Reviewerly/exHarmony huggingface dataset:
On modeling blockchain-enabled economic networks as stochastic dynamical systems

Abstract Blockchain networks have attracted tremendous attention for creating cryptocurrencies and decentralized economies built on peer-to-peer protocols. However, the complex nature of the dynamics and feedback mechanisms within these economic networks has rendered it difficult to reason about the growth and evolution of these networks. Hence, proper mathematical frameworks to model and analyze the behavior of blockchain-enabled networks are essential. To address this need, we establish a formal mathematical framework, based on dynamical systems, to model the core concepts in blockchain-enabled economies. Drawing on concepts from differential games, control engineering, and stochastic dynamical systems, this paper proposes a methodology to model, simulate, and engineer networked token economies. To illustrate our framework, a model of a generalized token economy is developed, where miners provide a commodity service to a platform in exchange for a cryptocurrency and users consume a service from the platform. We illustrate the dynamics of token economies by simulating and testing two different block reward strategies. We then conclude by outlining future research directions that will integrate additional methods from signal processing and control theory into the toolkit for designers of blockchain-enabled economic systems.
Trust-Based Human-Machine Collaboration Mechanism for Predicting Crimes

In today's world, with the shifting nature of artificial intelligence (AI) to explainable AI, which involves humans and machines working and complementing each other, there is a need for a mechanism to govern their collaboration. We have proposed a trust-based mechanism to manage collaboration between them. Our trust-based mechanism has the ability to quantify human trust into a mathematical model. The proposed trust-based framework will facilitate decision making when humans and machines are involved in a process. This framework will ensure that either of them never under trust or over trust each other by computing trust information based on their history. To validate our proposed framework, experiments are performed on Indianapolis Crime Data which contains actual crime information, machine predictions, and police feedback. Results have shown that how the trust of both entities can impact the decision making of the police towards machine predictions.
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations

Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are related. A unified view on these methods has been missing. We present the generalized SIPA (sampling, intervention, prediction, aggregation) framework of work stages for model-agnostic interpretations and demonstrate how several prominent methods for feature effects can be embedded into the proposed framework. Furthermore, we extend the framework to feature importance computations by pointing out how variance-based and performance-based importance measures are based on the same work stages. The SIPA framework reduces the diverse set of model-agnostic techniques to a single methodology and establishes a common terminology to discuss them in future work.
Margin-Based Deep Learning Networks for Human Activity Recognition

Human activity recognition (HAR) is a popular and challenging research topic, driven by a variety of applications. More recently, with significant progress in the development of deep learning networks for classification tasks, many researchers have made use of such models to recognise human activities in a sensor-based manner, which have achieved good performance. However, sensor-based HAR still faces challenges; in particular, recognising similar activities that only have a different sequentiality and similarly classifying activities with large inter-personal variability. This means that some human activities have large intra-class scatter and small inter-class separation. To deal with this problem, we introduce a margin mechanism to enhance the discriminative power of deep learning networks. We modified four kinds of common neural networks with our margin mechanism to test the effectiveness of our proposed method. The experimental results demonstrate that the margin-based models outperform the unmodified models on the OPPORTUNITY, UniMiB-SHAR, and PAMAP2 datasets. We also extend our research to the problem of open-set human activity recognition and evaluate the proposed method's performance in recognising new human activities.
Role of sentiment analysis in social media security and analytics

Abstract Social media, in recent times, has with eased an explosion of data with so many social media platforms available to interact and express opinions freely. This has led to easy access to the privacy of social media users which raise broader security concerns and issues. The present paper provides an overview of various sentiment analysis approaches and techniques for social media security and analytics. The multiple security application domains like deception detection, anomaly detection, risk management, and disaster relief have been identified where sentiment analysis is used for social media security. An in‐depth study on security issues related to data provenance, distrust, e‐commerce security, consumer security breaches, market surveillance, credibility, and risk assessment in social media have been presented. A comparison of various techniques, methodologies, dataset, and application domain where sentiment analysis is used has been discussed. The present work discusses the results of different machine learning techniques based on the performance metrics that have been used for the implementation of sentiment analysis in the respective security domains. It identifies the various gaps, issues, and the recent advancements in the field and presents a line of work that needs to be carried forward in future. This article is categorized under: Commercial, Legal, and Ethical Issues &gt; Security and Privacy Technologies &gt; Machine Learning Technologies &gt; Prediction
ThuMouse: A Micro-gesture Cursor Input through mmWave Radar-based Interaction

