Reviewerly/exHarmony huggingface dataset:
Lattice-Based Proof-of-Work for Post-Quantum Blockchains

Proof of Work (PoW) protocols, originally proposed to circumvent DoS and email spam attacks, are now at the heart of the majority of recent cryptocurrencies. Current popular PoW protocols are based on hash puzzles. These puzzles are solved via a brute force search for a hash output with particular properties, such as a certain number of leading zeros. By considering the hash as a random function, and fixing a priori a sufficiently large search space, Grover's search algorithm gives an asymptotic quadratic advantage to quantum machines over classical machines. In this paper, as a step towards a fuller understanding of post quantum blockchains, we propose a PoW protocol for which quantum machines have a smaller asymptotic advantage. Specifically, for a lattice of rank $$n$$ sampled from a particular class, our protocol provides as the PoW an instance of the Hermite Shortest Vector Problem (Hermite-SVP) in the Euclidean norm, with a small approximation factor. Asymptotically, the best known classical and quantum algorithms that directly solve SVP type problems are heuristic lattice sieves, which run in time $$2^{0.292n + o(n)}$$ and $$2^{0.265n + o(n)}$$ respectively. We discuss recent advances in SVP type problem solvers and give examples of where the impetus provided by a lattice based PoW would help explore often complex optimization spaces.
Enhanced Multi-Task Learning Architecture for Detecting Pedestrian at Far Distance

Existing pedestrian detection methods suffer from performance degradation in the presence of small-scale pedestrians who are positioned at far distance from the camera. We present a pedestrian detection framework that is not only robust to small- and large-scale pedestrians, but is also significantly faster than state-of-the-art methods. The proposed framework incorporates semantic segmentation to confidence modules for RPN (Region Proposal Network) head and R-FCN (Region-based Fully Convolutional Networks) head, and a cascaded R-FCN head. The semantic segmentation confidence is extracted and utilized as auxiliary classification prior knowledge for RPN proposal selection and R-FCN head prediction. Finally, the cascaded R-FCN head progressively refine the pedestrian prediction accuracy with negligible computation overhead. The proposed framework is also capable of maintaining high detection performance on down-sampled input images, which leads to further reduction in overall computational complexity. Experiment results on CityPersons and MOT17Det datasets show that the proposed framework achieves competitive detection performance with about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> speedup over state-of-the-art methods.
FGC-Stream: A novel joint miner for frequent generators and closed itemsets in data streams

Mining condensed representations of frequent itemsets (FI), namely frequent closures (FCIs) or generators (FGIs), over a stream is already a challenging task, and mining both is even more so. Yet, FGIs and FCIs jointly serve in many practical settings and underlie popular association rule bases. To date, the only way to approach the task is by the (impractical) combination of two dedicated miners. As a remedy, we propose a novel joint miner implementing a holistic approach –emphasizing the underlying equivalence classes– to the joint maintenance of FGIs and FCIs upon stream updates. With a sliding window mining schema, we build upon known results on window expansion and provide novel results on window shrinking while adapting both to the support-based filtering. Thus, through the collaborative maintenance of both FCI and FGI, our method achieves effort factoring that helps it outperform its sole FGI stream mining competitor and keep up with two FCI ones.
DiGen: Distractor Generator for Multiple Choice Questions in Code Comprehension

We propose an automated tool to assess code comprehension using Multiple-Choice-Questions. The core of our tool, DiGen, is a Named Entity Recognition model and an instructor-created database of well-commented code (whole or partial programs). For each code entry <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E$</tex> in the database, DiGen creates a multiple-choice variant of an ‘Explain in Plain English’ (EiPE) question to assess learners' comprehension of code E. The correct choice is based on tags derived by the model from comments of E, and DiGen automatically generates distractors based on similar tags in the database, with pre-and post-processing routines to further improve distractor quality. To determine the efficacy of DiGen, we asked ten learners to compare the quality of the distractors generated by our tool against another tool that identifies distractors based on both comment similarity and code similarity. Our initial results suggest that our simpler approach leads to a slight improvement in distractor quality.
Blockchain: the Paradox of Consumer Trust in a Trustless System - a Systematic Review

