smaller space by directing the search from the projected initial state towards the projected final state. A state transition in the feature space maps to one or (usually) many moves in the domain space. Each of these transitions leads to a change in one or more features, with the FESS algorithm favoring transitions that make progress towards the projected final state. Each feature can be thought of as being a heuristic, and FESS is providing multi-objective guidance from the feature space to the search in the application domain.FESS is demonstrated using the challenging single-agent problem of Sokoban. For over twenty years, numerous approaches have been used to try solving the 90 problems in the standard benchmark test suite. FESS, using a set of domain-specific features, is the first program that solves all 90 problems. Further, although the experimental standard is to allocate 10 minutes of solving time per problem (900 minutes), the FESS approach solves the entire test set in less than 4 minutes.
Chapter 6. Double trouble

Linguistic data from social media is often discussed in the light of constraints, be it due to character limits or the putatively short attention span of users. In November 2017, Twitter eased one such constraint, increasing the maximum length of tweets from 140 to 280 characters. The present longitudinal study shows that this change has had an effect on the linguistic surface structure of tweets, especially in regard to optional syntactical and metatextual features. I discuss the origin and nature of these changes and their relation to hard constraints of social media platforms in addition to highlighting the impact on methodological aspects of future longitudinal studies of Twitter data which may cover periods both before and after the switch.
LASO: Exploiting Locomotive and Acoustic Signatures over the Edge to Annotate IMU Data for Human Activity Recognition

Annotated IMU sensor data from smart devices and wearables are essential for developing supervised models for fine-grained human activity recognition, albeit generating sufficient annotated data for diverse human activities under different environments is challenging. Existing approaches primarily use human-in-the-loop based techniques, including active learning; however, they are tedious, costly, and time-consuming. Leveraging the availability of acoustic data from embedded microphones over the data collection devices, in this paper, we propose LASO, a multimodal approach for automated data annotation from acoustic and locomotive information. LASO works over the edge device itself, ensuring that only the annotated IMU data is collected, discarding the acoustic data from the device itself, hence preserving the audio-privacy of the user. In the absence of any pre-existing labeling information, such an auto-annotation is challenging as the IMU data needs to be sessionized for different time-scaled activities in a completely unsupervised manner. We use a change-point detection technique while synchronizing the locomotive information from the IMU data with the acoustic data, and then use pre-trained audio-based activity recognition models for labeling the IMU data while handling the acoustic noises. LASO efficiently annotates IMU data, without any explicit human intervention, with a mean accuracy of $0.93$ ($\pm 0.04$) and $0.78$ ($\pm 0.05$) for two different real-life datasets from workshop and kitchen environments, respectively.
Health Vulnerability versus Economic Resilience to the COVID-19 pandemic: Global Evidence

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Machine Learning Based Spam E-mail Detection System for Turkish

Electronic mail is a digital letter sent over the internet. All types of files such as documents, pictures, music, videos can be attached to emails and transferred to the recipient's computer. E-mails, which are preferred due to their cheapness and ease, are sent to billions of people every year. Email is an effective way of communication as it saves time and money, hence it has become the most used communication tool in personal and professional communication. Emails are actively used by people or communities who want to make propaganda, advertising, phishing because of their ease of use and low cost. People or communities who want to achieve their goals send unnecessary and unsolicited mail to the e-mail accounts they never knew. These mails cause serious material and moral damages to internet users and also weaken internet traffic. Spam e-mail is a method that is sent to the recipient without his consent and that is generally used by malicious or promotional purposes. The purpose of spammers is to encourage computer users to purchase legal or prohibited products and services. Existing spam blocking methods often lag behind innovations that spammers constantly bring, so machine learning-based spam detection methods emerge. In this study, it is provided to detect spam by using 7 different machine learning methods on 800 Turkish e-mail datasets. In the developed method, when the feature selection is made with the chi-square test, the best result is obtained from the Sequential Minimal Optimization (SMO) algorithm. When the feature selection is made with the information gain method, the best result is obtained from the Multi Layer Perceptron (MLP) algorithm. Performance results obtained from SMO and MLP algorithms are 0.985 and 0.984 according to F-measure, respectively.
A Confidence-Calibrated MOBA Game Winner Predictor

In this paper, we propose a confidence-calibration method for predicting the winner of a famous multiplayer online battle arena (MOBA) game, League of Legends. In MOBA games, the dataset may contain a large amount of input-dependent noise; not all of such noise is observable. Hence, it is desirable to attempt a confidence-calibrated prediction. Unfortunately, most existing confidence calibration methods are pertaining to image and document classification tasks where consideration on uncertainty is not crucial. In this paper, we propose a novel calibration method that takes data uncertainty into consideration. The proposed method achieves an outstanding expected calibration error (ECE) (0.57%) mainly owing to data uncertainty consideration, compared to a conventional temperature scaling method of which ECE value is 1.11%.
Dynamically Growing Neural Network Architecture for Lifelong Deep Learning on the Edge

Conventional deep learning models are trained once and deployed. However, models deployed in agents operating in dynamic environments need to constantly acquire new knowledge, while preventing catastrophic forgetting of previous knowledge. This ability is commonly referred to as lifelong learning. In this paper, we address the performance and resource challenges for realizing lifelong learning on edge devices. We propose a FPGA based architecture for a Self-Organization Neural Network (SONN), that in combination with a Convolutional Neural Network (CNN) can perform class-incremental lifelong learning for object classification. The proposed SONN architecture is capable of performing unsupervised learning on input features from the CNN by dynamically growing neurons and connections. In order to meet the tight constraints of edge computing, we introduce efficient scheduling methods to maximize resource reuse and parallelism, as well as approximate computing strategies. Experiments based on the Core50 dataset for continuous object recognition from video sequences demonstrated that the proposed FPGA architecture significantly outperforms CPU and GPU based implementations.
Potentially toxic element accumulation in badgers (Meles meles): a compositional approach

Potentially Toxic Elements (PTEs) in Badgers (Meles meles), otherwise known as heavy metals, are unique amongst environmental pollutants occurring, both naturally and anthropogenically. PTEs have a broad range of negative health and environmental effects, therefore identifying their sources and pathways through the environment is imperative for public health policy. This is difficult in terrestrial systems due to the compositional nature of soil geochemistry. In this study, a compositional statistical approach was used to identify how PTEs accumulate in a terrestrial carnivorous mammal, Eurasian Badgers (Meles meles). Compositional principal component analysis (PCA) was used on geochemical data from the Tellus survey, the soil baseline and badger tissue data to map geo-spatial patterns of PTEs and show accumulative trends measured in time. Mapping PCs identified distinct regions of PTE presence in soil and PTE accumulation in badger tissues in Northern Ireland. PTEs were most elevated in liver, kidney and then muscle tissues. Liver and kidney showed the most distinct geo-spatial patterns of accumulation and muscle was the most depleted. PC1 and 2 for each type were modelled using generalised additive mixed models (GAMM) to identify trends through time. PC1 for the liver and muscle were associated with rainfall and ∂N15 in the liver, showing a link to diet and a bioaccumulation pathway, whilst PC2 for both tissues was associated with mean temperature, showing a link to seasonal activity and a bioaccessibility pathway. However, in kidney tissue these trends are reversed and PC1 was associated with bioaccessibility and PC2 with bioaccumulation. Combined these techniques can elucidate both geo-spatial trends in PTEs and the mechanisms by which they move in environment and in future may be an effective tool for assessing PTE bioavailability in environmental health surveys.