Reviewerly/exHarmony huggingface dataset:
Measurements, Analyses, and Insights on the Entire Ethereum Blockchain Network

Blockchains are increasingly becoming popular due to the prevalence of cryptocurrencies and decentralized applications. Ethereum is a distributed public blockchain network that focuses on running code (smart contracts) for decentralized applications. More simply, it is a platform for sharing information in a global state that cannot be manipulated or changed. Ethereum blockchain introduces a novel ecosystem of human users and autonomous agents (smart contracts). In this network, we are interested in all possible interactions: user-to-user, user-to-contract, contract-to-user, and contract-to-contract. This requires us to construct interaction networks from the entire Ethereum blockchain data, where vertices are accounts (users, contracts) and arcs denote interactions. Our analyses on the networks reveal new insights by combining information from the four networks. We perform an in-depth study of these networks based on several graph properties consisting of both local and global properties, discuss their similarities and differences with social networks and the Web, draw interesting conclusions, and highlight important, future research directions.
Partly Cloudy With a Chance of Lava Flows: Forecasting Volcanic Eruptions in the Twenty‐First Century

Abstract A primary goal of volcanology is forecasting hazardous eruptive activity. Despite much progress over the last century, however, volcanoes still erupt with no detected precursors, lives and livelihoods are lost to eruptive activity, and forecasting the onsets of eruptions remains fraught with uncertainty. Long‐term forecasts are generally derived from the geological and historical records, from which recurrence intervals and styles of activity can be inferred, while shorter‐term forecasts are derived from patterns in monitoring data. Information from geology and monitoring data can be evaluated and combined using statistical analysis, expert elicitation, and conceptual and or mathematical models. Integrative frameworks, such as event trees, combine this diversity of information to produce probabilistic forecasts that can inform the style and scale of the societal response to a potential future eruption. Several developments show promise to revolutionize the utility and accuracy of these forecasts. These include growth in the quantity and quality of multidisciplinary monitoring data, coupled with increases in computing power; machine learning algorithms, which will allow far better utilization of this growing volume of data; and new physiochemical volcano models and data assimilation algorithms, which take advantage of a wide range of monitoring data and realistic physics to better predict the evolution of a given physical state. Although eruption forecasts may never be as generally reliable as weather forecasts, and great caution must be exercised when attempting to predict highly complex volcanic behavior, these and other innovations—particularly when combined in integrative, fully probabilistic forecasting frameworks—should help volcanologists to better issue warnings of volcanic activity on societally relevant time frames.
Deep Learning for Seeing Through Window With Raindrops

When taking pictures through glass window in rainy day, the images are comprised and corrupted by the raindrops adhered to glass surfaces. It is a challenging problem to remove the effect of raindrops from an image. The key task is how to accurately and robustly identify the raindrop regions in an image. This paper develops a convolutional neural network (CNN) for removing the effect of raindrops from an image. In the proposed CNN, we introduce a double attention mechanism that concurrently guides the CNN using shape-driven attention and channel re-calibration. The shape-driven attention exploits physical shape priors of raindrops, i.e. convexness and contour closedness, to accurately locate raindrops, and the channel re-calibration improves the robustness when processing raindrops with varying appearances. The experimental results show that the proposed CNN outperforms the state-of-the-art approaches in terms of both quantitative metrics and visual quality.
Software Vulnerability Discovery via Learning Multi-Domain Knowledge Bases

Machine learning (ML) has great potential in automated code vulnerability discovery. However, automated discovery application driven by off-the-shelf machine learning tools often performs poorly due to the shortage of high-quality training data. The scarceness of vulnerability data is almost always a problem for any developing software project during its early stages, which is referred to as the cold-start problem. This article proposes a framework that utilizes transferable knowledge from pre-existing data sources. In order to improve the detection performance, multiple vulnerability-relevant data sources were selected to form a broader base for learning transferable knowledge. The selected vulnerability-relevant data sources are cross-domain, including historical vulnerability data from different software projects and data from the Software Assurance Reference Database (SARD) consisting of synthetic vulnerability examples and proof-of-concept test cases. To extract the information applicable in vulnerability detection from the cross-domain data sets, we designed a deep-learning-based framework with Long-short Term Memory (LSTM) cells. Our framework combines the heterogeneous data sources to learn unified representations of the patterns of the vulnerable source codes. Empirical studies showed that the unified representations generated by the proposed deep learning networks are feasible and effective, and are transferable for real-world vulnerability detection. Our experiments demonstrated that by leveraging two heterogeneous data sources, the performance of our vulnerability detection outperformed the static vulnerability discovery tool <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flawfinder</i> . The findings of this article may stimulate further research in ML-based vulnerability detection using heterogeneous data sources.
Bitcoin and its mining on the equilibrium path

