Reviewerly/exHarmony huggingface dataset:
Accelerating State Synchronization in Bitcoin Combining Secure Snapshot with IPFS

In recent years, the Bitcoin blockchain has gained wide attention due to its decentralization and transparency. With the extensive adoptions of the Bitcoin blockchain, its size has reached to 408 GB so far and is still growing, which significantly degrades its scalability. Besides, the bootstrap process for newly joining nodes is quite slow and costly due to the huge communication and computation overhead. In the paper, we devise a full-fledged solution that not only improves the scalability of the Bitcoin blockchain, but also accelerates the state synchronization of the Bitcoin blockchain. We adopt IPFS (Inter Planetary File System), a trusted off-chain storage platform, to amortize the storage pressure of the Bitcoin full nodes. Moreover, we present snapshot blocks to enable secure and fast synchronization of UTXO (Unspent Transaction Outputs) set instead of scanning again from the beginning of the Bitcoin blockchain. Finally, we conduct extensive experiments. The results show that, compared with the official Bitcoin core, the scheme reduces the storage space for Bitcoin full nodes by 97.8% and the synchronization time of new nodes by 81.5%, without breaking the protocols of existing system.
Multi-view Contrastive Multiple Knowledge Graph Embedding for Knowledge Completion

Knowledge graphs (KGs) are useful information sources to make machine learning efficient with human knowledge. Since KGs are often incomplete, KG completion has become an important problem to complete missing facts in KGs. Whereas most of the KG completion methods are conducted on a single KG, multiple KGs can be effective to enrich embedding space for KG completion. However, most of the recent studies have concentrated on entity alignment prediction and ignored KG-invariant semantics in multiple KGs that can improve the completion performance. In this paper, we propose a new multiple KG embedding method composed of intra-KG and inter-KG regularization to introduce KG-invariant semantics into KG embedding space using aligned entities between related KGs. The intra-KG regularization adjusts local distance between aligned and not-aligned entities using contrastive loss, while the inter-KG regularization globally correlates aligned entity embeddings between KGs using multi-view loss. Our experimental results demonstrate that our proposed method combining both regularization terms largely outperforms existing baselines in the KG completion task.
Feature Extraction for Out of Distribution Detection via Self-Supervised Learning

In an operational environment, an automated system performing image classification can encounter observations belonging to classes for which it was not previously trained to recognize. In such cases, modern machine learning classifiers are prone to misclassifying Out Of Distribution (OOD) observations as one of the model’s known classes - with high confidence. In defense applications, such an error could have catastrophic consequences, and requires reliable OOD detection to mitigate this risk. Techniques exist to reliably detect OOD observations, but these techniques largely assume the availability of a useful OOD exposure set during model training. Unfortunately, such an exposure set cannot be assumed for many defense applications that suffer from low amounts of labeled (and unlabeled) data. Therefore, in this work we investigate enabling OOD detection without an OOD exposure set. Specifically, we investigate Self-Supervised Learning as a feature extraction method, with the hypothesis that Self-Supervised Learning can provide a rich feature set for OOD detection, without OOD exposure during training.
An Optimized Scheduling Algorithm for the Multi-channel Hyperledger Fabric

Hyperledger Fabric (Fabric for short), a typical consortium blockchain platform, has released many versions that support different consensus protocols to ensure that the system can withstand faults (such as crashes, network partitions, or network shutdowns). However, if there is a malicious node in the system, Fabric cannot guarantee any behavior of the malicious node. Therefore, when a certain number of nodes are attacked simultaneously, it directly affects the security and efficiency of the entire system. In the face of multi-channel Fabric network architecture, the parallel processing of transactions by node groups linked on different channels significantly improves the throughput of the Fabric network. However, it may also cause system congestion and safety problems. To solve this problem, this paper designs a Lyapunov-based scheduling algorithm for the performance optimization of Fabric. When considering the system's security probability and queue accumulation, we give the maximum consensus verification rate under the minimum security probability guarantee. Finally, we installed the latest stable version v2.0 of Fabric on the cloud server to verify the algorithm's effectiveness.
Active learning of causal probability trees