This systematic review investigates consumer trust in blockchain applications and makes recommendations for future research. The review targeted papers focusing on blockchain applications, collecting data from a consumer perspective, and examining trust as an outcome variable. We excluded non-peer-reviewed papers written before 2008 or in languages other than English. Our search in 5 databases yielded 704 studies. 29 studies were retained for the full-text review and 5 studies were included after conflict resolution. The small number of studies retained for analysis highlights the need for further empirical research on consumer trust in blockchain technology. While blockchain remained a mysterious term for most consumers, trust was the main factor determining the use of blockchain applications. Additionally, interface design, service, and information quality, together with the platform's ability to allow consumers to investigate products using blockchain applications. Trust remains the central issue for blockchain as trust in this “trustless” system appears to be a prerequisite for actual use, creating a “trust paradox”. This systematic review on consumer trust in blockchain technology is the first to provide a preliminary synthesis of consumers' needs and expectations for blockchain developers and provides important directions for future research on blockchain applications.
Analysis of Attack Activities for Honeypots Installation in Ethereum Network

In recent years, blockchain-based cryptocurren-cies have attracted much attention. Attacks targeting cryptocurrencies and related services directly profit an attacker if successful. Related studies have reported attacks targeting configuration-vulnerable nodes in Ethereum using a method called honeypots to observe malicious user attacks. They have analyzed 380 million observed requests and showed that attacks had to that point taken at least 4193 Ether. However, long-term observations using honeypots are difficult because the cost of maintaining honeypots is high. In this study, we analyze the behavior of malicious users using our honeypot system. More precisely, we clarify the pre-investigation that a malicious user performs before attacks. We show that the cost of maintaining a honeypot can be reduced. For example, honeypots need to belong in Ethereum's P2P network but not to the mainnet. Further, if they belong to the testnet, the cost of storage space can be reduced.
Near-real-time Anomaly Detection in Encrypted Traffic using Machine Learning Techniques

In the last decade, the adoption of HTTPS for securing Internet communications increased by up to 90%. Threat actors adapted to this transition to HTTPS by writing more sophisticated malware that encrypt their communications with command-and-control centers. On the other hand, network security appliances are limited by the impossibility of inspecting packet payloads for deeper investigations. In this paper, we propose a cybersecurity analytics which monitors encrypted network flows and extracts features to detect possible occurring attacks and anomalies, by combining machine learning with a statistical approach. The analytics is embedded in a network security monitoring platform, named ararnis <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> , which provides cybersecurity analysts with a comprehensive overview of the monitored network and its traffic to support them in the identification of potentially malicious activities taking place. The detection capabilities of the proposed analytics have been tested both on a benign and a malicious dataset. This latter has been assembled by our security analysts and includes packet captures of samples and tools, respectively, developed and used by worldwide leading threat actors. Results show 96.6% accuracy on the malicious dataset, with a false positive rate approximatively equal to 0.001% when the analytics monitors legitimate encrypted network traffic.
Lung Cancer Classification using Support Vector Machine and Hybrid Particle Swarm Optimization-Genetic Algorithm

Cancer is an uncontrolled growth of abnormal cells in the body. It affects different parts of the body and the ones associated with the lungs is known as lung cancer. Some of the factors increasing a person's risk of the disease include smoking, family history of lung cancer, radiation exposure, and HIV infection. Although the diagnosis of this disease has been made in many ways, there are still some errors in diagnosing the disease. Therefore, this study proposed the classification of lung cancer using the machine learning method to avoid these errors. This involved using the CT Scan dataset obtained from Cipto Mangunkusumo Hospital, Jakarta, Indonesia, and the application of the Particle Swarm Optimization-Genetic Algorithm-Support Vector Machine (PSO-GA-SVM) method of classification. The Particle Swarm Optimization-Genetic Algorithm (PSO-GA) method was used to optimize the parameters of the Support Vector Machine. Moreover, the values of accuracy, precision, recall, and fl-score of the method were measured to evaluate its performance and later compared with the SVM without parameter optimization. The results showed that the classification using PSO-GA-SVM had better performance compared to Support Vector Machine without parameter optimization. This is indicated by the values of the accuracy, precision, recall, and f1-score for the PSO-GA-SVM which were found to be 97.69%, 98.46%, 98.82 %, and 97.66% respectively.
A global path planning algorithm based on the feature map

