Reviewerly/exHarmony huggingface dataset:
Shallow or Deep? An Empirical Study on Detecting Vulnerabilities using Deep Learning

Deep learning (DL) techniques are on the rise in the software engineering research community. More and more approaches have been developed on top of DL models, also due to the unprecedented amount of software-related data that can be used to train these models. One of the recent applications of DL in the software engineering domain concerns the automatic detection of software vulnerabilities. While several DL models have been developed to approach this problem, there is still limited empirical evidence concerning their actual effectiveness especially when compared with shallow machine learning techniques. In this paper, we partially fill this gap by presenting a large-scale empirical study using three vulnerability datasets and five different source code representations (i.e., the format in which the code is provided to the classifiers to assess whether it is vulnerable or not) to compare the effectiveness of two widely used DL-based models and of one shallow machine learning model in (i) classifying code functions as vulnerable or non-vulnerable (i.e., binary classification), and (ii) classifying code functions based on the specific type of vulnerability they contain (or "clean", if no vulnerability is there). As a baseline we include in our study the AutoML utility provided by the Google Cloud Platform. Our results show that the experimented models are still far from ensuring reliable vulnerability detection, and that a shallow learning classifier represents a competitive baseline for the newest DL-based models.
Fashion-Guided Adversarial Attack on Person Segmentation

This paper presents the first adversarial example based method for attacking human instance segmentation networks, namely person segmentation networks in short, which are harder to fool than classification networks. We propose a novel Fashion-Guided Adversarial Attack (FashionAdv) framework to automatically identify attackable regions in the target image to minimize the effect on image quality. It generates adversarial textures learned from fashion style images and then overlays them on the clothing regions in the original image to make all persons in the image invisible to person segmentation networks. The synthesized adversarial textures are inconspicuous and appear natural to the human eye. The effectiveness of the proposed method is enhanced by robustness training and by jointly attacking multiple components of the target network. Extensive experiments demonstrated the effectiveness of FashionAdv in terms of robustness to image manipulations and storage in cyberspace as well as appearing natural to the human eye. The code and data are publicly released on our project page <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
MoMAC: Multi-objective optimization to combine multiple association rules into an interpretable classification

A crucial characteristic of machine learning models in various domains (such as medical diagnosis, financial analysis, or real-time process monitoring) is the interpretability. The interpretation supports humans in understanding the meaning behind every single prediction made by the machine, and enables the user to assess trustworthiness before acting on the predictions. This article presents our work in building an interpretable classification model based on association rule mining and multi-objective optimization. The classification model itself is a rule list, making a single prediction based on multiple rules. The rule list consists of If ... THEN statements that are understandable to humans. We choose these rules from a large set of pre-mined rules according to an interestingness measure which is formulated as a function of basic probabilities related to the rules. We learned the interestingness measure through multi-objective optimization, concentrating on two objectives: the classifier’s size in terms of number of rules and prediction accuracy. The model is called MoMAC, “Multi-Objective optimization to combine Multiple Association rules into an interpretable Classification”. The experimental results on benchmark datasets demonstrate that MoMAC outperforms other existing rule-based classification methods in terms of classification accuracy.
Impact of comprehensive information security awareness and cognitive characteristics on security incident management – an empirical study

Organizations deploy a team of dedicated security professionals and spend significant resources safeguarding their digital assets. Despite best efforts, security incidents are on the rise and remain a key challenge. The literature has focused inadequately on the lack of professionals’ awareness of security, system, or situational aspects. Extant literature on the impact of awareness on threat management tasks is disjointed and does not adequately consider the metacognitive awareness and self-efficacy of security professionals. To this effect, we propose and empirically validate a model to study the relationship between security, system, situational awareness, and security professionals’ ability to detect, assess, and mitigate threats. We also investigate the effects of metacognitive awareness and self-efficacy on the relationship between awareness and threat management tasks. We validate the model using a survey of 100 information security professionals. Results indicate a significant relationship between awareness, metacognitive awareness, self-efficacy, and threat management task performance. The analysis also demonstrates that metacognitive awareness and self-efficacy mediated the impact of awareness on threat management task performance. We discuss the effects and implications of this study for practice and research.
BIRCHSCAN: A sampling method for applying DBSCAN to large datasets