In this paper, we propose ThuMouse, a novel interaction paradigm aimed to create a gesture-based and touch-free cursor interaction that accurately tracks the motion of fingers in real-time. ThuMouse enables users to move the cursor using frequency-modulated continuous-wave (FMCW) radar. While previous work with FMCW radar in human-computer-interfaces (HCI) has focused on classifying a set of predefined hand gestures, ThuMouse regressively tracks the position of a finger, which allows for finer-grained interaction. This paper presents the gesture sensing pipeline we built, with regressive tracking through deep neural networks, data augmentation for robustness, and computer vision as a training base. We also report on a proof-of-concept demonstration shows how our system can function as a mouse, and identify areas for future work. This work builds a foundation for designing finer micro gesture-based interactions, allowing the finger to emulate external input devices such as a joystick and touch-pad.
Deep Learning-Based Fake-Banknote Detection for the Visually Impaired People Using Visible-Light Images Captured by Smartphone Cameras

Automatic recognition of fake banknotes is an important task in practical banknote handling.Research on this task has mostly involved methods applied to automatic sorting machines with multiple imaging sensors or that use specialized sensors for capturing banknote images in various light wavelengths.These approaches can make use of the security features on banknotes for counterfeit detection.However, they require specialized devices, which are not always available for general users or visually impaired people.Meanwhile, smartphones are becoming more popular and can be useful imaging devices.Moreover, the types of fake banknotes created by imaging devices such as smartphone cameras or scanners are sometimes cannot be recognized by especially the visually impaired people.Addressing these problems, we propose a method for classifying fake and genuine banknotes using visible-light images captured by smartphone cameras based on convolutional neural networks.Experimental results on a self-collected dataset of US dollar, Euro, Korean won, and Jordanian dinar banknotes showed that our method performs better in terms of fake detection than the state-of-the-art methods.
AUSearch: Accurate API Usage Search in GitHub Repositories with Type Resolution

Nowadays, developers use APIs to implement their applications. To know how to use an API, developers may search for code examples that use the API in repositories such as GitHub. Although code search engines have been developed to help developers perform such search, these engines typically only accept a query containing the description of the task that needs to be implemented or the names of the APIs that the developer wants to use without the capability for the developer to specify particular search constraints, such as the class and parameter types that the relevant API should take. These engines are not designed for cases when the specific API and its types to search are known and the developers want code examples that show how the specific API is used, and therefore, their search results are often too inaccurate. In this work, we propose a tool, named AUSearch, to fill this gap. Given an API query that allows type constraints, AUSearch finds code examples in GitHub that contain usages of the specific APIs in the query. AUSearch performs type resolutions to ensure that the API usages found in the returned files are indeed invocations of the APIs specified in the query and highlights the relevant lines of code in the files for easier reference. We show that AUSearch is much more accurate than GitHub Code Search. A video demonstrating our tool is available from https://youtu.be/DKiGal5bSkU.
RHINE: Relation Structure-Aware Heterogeneous Information Network Embedding

Heterogeneous information network (HIN) embedding aims to learn the low-dimensional representations of nodes while preserving structures and semantics in HINs. Although most existing methods consider heterogeneous relations and achieve promising performance, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of HIN embedding. In this paper, we argue that heterogeneous relations have different structural characteristics, and propose a novel Relation structure-aware HIN Embedding model, called RHINE. By exploring four real-world networks with thorough analysis, we present two structure-related measures which consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive structural characteristics of relations, in RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures in HINs. Finally, we combine and optimize these models in a unified manner. Furthermore, considering that nodes connected via heterogeneous relations may have multi-aspect semantics and each relation focuses on one aspect, we introduce relation-specific projection matrices to learn node and relation embeddings in separate spaces rather than a common space, which can better preserve the semantics in HINs, referring to a new model RHINE-M. Experiments on four real-world datasets demonstrate that our models significantly outperform the state-of-the-art methods in four tasks.
On the Adoption of Kotlin on Android Development: A Triangulation Study