Bitcoin as a major cryptocurrency has come up as a shooting star of the 2017 and 2018 headlines. After exploding its price twenty times just in the twelve months of 2017, the tone has changed dramatically in 2018 after major price corrections and increasing concerns about its mining power consumption and overall sustainability. The dynamics and interaction between Bitcoin price and its mining costs have become of major interest. Here we show that these two quantities are tightly interconnected and they tend to a common long-term equilibrium. Mining costs adjust to the cryptocurrency price with the adjustment time of several months up to a year. Current developments suggest that we have arrived at a new era of Bitcoin mining where marginal (electricity) costs and mining efficiency play the prime role. Presented results open new avenues towards interpreting past and predicting future developments of the Bitcoin mining framework and their main possible directions are outlined and discussed here as well.
Severely imbalanced Big Data challenges: investigating data sampling approaches

Abstract Severe class imbalance between majority and minority classes in Big Data can bias the predictive performance of Machine Learning algorithms toward the majority (negative) class. Where the minority (positive) class holds greater value than the majority (negative) class and the occurrence of false negatives incurs a greater penalty than false positives, the bias may lead to adverse consequences. Our paper incorporates two case studies, each utilizing three learners, six sampling approaches, two performance metrics, and five sampled distribution ratios, to uniquely investigate the effect of severe class imbalance on Big Data analytics. The learners ( Gradient-Boosted Trees, Logistic Regression, Random Forest ) were implemented within the Apache Spark framework. The first case study is based on a Medicare fraud detection dataset. The second case study, unlike the first, includes training data from one source (SlowlorisBig Dataset) and test data from a separate source (POST dataset). Results from the Medicare case study are not conclusive regarding the best sampling approach using Area Under the Receiver Operating Characteristic Curve and Geometric Mean performance metrics. However, it should be noted that the Random Undersampling approach performs adequately in the first case study. For the SlowlorisBig case study, Random Undersampling convincingly outperforms the other five sampling approaches ( Random Oversampling, Synthetic Minority Over-sampling TEchnique, SMOTE-borderline1 , SMOTE-borderline2 , ADAptive SYNthetic ) when measuring performance with Area Under the Receiver Operating Characteristic Curve and Geometric Mean metrics. Based on its classification performance in both case studies, Random Undersampling is the best choice as it results in models with a significantly smaller number of samples, thus reducing computational burden and training time.
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction

Conditional text-to-image generation is an active area of research, with many possible applications. Existing research has primarily focused on generating a single image from available conditioning information in one step. One practical extension beyond one-step generation is a system that generates an image iteratively, conditioned on ongoing linguistic input or feedback. This is significantly more challenging than one-step generation tasks, as such a system must understand the contents of its generated images with respect to the feedback history, the current feedback, as well as the interactions among concepts present in the feedback history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, and apply simple transformations to existing objects. We believe our approach is an important step toward interactive generation. Code and data is available at: https://www.microsoft.com/en-us/research/project/generative-neural-visual-artist-geneva/.
Incorporating context-relevant concepts into convolutional neural networks for short text classification

Text classification is an important task in natural language processing. Previous text classification models do not perform well on short texts due to the data sparsity problem. In order to solve this problem, recent research extracts concepts of words to enrich text representation. However, this approach might bring general concepts, which might not be helpful in discriminating categories in text classification. Furthermore, it might bring noise into text representation and lead to performance degradation. To tackle these problems, we propose a neural network called DE-CNN, which can incorporate context-relevant concepts into a convolutional neural network for short text classification. Our model firstly utilizes two layers to extract concepts and context respectively and then employs an attention layer to extract context-relevant concepts. Then the concepts are incorporated into text representation for short text classification. The experimental results on three text classification tasks show that our proposed model outperforms compared state-of-the-art models.
Requirements Engineering Challenges in Building AI-Based Complex Systems