The DBSCAN algorithm is a traditional density-based clustering method. This algorithm allows to identify clusters of different shapes, with the ability to manage noisy patterns in the data. DBSCAN usually presents good results, however it performs several distance calculations in the clustering process. This leads to a low efficiency and its application in large datasets is not recommended. This work presents a new method to apply DBSCAN to a reduced set of elements in order to cluster the entire dataset. Therefore, the method allows clustering large datasets with similar results to the outcoe of DBSCAN on the entire dataset. Experiments results suggest that the proposed technique presents good results and consistency compared to other algorithms with similar approach.
A variational quantum algorithm for Hamiltonian diagonalization

Hamiltonian diagonalization is at the heart of understanding physical properties and practical applications of quantum systems. It is highly desired to design quantum algorithms that can speedup Hamiltonian diagonalization, especially those can be implemented on near-term quantum devices. In this work, we propose a variational algorithm for Hamiltonians diagonalization (VQHD) of quantum systems, which explores the important physical properties, such as temperature, locality and correlation, of the system. The key idea is that the thermal states of the system encode the information of eigenvalues and eigenstates of the system Hamiltonian. To obtain the full spectrum of the Hamiltonian, we use a quantum imaginary time evolution algorithm with high temperature, which prepares a thermal state with a small correlation length. With Trotterization, this then allows us to implement each step of imaginary time evolution by a local unitary transformation on only a small number of sites. Diagonalizing these thermal states hence leads to a full knowledge of the Hamiltonian eigensystem. We apply our algorithm to diagonalize local Hamiltonians and return results with high precision. Our VQHD algorithm sheds new light on the applications of near-term quantum computers.
Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems

Increasing availability of machine learning (ML) frameworks and tools, as well as their promise to improve solutions to data-driven decision problems, has resulted in popularity of using ML techniques in software systems. However, end-to-end development of ML-enabled systems, as well as their seamless deployment and operations, remain a challenge. One reason is that development and deployment of ML-enabled systems involves three distinct workflows, perspectives, and roles, which include data science, software engineering, and operations. These three distinct perspectives, when misaligned due to incorrect assumptions, cause ML mismatches which can result in failed systems. We conducted an interview and survey study where we collected and validated common types of mismatches that occur in end-to-end development of ML-enabled systems. Our analysis shows that how each role prioritizes the importance of relevant mismatches varies, potentially contributing to these mismatched assumptions. In addition, the mismatch categories we identified can be specified as machine readable descriptors contributing to improved ML-enabled system development. In this paper, we report our findings and their implications for improving end-to-end ML-enabled system development.
Dynamic secure multi-keyword ranked search over encrypted cloud data

Due to increasing volume of stored data in organizations, data owners upload their data to cloud servers. Sensitive data has to be encrypted prior to uploading to the cloud server because of security requirements. Therefore, capable of searching over the encrypted cloud data is a very important issue. One of the most important challenges that must be addressed is to search whole documents to find related documents to a query. In this paper, a scheme is suggested to eliminate the need of searching for whole documents. A secure multi-keyword ranked search method over the encrypted cloud data is introduced that provides dynamic operation on the document collection. Also, access control mechanism is considered in the proposed scheme. The vector space model is employed to B+tree index and query creating. Due to generation of a B+tree-based index structure, search time is getting sub-linear. First, the cloud server is assumed to be “Honest-but-Curious”. Second, it is considered that the data are stored on an untrusted cloud server. The Merkle B-tree (MB-tree) is utilized to verify search results in the presence of access control rules when the cloud server is untrusted. Experiments are conducted using the real world dataset to confirm efficiency and precision of the offered scheme.
Unitary Excitation Transfer between Coupled Cavities Using Temporal Switching

Tailored time variations can enable efficient control over signal flows, giving rise to exotic wave phenomena. In this work, we demonstrate how abrupt temporal switching of the coupling between two cavities can tailor the energy flow between them beyond the limitations of static scenarios, enabling unitary excitation transfer. The proposed scheme is robust with respect to a wide range of nonidealities, with implications for classical and quantum phenomena, from computing to nanophotonic systems.
Predicting the abundance of copper in soil using reflectance spectroscopy and GF5 hyperspectral imagery