The feature map is a characteristic of high computational efficiency, but it is seldom used in path planning due to its lack of expression of environmental details. To solve this problem, a global path planning algorithm based on the feature map is proposed based on the directionality of line segment features. First, the robot searches the path along the direction of the target position but turns to search in the direction parallel to the obstacle, which it approaches until the line between the robot and the target position does not intersect with obstacles. Then it turns to the target position, keep searching the path. Meanwhile, the problems of the direction selection of turning point, corner point and obstacle circumvention in the searching process are analysed and corresponding solutions are put forth. Finally, a path optimisation algorithm with variable parameters is proposed, making the optimised path shorter and smoother. Simulation experiments demonstrate that the proposed algorithm is superior to A* algorithm in terms of computation time and path length, especially of the computation efficiency.
On the suitability of incremental learning for regression tasks in exoskeleton control

In recent times, a new generation of modern exoskeleton robots has come into existence, that aims to utilize machine learning to learn the specific needs and preferences of its users. A simple way to facilitate a personalization of an exoskeleton to the end user is to make use of incremental algorithms that keep learning throughout their deployment. However, it is not clear, if any standard algorithms are fast enough to keep pace with sudden change points in the data stream, like for example the change in movement pattern from a normal walk to going up the stairs. In this paper, we study how well common incremental regression algorithms are suited to predict such an ongoing data stream. We use both, theoretical benchmarks and real world human movement data, to evaluate how fast an algorithm reacts to change points in the data, and how well it is able to remember reoccurring patterns. The results show that a simple KNN algorithm outperforms all other more sophisticated models.
Composition-Enhanced Graph Collaborative Filtering for Multi-behavior Recommendation

Rapid and accurate prediction of user preferences is the ultimate goal of today’s recommender systems. More and more researchers pay attention to multi-behavior recommender systems which utilize the auxiliary types of user-item interaction data, such as page view and add-to-cart to help estimate user preferences. Recently, graph-based methods were proposed to showcase an advanced capability in representation learning and capturing collaborative signals. However, we argue that these methods ignore the intrinsic difference between the two types of nodes in the bipartite graph and aggregate information from neighboring nodes with the same functions. Besides, these models do not fully explore the collaborative signals implied by the meta-path across different types of behavior, which causes a huge loss of the potential semantic information across behaviors. To address the above limitations, we present a unified graph model named SaGCN (short for Semantic-aware Graph Convolutional Networks). Specifically, we construct separate user-user and item-item graphs by meta-path, and apply separate aggregation and transformation functions to propagate user and item information. To perform better semantic propagation, we design a relation composition function and a semantic propagation architecture for heterogeneous collaborative filtering signals learning. Extensive experiments on two real-world datasets show that SaGCN outperforms a wide range of state-of-the-art methods in multi-behavior scenarios.
A Deep Learning Based Method to Detect Email and SMS Spams

Over the last decade, smartphone overuse has made users dependent on digital communication tools such as Email and SMS. The use of e-mail and SMS services, which are generally used for two-way communication, by individuals and organizations as a one-way communication tool for notification, warning, and a reminder has greatly increased. However, these communication media are vulnerable to malicious attacks called spam. In order to detect these spams quickly and accurately, the success of the previous studies with machine learning could not exceed a certain level. In this study, a method is proposed to more accurately identify spam in e-mails and SMS using the deep learning method. The results of the experiments on the real dataset show that the proposed method is superior to the classical machine learning approaches.
Worrisome Patterns in Developers: A Survey in Cryptography

We surveyed 97 developers who had used cryptography in open-source projects, in the hope of identifying developer security and cryptography practices. We asked them about individual and company-level practices, and divided respondents into three groups (i.e., high, medium, and low) based on their level of knowledge. We found differences between the high-profile developers and the other two groups. For instance, high-profile developers have more years of experience in programming, have attended more security and cryptography courses, have more background in security, are highly concerned about security, and tend to use security tools more than the other two groups. Never-theless, we observed worrisome patterns among all participants such as the high usage of unreliable sources like Stack Overflow, and the low rate of security tool usage.
Identifying non-natural language artifacts in bug reports

Bug reports are a popular target for natural language processing (NLP). However, bug reports often contain artifacts such as code snippets, log outputs and stack traces. These artifacts not only inflate the bug reports with noise, but often constitute a real problem for the NLP approach at hand and have to be removed. In this paper, we present a machine learning based approach to classify content into natural language and artifacts at line level implemented in Python. We show how data from GitHub issue trackers can be used for automated training set generation, and present a custom preprocessing approach for bug reports. Our model scores at 0.95 ROC-AUC and 0.93 F1 against our manually annotated validation set, and classifies 10k lines in 0.72 seconds. We cross evaluated our model against a foreign dataset and a foreign R model for the same task. The Python implementation of our model and our datasets are made publicly available under an open source license.
Interpretable AI Agent Through Nonlinear Decision Trees for Lane Change Problem