This paper identifies and tackles the challenges of the requirements engineering discipline when applied to development of AI-based complex systems. Due to their complex behaviour, there is an immanent need for a tailored development process for such systems. However, there is still no widely used and specifically tailored process in place to effectively and efficiently deal with requirements suitable for specifying a software solution that uses machine learning. By analysing the related work from software engineering and artificial intelligence fields, potential contributions have been recognized from agent-based software engineering and goal-oriented requirements engineering research, as well as examples from large product development companies. The challenges have been discussed, with proposals given how and when to tackle them. RE4AI taxonomy has also been outlined, to inform the tailoring of development process.
An efficient krill herd algorithm for color image multilevel thresholding segmentation problem

The conventional thresholding methods are very efficient for bi-level thresholding, but the computational complexity may be excessively high for color image multilevel thresholding. Color image multilevel thresholding segmentation can be considered as a constrained optimization problem, therefore swarm intelligence algorithms are widely used to reduce the complexity. In this paper, an efficient krill herd (EKH) algorithm is proposed to search optimal thresholding values at different level for color images and the Otsu’s method, Kapur’s entropy and Tsallis entropy are employed as objective functions. Seven different algorithms, KH without any genetic operators (KH I), KH with crossover operator (KH II), KH with crossover and mutation operators (KH IV), modified firefly algorithm (MFA), modified grasshopper optimization algorithm (MGOA), bat algorithm (BA) and water cycle algorithm (WCA), are compared with the EKH algorithm. Experiments are performed on ten color benchmark images in terms of optimal threshold values, objective values, PSNR, SSIM and standard deviation of the objective values at different levels. The experimental results show that the presented EKH algorithm is superior to the other algorithms for color image multilevel thresholding segmentation. On the other hand, Kapur’s entropy is found to be more accurate and robust for color image multilevel thresholding segmentation.
Decentralized Self-Enforcing Trust Management System for Social Internet of Things

The Internet of Things (IoT) is the network of connected computing devices that have the ability to transfer valued data between each other via the Internet without requiring human intervention. In such a connected environment, the social IoT (SIoT) has become an emerging trend where multiple IoT devices owned by users support communication within a social circle. Trust management in the SIoT network is imperative as trusting the information from compromised devices could lead to serious compromises within the network. It is important to have a mechanism where the devices and their users evaluate the trustworthiness of other devices and users before trusting the information sent by them. The privacy preservation, decentralization, and self-enforcing management without involving trusted third parties are the fundamental challenges in designing a trust management system for SIoT. To fulfill these challenges, this article presents a novel framework for computing and updating the trustworthiness of participants in the SIoT network in a self-enforcing manner without relying on any trusted third party. The privacy of the participants in the SIoT is protected by using homomorphic encryption in the decentralized setting. To achieve the properties of self-enforcement, the trust score of each device is automatically updated based on its previous trust score and the up-to-date tally of the votes by its peers in the network with zero-knowledge proofs (ZKPs) to enforce that every participant follows the protocol honestly. We evaluate the performance of the proposed scheme and present evaluation benchmarks by prototyping the main functionality of the system. The performance results show that the system has a linear increase in computation and communication overheads with more participants in the network. Furthermore, we prove the correctness, privacy, and security of the proposed system under a malicious adversarial model.
INGRESS: Interactive visual grounding of referring expressions

This article presents INGRESS, a robot system that follows human natural language instructions to pick and place everyday objects. The key question here is to ground referring expressions: understand expressions about objects and their relationships from image and natural language inputs. INGRESS allows unconstrained object categories and rich language expressions. Further, it asks questions to clarify ambiguous referring expressions interactively. To achieve these, we take the approach of grounding by generation and propose a two-stage neural-network model for grounding. The first stage uses a neural network to generate visual descriptions of objects, compares them with the input language expressions, and identifies a set of candidate objects. The second stage uses another neural network to examine all pairwise relations between the candidates and infers the most likely referred objects. The same neural networks are used for both grounding and question generation for disambiguation. Experiments show that INGRESS outperformed a state-of-the-art method on the RefCOCO dataset and in robot experiments with humans. The INGRESS source code is available at https://github.com/MohitShridhar/ingress .
LDV: A Lightweight DAG-Based Blockchain for Vehicular Social Networks