Heavy metals in soil are harmful environmental pollutants due to their toxicity and copper (Cu) is a key indicator. Detecting and mapping the distribution of soil property using remote sensing technique is cost-effective. In this study, GF5 hyperspectral data and field spectroscopy were used to estimate soil Cu concentration of the farmland soils around the tailing reservoir of Tongkuangyu Copper deposit, Shanxi Province, China. Sixty-six soil samples were collected and their reflectance spectra were measured for modeling the relationship between Cu concentration in soil and visible-shortwave infrared reflectance (VIS-SWIR) reflectance data. Spectral index (2209 nm/907 nm) was found sensitive to Cu contents in soil and prediction was conducted by piecewise partial least square regression (P-PLSR). The coefficient of determination (R2) and residual prediction deviation (RPD) of the model developed from laboratory soil spectra were 0.89 and 2.81. The model was then applied to the GF5 satellite hyperspectral data and the spatial distribution of Cu content was mapped with acceptable accuracy R2 (0.83) and RPD (1.56). The result confirms that it is feasible to use satellite data to map soil copper anomaly quantitatively with simple spectral index. The study also demonstrates that GF5 hyperspectral data, due to its high spectral resolution (4–7 nm), good signal-to-noise ratio (SNR 200:1–100:1) and large swath coverage (60 km), can provide an alternative data source for quick and large-scale environment monitoring to support soil remediation policies.
Decentralized Federated Learning for Road User Classification in Enhanced V2X Networks

Federated Learning (FL) techniques are emerging in the automotive context to support connected automated driving services. Yet, when applied to vehicular use cases, conventional centralized FL policies show some drawbacks in terms of latency and scalability. This paper focuses on decentralized FL solutions, which attempt to overcome such limitations, by introducing a distributed computing architecture: vehicles exchange the parameters of a shared Machine Learning (ML) model via V2V links, without the need of a central orchestrator. Sharing all ML parameters, however, might not be feasible when minimal V2X bandwidth usage is required or the model is highly complex (e.g., extremely deep networks) as in advanced scenarios for high levels of automation. We thus propose a modular decentralized FL solution and we discuss its application to road user classification in a cooperative vehicular sensing use case. The proposed FL solution performs the point cloud processing of Lidar sensor inputs using a PointNet compliant architecture. It enables the exchange of a subset of the model parameters, namely selected ML model layers, optimized for communication efficiency, convergence and accuracy. We use real sensor data extracted from a publicly available dataset to validate the method, focusing on non-uniform scenarios where sensor data are highly unbalanced across the connected vehicles. For all cases, FL is shown to outperform the ego-sensing approach with minimal bandwidth usage.
Interlocutor modelling in lexical alignment: The role of linguistic competence

In what is known as lexical alignment in dialogue, speakers tend to re-use their interlocutor's prior lexical expressions, probably in an attempt to facilitate ease and success of comprehension for the interlocutor. But do speakers take into account their interlocutor's linguistic competence when producing language to their interlocutor (e.g., using more lexical alignment towards an interlocutor with more limited linguistic competence to boost communicative success)? In three online experiments, we compared speakers' tendency to lexically align with interlocutors of different linguistic competence: an adult native interlocutor, a child native interlocutor, and an adult non-native interlocutor. Native Mandarin-speaking participants were told that they would take turns with another participant (in fact just recordings) to name and match pictures on the internet. We compared lexical alignment with the three interlocutors in a between-participant design in Experiment 1 and later, in a within-participant design, lexical alignment with child vs. adult native interlocutors in Experiment 2 and lexical alignment with adult non-native vs. native interlocutors in Experiment 3. Speakers more often re-used picture names previously produced by a child than adult native interlocutor, and by an adult non-native than native interlocutor. These interlocutor-dependent effects in lexical alignment were not due to memory strength or speech rate associated with an interlocutor's utterances; instead, these findings suggest that speakers model their interlocutor's linguistic competence and lexically align more with linguistically less competent interlocutors in order to maximize communicative success.
A CNN model for real time hand pose estimation

Recently convolutional neural networks (CNNs) have been employed to address the problem of hand pose estimation. In this work, we introduce an end-to-end deep architecture that can accurately estimate hand pose through the joint use of model-based and fine-tuning methods. In the model-based stage, we make use of the prior information in hand model geometry to ensure the geometric validity of the estimated poses. Next, we introduce a fine-tuning approach that learns to refine the errors between the model and observed hand. Our approach is validated on three challenging public datasets and achieves state-of-the-art performance.
Graph Convolutional Neural Networks with Geometric and Discrimination information

In recent years, geometric deep learning methods have been proposed, which are called Graph Convolutional Neural Networks (GCNNs). GCNNs not only can extract effective features like the classical CNN, but also can effectively reflect the true geometric structure of original data. Although GCNNs consider the geometric structure of original data, they construct the same feature graph to perform graph convolution, and ignore the difference between the local structures of different samples. Therefore, a novel Graph Convolutional Neural Network with Geometric and Discrimination information (GDGCNN) is proposed, which integrates traditional machine learning ideas to further improve the performance of feature extraction. In order to exploit differences between the local structures of different samples and make full use of the geometric structure of original data, GDGCNN constructs different feature graphs for different training batches to fully exploit the local geometry of data. Moreover, the discriminant regularization is introduced into GDGCNN to effectively utilize the discriminant information contained in original data. Therefore, GDGCNN has good discriminative ability and robustness. The experimental results show that GDGCNN can perform feature extraction tasks very well, and it is superior to some existing methods for classification in terms of accuracy and F1-Score.
A decentralized algorithm for distributed ensemble clustering