In 2017, Google announced Kotlin as one of the officially supported languages for Android development. Among the reasons for choosing Kotlin, Google mentioned it is “concise, expressive, and designed to be type and null-safe”. Another important reason is that Kotlin is a language fully interoperable with Java and runs on the JVM. Despite Kotlin's rapid rise in the industry, very little has been done in academia to understand how developers are dealing with the adoption of Kotlin. The goal of this study is to understand how developers are dealing with the recent adoption of Kotlin as an official language for Android development, their perception about the advantages and disadvantages related to its usage, and the most common problems faced by them. This research was conducted using the concurrent triangulation strategy, which is a mixed-method approach. We performed a thorough analysis of 9,405 questions related to Kotlin development for the Android platform on Stack Overflow. Concurrently, we also conducted a basic qualitative research interviewing seven Android developers that use Kotlin to confirm and cross-validate our findings. Our study reveals that developers do seem to find the language easy to understand and to adopt it. This perception begins to change when the functional paradigm becomes more evident. Accordingly to the developers, the readability and legibility are compromised if developers overuse the functional flexibility that the language provides. The developers also consider that Kotlin increases the quality of the produced code mainly due to its null-safety guarantees, but it can also become a challenge when interoperating with Java, despite the interoperability being considered as an advantage. While adopting Kotlin requires some care from developers, its benefits seem to bring many advantages to the platform according to the developers, especially in the aspect of adopting a more modern language while maintaining the consolidated Java-based development environment.
Essential Sentences for Navigating Stack Overflow Answers

Stack Overflow (SO) has become an essential resource for software development. Despite its success and prevalence, navigating SO remains a challenge. Ideally, SO users could benefit from highlighted navigational cues that help them decide if an answer is relevant to their task and context. Such navigational cues could be in the form of essential sentences that help the searcher decide whether they want to read the answer or skip over it. In this paper, we compare four potential approaches for identifying essential sentences. We adopt two existing approaches and develop two new approaches based on the idea that contextual information in a sentence (e.g., “if using windows”) could help identify essential sentences. We compare the four techniques using a survey of 43 participants. Our participants indicate that it is not always easy to figure out what the best solution for their specific problem is, given the options, and that they would indeed like to easily spot contextual information that may narrow down the search. Our quantitative comparison of the techniques shows that there is no single technique sufficient for identifying essential sentences that can serve as navigational cues, while our qualitative analysis shows that participants valued explanations and specific conditions, and did not value filler sentences or speculations. Our work sheds light on the importance of navigational cues, and our findings can be used to guide future research to find the best combination of techniques to identify such cues.
Lightweight network architecture for real-time action recognition

In this work we present a new efficient approach to Human Action Recognition called Video Transformer Network (VTN). It leverages the latest advances in Computer Vision and Natural Language Processing and applies them to video understanding. The proposed method allows us to create lightweight CNN models that achieve high accuracy and real-time speed using just an RGB mono camera and general purpose CPU. Furthermore, we explain how to improve accuracy by distilling from multiple models with different modalities into a single model. We conduct a comparison with state-of-the-art methods and show that our approach performs on par with most of them on famous Action Recognition datasets. We benchmark the inference time of the models using the modern inference framework and argue that our approach compares favorably with other methods in terms of speed/accuracy trade-off, running at 56 frames per second (FPS) on CPU. The models and the training code are available1.
WiST ID—Deep Learning-Based Large Scale Wireless Standard Technology Identification

Dynamic spectrum access based wireless networks and next-generation cognitive electronic warfare systems demand rapid identification and labelling of high data rate radio frequency (RF) information. This requires receiver front-end designs to distinguish numerous kinds of wireless signals of different standards over a relatively wide spectrum. This paper proposes a novel attempt at large scale, blind identification of signals from 29 wireless standard technologies that occupy the modern day spectrum. A deep convolutional neural network model called ‘Wireless Standard Technology Identifier (WiST ID)’ is deployed, along with a pre-processing method based on the Stockwell transform time-frequency representation for highly accurate classification over relatively large number of signal classes. The model demonstrates enhanced learning of RF fingerprints from the pre-processed Stockwell images belonging to a variety of wireless technologies. Analyses of classification performance over synthetically generated samples with SNR scenarios varying from −10 dB to 10 dB reveal the robustness of the model under low and moderate SNR. At a modest SNR of 10 dB, the model achieves 100% classification accuracy over a small scale synthetic dataset (9 classes). Over a large scale dataset (29 classes) consisting of both synthetically generated and over-the-air captured samples, the classification accuracy achieved is 98.91%.
LSQ: Load Balancing in Large-Scale Heterogeneous Systems With Multiple Dispatchers