The recent years have witnessed a surge in application of deep neural networks (DNNs) and reinforcement learning (RL) methods to various autonomous control systems and game playing problems. While they are capable of learning from real-world data and produce adequate actions to various state conditions, their internal complexity does not allow an easy way to provide an explanation for their actions. In this paper, we generate state-action pair data from a trained DNN/RL system and employ a previously proposed nonlinear decision tree (NLDT) framework to decipher hidden simplistic rule sets that interpret the working of DNN/RL systems. The complexity of the rule sets are controllable by the user. In essence, the inherent bilevel optimization procedure that finds the NLDTs is capable of reducing the complexities of the state-action logic to a minimalist and intrepretable level. Demonstrating the working principle of the NLDT method to a revised mountain car control problem, this paper applies the methodology to the lane changing problem involving six critical cars in front and rear in left, middle, and right lanes of a pilot car. NLDTs are derived to have simplistic relationships of 12 decision variables involving relative distances and velocities of the six critical cars. The derived analytical decision rules are then simplified further by using a symbolic analysis tool to provide English-like interpretation of the lane change problem. This study makes a scratch to the issue of interpretability of modern machine learning based tools and it now deserves further attention and applications to make the overall approach more integrated and effective.
A Linear Primal-Dual Multi-Instance SVM for Big Data Classifications

Multi-instance learning (MIL) is an area of machine learning that handles data that is organized into sets of instances known as bags. Traditionally, MIL is used in the supervised-learning setting and is able to classify bags which can contain any number of instances. This property allows MIL to be naturally applied to solve the problems in a wide variety of real-world applications from computer vision to healthcare. However, many traditional MIL algorithms do not scale efficiently to large datasets. In this paper we present a novel Primal-Dual Multi-Instance Support Vector Machine (pdMISVM) derivation and implementation that can operate efficiently on large scale data. Our method relies on an algorithm derived using a multi-block variation of the alternating direction method of multipliers (ADMM). The approach presented in this work is able to scale to large-scale data since it avoids iteratively solving quadratic programming problems which are generally used to optimize MIL algorithms based on SVMs. In addition, we modify our derivation to include an additional optimization designed to avoid solving a least-squares problem during our algorithm; this optimization increases the utility of our approach to handle a large number of features as well as bags. Finally, we apply our approach to synthetic and real-world multi-instance datasets to illustrate the scalability, promising predictive performance, and interpretability of our proposed method. We end our discussion with an extension of our approach to handle non-linear decision boundaries. Code and data for our methods are available online at: https://github.com/minds-mines/pdMISVM.jl.
Exchange rate uncertainty and foreign direct investment in Africa: Does financial development matter?

Abstract The orthodox view is that uncertainty deters investments and, by extension, private capital inflows. Paying specific attention to the volatility of the domestic exchange rate, foreign direct investment (FDI), and financial development indicators, this study investigates the impact of exchange rate uncertainty on FDI and whether financial development matters in such association. We establish our empirical relationship with a system general methods of moments (GMM) two‐step robust estimator with orthogonal deviations. We found evidence supporting a nonlinear U‐shaped relationship between uncertainty and FDI and that the impact of uncertainty on FDI depends on varying levels of uncertainty. We also document that uncertainty deters FDI flows and that countries with a well‐functioning financial development can transform the adverse impact of volatility on FDI. However, curbing the adverse effect depends on the specific indicator and the threshold value of financial development (financial institutions or financial markets).
A Solidity-to-CPN Approach Towards Formal Verification of Smart Contracts

While Blockchains can open intriguing opportunities of research in many application contexts, they come with the risk of bringing new unconventional problems. In fact, because of the monetary value they hold, Blockchains have been subject to many attacks. Smart contracts, which are at the core of second-generation Blockchains, have been proven to be the origin of such attacks due to the exploitable vulnerabilities their code may hold. It is therefore an essential requirement to prove the correctness of the smart contracts to be deployed on a Blockchain to ensure its protection. The existing approaches have been focusing on targeting generic vulnerabilities like reentrancy, without offering the possibility to check temporal-based contract-specific properties. In this paper, we aim to address smart contracts verification while supporting such properties. We propose and implement a transformation of Solidity smart contracts into Coloured Petri nets and investigate the capability of existing model checking tools to check specific temporal properties of the formally modeled contract.