In this paper, we consider the problem of distributed unsupervised learning where data to be clustered are partitioned over a set of agents having limited connectivity. In order to solve this problem, we consider a novel and extended ensemble clustering procedure in order to make it suitable to a fully distributed scenario. The proposed algorithm can deal with the case where each agent has a local and different dataset. Additionally, to reduce the total amount of exchanged information, only the local prototypes of clusters are forwarded among the neighbors. Cluster similarity indexes are adopted to solve conflicts among agents and to achieve a common structure at the end of the communication process. The experimental results prove the feasibility of this approach, which is able to reach an optimal performance when compared to a fully centralized implementation, that is where data is collected beforehand on a single clustering agent.
DialTest: automated testing for recurrent-neural-network-driven dialogue systems

With the tremendous advancement of recurrent neural networks(RNN), dialogue systems have achieved significant development. Many RNN-driven dialogue systems, such as Siri, Google Home, and Alexa, have been deployed to assist various tasks. However, accompanying this outstanding performance, RNN-driven dialogue systems, which are essentially a kind of software, could also produce erroneous behaviors and result in massive losses. Meanwhile, the complexity and intractability of RNN models that power the dialogue systems make their testing challenging. In this paper, we design and implement DialTest, the first RNN-driven dialogue system testing tool. DialTest employs a series of transformation operators to make realistic changes on seed data while preserving their oracle information properly. To improve the efficiency of detecting faults, DialTest further adopts Gini impurity to guide the test generation process. We conduct extensive experiments to validate DialTest. We first experiment it on two fundamental tasks, i.e., intent detection and slot filling, of natural language understanding. The experiment results show that DialTest can effectively detect hundreds of erroneous behaviors for different RNN-driven natural language understanding (NLU) modules of dialogue systems and improve their accuracy via retraining with the generated data. Further, we conduct a case study on an industrial dialogue system to investigate the performance of DialTest under the real usage scenario. The study shows DialTest can detect errors and improve the robustness of RNN-driven dialogue systems effectively.
Securing IoT-Blockchain Applications Through Honesty-Based Distributed Proof of Authority Consensus Algorithm

Integrating blockchain into Internet of Things (IoT) systems can offer many advantages to users and organizations. It provides the IoT network with the capability to distribute computation over many devices and improves the network's security by enhancing information integrity, ensuring accountability, and providing a way to implement better access control. The consensus mechanism is an essential part of any IoT-blockchain platform. In this paper, a novel consensus mechanism based on Proof-of-Authority (PoA) and Proof-of-Work (PoW) is proposed. The security advantages provided by PoW have been realized, and its long confirmation time can be mitigated by combining it with PoA in a single consensus mechanism called Honesty-based Distributed Proof-of-Authority (HDPoA) via scalable work. The measured results of transaction confirmation time and power consumption, and the analyses of security aspects have shown that HDPoA is a suitable and secure protocol for deployment within blockchain-based IoT applications.
Bots Don’t Mind Waiting, Do They? Comparing the Interaction With Automatically and Manually Created Pull Requests

As a maintainer of an open source software project, you are usually happy about contributions in the form of pull requests that bring the project a step forward. Past studies have shown that when reviewing a pull request, not only its content is taken into account, but also, for example, the social characteristics of the contributor. Whether a contribution is accepted and how long this takes therefore depends not only on the content of the contribution. What we only have indications for so far, however, is that pull requests from bots may be prioritized lower, even if the bots are explicitly deployed by the development team and are considered useful. One goal of the bot research and development community is to design helpful bots to effectively support software development in a variety of ways. To get closer to this goal, in this GitHub mining study, we examine the measurable differences in how maintainers interact with manually created pull requests from humans compared to those created automatically by bots. About one third of all pull requests on GitHub currently come from bots. While pull requests from humans are accepted and merged in 72.53% of all cases, this applies to only 37.38% of bot pull requests. Furthermore, it takes significantly longer for a bot pull request to be interacted with and for it to be merged, even though they contain fewer changes on average than human pull requests. These results suggest that bots have yet to realize their full potential.