The past two decades have seen a growing interest in combining causal information, commonly represented using causal graphs, with machine learning models. Probability trees provide a simple yet powerful alternative representation of causal information. They enable both computation of intervention and counterfactuals, and are strictly more general, since they allow context-dependent causal dependencies. Here we present a Bayesian method for learning probability trees from a combination of interventional and observational data. The method quantifies the expected information gain from an intervention, and selects the interventions with the largest gain. We demonstrate the efficiency of the method on simulated and real data. An effective method for learning probability trees on a limited interventional budget will greatly expand their applicability.
PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.
Safe Robot Navigation Using Constrained Hierarchical Reinforcement Learning

Safe navigation is one of the steps necessary for achieving autonomous control of robots. Among different algorithms that focus on robot navigation, Reinforcement Learning (and more specifically Deep Reinforcement Learning) has shown impressive results for controlling robots with complex and high-dimensional state representations. However, when integrating methods to comply with safety requirements by means of constraint satisfaction in flat Reinforcement Learning policies, the system performance can be affected. In this paper, we propose a constrained Hierarchical Reinforcement Learning framework with a safety layer used to modify the low-level policy to achieve a safer operation of the robot. Results obtained in simulation show that the proposed method is better at retaining performance while keeping the system in a safe region when compared to a constrained flat model.
GreenBFS: Space-Efficient BFS Engine for Power-aware Graph Processing

BFS (Breadth-First Search) is a promising killer engine for graph processing and currently has an extremely important role in real-time processing scenarios in everyday life. Unfortunately, the current BFS engine often delivers poor efficiency owing to complex graph representation, and high energy/memory cost. This paper presents GreenBFS, a space-efficient BFS engine for power-aware graph processing. GreenBFS (i) adopts Folding@CSR, a novel compressed sparse row(CSR) format based on folding edges for sorted CSR. Folding@CSR can store graph data using 50% smaller memory compared to the state-of-the-art graph compression; and (ii) proposes a mixed-conservation mode based on hardware and software integration that can attain power conservation by 35.7%. We use both benchmark and real-world graphs to demonstrate the effectiveness of GreenBFS. Firstly, GreenGraph500, a widely accepted benchmark to rank graph processing capability with energy, is used to validate GreenBFS. GreenBFS-based Tianhe Exa-node has won both No.1 in the latest GreenGraph500 ranking (June, 2022) in big data and small data categories. We finally apply GreenBFS to construct graphs, which is much more efficient on memory footprint than that of the state-of-the-art graph representation with lower electric supply.
Stylometric and Semantic Analysis of Demographically Diverse Non-native English Review Data

The demographic knowledge facilitates a fine-grained interpretation of the user-generated review text and enables better decision-making. In this study, we aim to com-prehend how various attributes of non-native English text vary across demographically distinct groups. We introduce a non-native English corpus of around 1150 reviews representing four demographically diverse country-specific groups: Finland, Kenya, Bangladesh, and China. The reviews differ in various contexts, including geography, native language family, race and culture, and English proficiency levels of the reviewers. We then perform stylometric and semantic analysis on these distinct sets of reviews to unveil how the linguistic characteristics differ across the demography. The investigation reveals that stylometric features are mostly similar across the reviews of various groups; nevertheless, dissimilarities are observed in attributes, such as review length, presence of articles, or prepositions. We employ classical machine learning (ML) algorithms and transformer-based fine-tuned language models for categorizing the reviews into distinct demographic groups. We observe that semantic features yield slightly better efficacy than syntactic features for distinguishing the demography-specific reviews.
Improving Code Review with GitHub Issue Tracking

Software quality is an important problem for technology companies, since it substantially impacts the efficiency, usefulness, and maintainability of the final product; hence, code review is a must-do activity for software developers. During the code review process, senior engineers monitor other developers' work to spot possible problems and enforce coding standards. One of the most widely used open-source software platforms, GitHub, attracts millions of developers who use it to store their projects. This study aims to analyze code quality on GitHub from the standpoint of code reviews. We examined the code review process using GitHub's Issues Tracker, which allows team members to evaluate, discuss, and share their opinions on the proposed code before it is approved. Based on our analysis, we present a novel approach for improving the code review process by promoting regularity and community involvement.
MVDetecter: Vulnerability Primitive-based General Memory Vulnerability Detection