As social networks are integrated into the Vehicular Ad Hoc Networks (VANETs), the emerging Vehicular Social Networks (VSNs) have gained massive interests. However, the security and privacy of data generated by various applications in VSNs is a great challenge, which blocks the further development of VSNs. The emerging blockchain technology seems to be a good catalyst for the development of VSN with its high security and irreversible features, which can be also a data management tool for rapidly generated data of VSNs with tamper proof. However, the full duplicates of blockchain data need to be stored in each node to ensure security, which is unacceptable for vehicles with limited resource. In this paper, to address the above storage challenge, a lightweight Directed Acyclic Graph (DAG) based blockchain (LDV) is proposed for resource-constrained VSNs. Specifically, based on the in-depth analysis of VSNs, we propose the social-based data reduction approach. In detail, each node only stores the interested data within the topic groups of interest and ignores the irrelevant data. To avoid the huge storage cost within large-scale groups with large amounts of data, we further present the historical data pruning method within a group, which meets the storage requirement by reducing the number of duplicates stored in each node. Experimental results show that LDV can save 97.13% storage space and has good scalability.
PIC, a paediatric-specific intensive care database

Abstract PIC (Paediatric Intensive Care) is a large paediatric-specific, single-centre, bilingual database comprising information relating to children admitted to critical care units at a large children’s hospital in China. The database is deidentified and includes vital sign measurements, medications, laboratory measurements, fluid balance, diagnostic codes, length of hospital stays, survival data, and more. The data are publicly available after registration, which includes completion of a training course on research with human subjects and signing of a data use agreement mandating responsible handling of the data and adherence to the principle of collaborative research. Although the PIC can be considered an extension of the widely used MIMIC (Medical Information Mart for Intensive Care) database in the field of paediatric critical care, it has many unique characteristics and can support database-based academic and industrial applications such as machine learning algorithms, clinical decision support tools, quality improvement initiatives, and international data sharing.
A novel density-based clustering algorithm using nearest neighbor graph

Density-based clustering has several desirable properties, such as the abilities to handle and identify noise samples, discover clusters of arbitrary shapes, and automatically discover of the number of clusters. Identifying the core samples within the dense regions of a dataset is a significant step of the density-based clustering algorithm. Unlike many other algorithms that estimate the density of each samples using different kinds of density estimators and then choose core samples based on a threshold, in this paper, we present a novel approach for identifying local high-density samples utilizing the inherent properties of the nearest neighbor graph (NNG). After using the density estimator to filter noise samples, the proposed algorithm ADBSCAN in which “A” stands for “Adaptive” performs a DBSCAN-like clustering process. The experimental results on artificial and real-world datasets have demonstrated the significant performance improvement over existing density-based clustering algorithms.
U-CAM: Visual Explanation Using Uncertainty Based Class Activation Maps

Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we further improve by using the gradients for these estimates. These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions. The improved attention maps result in consistent improvement for various methods for visual question answering. Therefore, the proposed technique can be thought of as a recipe for obtaining improved certainty estimates and explanation for deep learning models. We provide detailed empirical analysis for the visual question answering task on all standard benchmarks and comparison with state of the art methods.
Three Dynamic Pricing Schemes for Resource Allocation of Edge Computing for IoT Environment

With the widespread use of Internet of Things (IoT), edge computing has recently emerged as a promising technology to tackle low-latency and security issues with personal IoT data. In this regard, many works have been concerned with computing resource allocation of the edge computing server, and some studies have conducted to the pricing schemes for resource allocation additionally. However, few works have attempted to address the comparison among various kinds of pricing schemes. In addition, some schemes have their limitations such as fairness issues on differentiated pricing schemes. To tackle these limitations, this article considered three dynamic pricing mechanisms for resource allocation of edge computing for the IoT environment with a comparative analysis: BID-proportional allocation mechanism (BID-PRAM), uniform pricing mechanism (UNI-PRIM), and fairness-seeking differentiated pricing mechanism (FAID-PRIM). BID-PRAM is newly proposed to overcome the limitation of the auction-based pricing scheme; UNI-PRIM is a basic uniform pricing scheme; FAID-PRIM is newly proposed to tackle the fairness issues of the differentiated pricing scheme. BID-PRAM is formulated as a noncooperative game. UNI-PIM and FAID-PRIM are formulated as a single-leader-multiple-followers Stackelberg game. In each mechanism, the Nash equilibrium (NE) or Stackelberg equilibrium (SE) solution is given with the proof of existence and uniqueness. Numerical results validate the proposed theorems and present a comparative analysis of three mechanisms. Through these analyses, the advantages and disadvantages of each model are identified, to give edge computing service providers guidance on various kinds of pricing schemes.