Nowadays, the efficiency and even the feasibility of traditional load-balancing policies are challenged by the rapid growth of cloud infrastructure and the increasing levels of server heterogeneity. In such heterogeneous systems with many loadbalancers, traditional solutions, such as JSQ, incur a prohibitively large communication overhead and detrimental incast effects due to herd behavior. Alternative low-communication policies, such as JSQ(d) and the recently proposed JIQ, are either unstable or provide poor performance. We introduce the Local Shortest Queue (LSQ) family of load balancing algorithms. In these algorithms, each dispatcher maintains its own, local, and possibly outdated view of the server queue lengths, and keeps using JSQ on its local view. A small communication overhead is used infrequently to update this local view. We formally prove that as long as the error in these local estimates of the server queue lengths is bounded in expectation, the entire system is strongly stable. Finally, in simulations, we show how simple and stable LSQ policies exhibit appealing performance and significantly outperform existing low-communication policies, while using an equivalent communication budget. In particular, our simple policies often outperform even JSQ due to their reduction of herd behavior. We further show how, by relying on smart servers (i.e., advanced pull-based communication), we can further improve performance and lower communication overhead.
Deep learning in static, metric-based bug prediction

Our increasing reliance on software products and the amount of money we spend on creating and maintaining them makes it crucial to find bugs as early and as easily as possible. At the same time, it is not enough to know that we should be paying more attention to bugs; finding them must become a quick and seamless process in order to be actually used by developers. Our proposal is to revitalize static source code metrics – among the most easily calculable, while still meaningful predictors – and combine them with deep learning – among the most promising and generalizable prediction techniques – to flag suspicious code segments at the class level. In this paper, we show a detailed methodology of how we adapted deep neural networks to bug prediction, applied them to a large bug dataset (containing 8780 bugged and 38,838 not bugged Java classes), and compared them to multiple “traditional” algorithms. We demonstrate that deep learning with static metrics can indeed boost prediction accuracies. Our best model has an F-measure of 53.59%, which increases to 55.27% for the best ensemble model containing a deep learning component. Additionally, another experiment suggests that these values could improve even further with more data points. We also open-source our experimental Python framework to help other researchers replicate our findings.
Factoring User Experience into the Security and Privacy Design of Smart Home Devices: A Case Study

Smart home devices are growing in popularity due to their functionality, convenience, and comfort. However, they are raising security and privacy concerns for users who may have very little technical ability. User experience (UX) focuses on improving user interactions, but little work has investigated how companies factor user experience into the security and privacy design of smart home devices as a means of addressing these concerns. To explore this in more detail, we designed and conducted six in-depth interviews with employees of a large smart home company in the United Kingdom. We analyzed the data using Grounded Theory, and found little evidence that UX is a consideration for the security design of these devices. Based on the results of our study, we proposed user-centered design guidelines and recommendations to improve data protection in smart homes.
A Width-growth Model with Subnetwork Nodes and Refinement Structure for Representation Learning and Image Classification

This article presents a new supervised multilayer subnetwork-based feature refinement and classification model for representation learning. The novelties of this algorithm are as follows: 1) different from most multilayer networks that go deeper with increased number of network layers, this work architects a model with wider subnetwork nodes; 2) the conventional classification methods adopt a separate search mechanism to derive a generalized feature space and to get the final cognition, but this work proposes a one-shot process to find the meaningful latent space and recognize the objects; and 3) the traditional feature representation and image classification approaches apply a unimodal feature coding, which suffers from lack of global knowledge. This work overcomes the pitfall through multimodal fusion that fuses various feature sources into one superstate encoding to achieve higher performance. A cross-domain experimental study on camera identification and image classification shows that the proposed method achieves superior performance compared to the existing models.