Memory vulnerability is one of the most harmful software vulnerabilities. The attackers can tamper/leak memory data or take complete control over the program by exploiting these vulnerabilities. It is critical to detect memory vulnerability comprehensively. Unfortunately, type-based detections hardly achieve comprehensive detection due to the inexhaustible vul-nerability types, and the general detections usually are heuristic, causing high false negatives. Therefore, there is a lack of effective and comprehensive methods for memory vulnerability detection. This paper presents a new Vulnerability Primitive-based static detection prototype, MVDetecter, to solve the problem. By exploring the root cause of vulnerability generation and exploitation, we propose the memory vulnerability primitive as a guideline for vulnerability detection. We perform static analysis to collect the required information. Then we search for vulnerability primitive to locate the memory vulnerabilities. We implemented MVDe-teeter based on Joern and evaluated its effectiveness with Juliet Test Suite (JTS), two open-source software, and 32 advanced attacks. The results show that MVDetecter can successfully detect most vulnerabilities with a 1.41 % false negative for JTS. And it successfully determines the location of the vulnerability exploited by the real-world attack. Besides, the runtime overhead of MVDetecter is acceptable in SPEC CPU 2006.
WayPop Machine: A Wayback Machine to Investigate Popularity and Root Out Trolls

Contrary to celebrities who owe their popularity online to their activity offline, malicious users such as trolls have to gain fame on social media through the social media itself. The exact reasons that a certain user has become popular are often obscure especially when the popularity was gained illicitly through means such as fake amplification of content. In this paper, we develop a methodology for uncovering why an account has become popular and present an open source tool that encapsulates this methodology. This tool aims to aid others in uncovering malicious accounts which have artificially gained many followers and to distinguish such accounts from those which gained followers and popularity honestly.
DIPool: Degree-Induced Pooling for Hierarchical Graph Representation Learning

Graph Neural Networks have been popularly investigated and achieved promising performance in node-wise and graph-level tasks, like node classification and graph classification. For graph classification, correctly defining pooling operations to coarsen an input graph for distilling hierarchical knowledge from node representations is demanded. Some works have devised pooling strategies to summarize a graph by evaluating and gathering the ranked top-k representative nodes. Yet there is few literature to explicitly consider the node's degree-specific structure information in pooling procedure. One intuition is the larger the node's degree is, the more important it probably is in the entire graph. In this work, we propose a novel Degree-Induced Pooling (DIPool), which can be integrated into various graph neural network architectures. Specifically, the DIPool layer devises two approaches for explicitly incorporating the characteristics and degree-specific structures of nodes into a unified criterion to evaluate the importance of the nodes within an input graph. This setting permits to retain more representative nodes to generate the newly pooled graph. Experimental results on a collection of public benchmarks have demonstrated the effectiveness of the proposed DIPool operator. Especially, DIPool achieves relative improvements of 3.69% on NCI1 and 3.24% on NCI109 compared with the best baseline ASAP.
IKEA: Unsupervised domain-specific keyword-expansion

How can we expand an initial set of keywords with a target domain in mind? A possible application is to use the expanded set of words to search for specific information within the domain of interest. Here, we focus on online forums and specifically security forums. We propose IKEA, an iterative embedding-based approach to expand a set of keywords with a domain in mind. The novelty of our approach is three-fold: (a) we use two similarity expansions in the word-word and post-post spaces, (b) we use an iterative approach in each of these expansions, and (c) we provide a flexible ranking of the identified words to meet the user needs. We evaluate our method with data from three security forums that span five years of activity and the widely-used Fire benchmark. IKEA outperforms previous solutions by identifying more relevant keywords: it exhibits more than 0.82 MAP and 0.85 NDCG in a wide range of initial keyword sets. We see our approach as an essential building block in developing methods for harnessing the wealth of information available in online forums.
GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.
DDSupport: Language Learning Support System that Displays Differences and Distances from Model Speech

When beginners learn to speak a non-native language, it is difficult for them to judge for themselves whether they are speaking well. Therefore, computer-assisted pronunciation training systems are used to detect learner mispronunciations. These systems typically compare the user’s speech with that of a native speaker in units of rhythm, phonemes, or words, and calculate the differences. However, they require extensive speech data with detailed annotations of non-native and native speakers, which are usually difficult to collect. To overcome this problem, we propose a new language learning support system that detects mispronunciations by beginners based on a small amount of unannotated native speaker speech data. The proposed system uses deep learning–based speech processing to display the pronunciation score of the learner’s speech and the difference/distance between the learner’s and the model’s pronunciation in an intuitively visual manner. Learners can gradually improve their pronunciation by eliminating differences and shortening the distance from the model until they become sufficiently proficient. We also built an application to help nonnative speakers learn English and confirmed that it can improve users’ speech intelligibility.
PIMan: A Comprehensive Approach for Establishing Plausible Influence among Software Repositories

How can we quantify the influence among repos-itories in online archives like GitHub? Determining repository influence is an essential building block for understanding the dynamics of GitHub-like software archives. The key challenge is to define the appropriate representation model of influence that captures the nuances of the concept and considers its diverse manifestations. We propose PIMan, a systematic approach to quantify the influence among the repositories in a software archive by focusing on the social level interactions. As our key novelty, we introduce the concept of Plausible Influence which considers three types of information: (a) repository level interactions, (b) author level interactions, and (c) temporal considerations. We evaluate and apply our method using 2089 malware repositories from GitHub spanning approximately 12 years. First, we show how our approach provides a powerful and flexible way to generate a plausible influence graph whose density is determined by the Plausible Influence Threshold (PIT), which is modifiable to meet the needs of a study. Second, we find that there is a significant collaboration and influence among the repositories in our dataset. We identify 28 connected components in the plausible influence graph (PIT = 0.25) with 7% of the components containing at least 15 repositories. Furthermore, we find 19 repositories that influenced at least 10 other repositories directly and spawned at least two “families” of repositories. In addition, the results show that our influence metrics capture the manifold aspects of the interactions that are not captured by the typical repository popularity metrics (e.g. number of stars). Overall, our work is a fundamental building block for identifying the influence and lineage of the repositories in online software platforms.
Discovering Affinity Relationships between Personality Types

Psychology research findings suggest that personality is related to differences in friendship characteristics and that some personality traits correlate with linguistic behavior. In this paper, we investigate the influence that personality may have on affinity formation. To this end, we derive affinity relationships from social media interactions, examine personality based on language use to discover the emotional stability of affinity relationships, and measure semantic similarity at the personality type level to understand the logic behind the development of affinity. Specifically, we conduct extensive experiments using a publicly available dataset containing information on individuals who self-identified with a Myers-Briggs personality type. Our results identify certain influential personality types that weigh more heavily on affinity relationships and show that personality can be predicted from spontaneous language with an F-1 score superior to 0.76. Future research avenues are proposed.
Noise Audits Improve Moral Foundation Classification

Morality plays an important role in culture, identity, and emotion. Recent advances in natural language processing have shown that it is possible to classify moral values expressed in text at scale. Morality classification relies on human annotators to label the moral expressions in text, which provides training data to achieve state-of-the-art performance. However, these annotations are inherently subjective and some of the instances are hard to classify, resulting in noisy annotations due to error or lack of agreement. The presence of noise in training data harms the classifier's ability to accurately recognize moral foundations from text. We propose two metrics to audit the noise of annotations. The first metric is entropy of instance labels, which is a proxy measure of annotator disagreement about how the instance should be labeled. The second metric is the silhouette coefficient of a label assigned by an annotator to an instance. This metric leverages the idea that instances with the same label should have similar latent representations, and deviations from collective judgments are indicative of errors. Our experiments on three widely used moral foundations datasets show that removing noisy annotations based on the proposed metrics improves classification performance. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our code can be found at: https://github.com/negar-mokhberian/noise-audits.