Reviewerly/exHarmony huggingface dataset:
Automatic Identification of Code Smell Discussions on Stack Overflow

Background: Code smells indicate potential design or implementation problems that may have a negative impact on programs. Similar to other software artefacts, developers use Stack Overflow (SO) to ask questions about code smells. However, given the high number of questions asked on the platform, and the limitations of the default tagging system, it takes significant effort to extract knowledge about code smells by means of manual approaches. Aim: We utilized supervised machine learning techniques to automatically identify code-smell discussions from SO posts. Method: We conducted an experiment using a manually labeled dataset that contains 3000 code-smell and 3000 non-code-smell posts to evaluate the performance of different classifiers when automatically identifying code smell discussions. Results: Our results show that Logistic Regression (LR) with parameter C=20 (inverse of regularization strength) and Bag of Words (BoW) feature extraction technique achieved the best performance amongst the algorithms we evaluated with a precision of 0.978, a recall of 0.965, and an F1-score of 0.971. Conclusion: Our results show that machine learning approach can effectively locate code-smell posts even if posts' title and/or tags cannot be of help. The technique can be used to extract code smell discussions from other textual artefacts (e.g., code reviews), and promisingly to extract SO discussions of other topics.
Conversational Error Analysis in Human-Agent Interaction

Conversational Agents (CAs) present many opportunities for changing how we interact with information and computer systems in a more natural, accessible way. Building on research in machine learning and HCI, it is now possible to design and test multi-turn CA that is capable of extended interactions. However, there are many ways in which these CAs can "fail" and fall short of human expectations. We systematically analyzed how five different types of conversational errors impacted perceptions of an embodied CA. Not all errors negatively impacted perceptions of the agent. Repetitions by the agent and clarifications by the human significantly decreased the perceived intelligence and anthropomorphism of the agent. Turn-taking errors significantly decreased the likability of the agent. However, coherence errors significantly positively increased likability, and these errors were also associated with positive valence via facial expressions, suggesting that the users found them amusing. We believe this work is the first to identify that turn-taking, repetition, clarification, and coherence errors directly affect users' acceptance of an embodied CA, and are worth taking note by designers of such systems during dialog configuration. We release the Agent Conversational Error (ACE) dataset, a set of transcripts and error annotations of human-agent conversations. The dataset can be found at the GITHUB link: https://github.com/deepalianeja/ACE-dataset
RIoTS: Risk Analysis of IoT Supply Chain Threats

Securing the supply chain of information and communications technology (ICT) has recently emerged as a critical concern for national security and integrity. With the proliferation of Internet of Things (IoT) devices and their increasing role in controlling real world infrastructure, there is a need to analyze risks in networked systems beyond established security analyses. Existing methods in literature typically leverage attack and fault trees to analyze malicious activity and its impact. In this paper, we develop RIoTS, a security risk assessment framework borrowing from system reliability theory to incorporate the supply chain. We also analyze the impact of grouping within suppliers that may pose hidden risks to the systems from malicious supply chain actors. The results show that the proposed analysis is able to reveal hidden threats posed to the IoT ecosystem from potential supplier collusion.
A Conversational Assistant on Mobile Devices for Primitive Learners of Computer Programming

According to eMarketer's survey, the penetration rate of smart phone in Taiwan is one of the top 5 countries in the world, around 77.6% of her population in 2018. Especially among youths, almost everyone has a smartphone. They use mobile devices ubiquitously and it causes problems regarding learning in classroom. Instead of restriction of using smart phones, we encourage students to utilize the devices for learning as frequent as possible. Therefore, a conversational assistant (chatbot) based on IBM Watson Assistant and Facebook Messenger is developed to help non-technical students as they are primitive to computer programming. At the development stage, students appreciate the assistant as an “first-aid” about the course materials and suggest that it should provide more helps about programming logics. In the progress into the stage of test, we will cyclically analyze the conversations between the assistant and users and identify the improvements for better instructional design. The controlled experiment method is utilized to tell if the chatbot could improve the students' engagements and interests to programming for the first version of chatbot.
On the Effect of the Vertical Axis Alignment on Cybersickness and Game Experience in a Supine Posture

Cybersickness is still an inevitable adverse effect when using VR systems, resulting in different levels of discomfort, and potentially breaking the immersive experience.To date, few studies have focused on exploring VR experience in the supine posture.Recent research indicated that simple adoption of VR game initially designed for a seated posture (by rotating 90 degrees) could lead to more severe cybersickness, even to experienced users.Following the insights from previous literature and the widely-accepted sensory conflict theory, we proposed an assumption that might explain such a phenomenon.The hypothesis is that when the perceived virtual coordinate system contradicts the received real-world coordinate through our vestibular system, the conflict appears, which can lead to a sense of discomfort.Hence, the primary goal of the study is to evaluate whether such conflict has an impact on cybersickness.Furthermore, we explored methods of mitigating this conflict through different game designs so as to improve the experience for the supine posture.The final results show that the design that aligned with the real vertical axis is effective in mitigating cybersickness, especially for games that present an acceleration sensation.
AutoMoDe-Arlequin: Neural Networks as Behavioral Modules for the Automatic Design of Probabilistic Finite-State Machines

We present Arlequin, an off-line automatic design method that produces control software for robot swarms by combining behavioral neural-network modules generated via neuro-evolution. The neural-network modules are automatically generated once, in a mission-agnostic way, and are then automatically assembled into probabilistic finite-state machines to perform various missions. With Arlequin, our goal is to reduce the amount of human intervention that is required for the implementation or the operation of previously published modular design methods. Simultaneously, we assess whether neuro-evolution can be used in a modular design method to produce control software that crosses the reality gap satisfactorily. We present robot experiments in which we compare Arlequin with Chocolate, a state of the art modular design method, and EvoStick, a traditional neuro-evolutionary swarm robotics method. The preliminary results suggest that automatically combining neural-network modules into probabilistic finite-state machines is a promising approach to the automatic conception of control software for robot swarms.
Recursive Block Based Keypoint Matching For Copy Move Image Forgery Detection

Images are used as legal proof in areas like forensic investigations. The authenticity of an image intended for investigation may adversely affect the result of such investigations, if the image is a manipulated one. In such fields, the detection of image forgery is very critical and sensitive. Among the prevailing methods, Block based methods divide the images into overlapping regular blocks and finds the match between every block of the whole image. This method is found to be more accurate though computationally expensive. Whereas Keypoint based methods compute the keypoints of an image and find the match between those keypoints. If the image is forged by copy move, there will be highest number of keypoint matches between the corresponding regions. This method is found to be computationally efficient but the accuracy is less. The proposed method utilizes the advantages of both keypoint based and block-based forgery detection methods. We identify meaningful irregular blocks and the similarity of such blocks are measured using the number of matched SIFT keypoints. To identify whether the image is forged or not, an adaptive threshold is employed on the number of keypoint matches and judiciously decide whether to go for block based matching strategy or not for each block. We show that the proposed method achieves better detection rate without compromising the merit on the computational complexity of keypoint based forgery detection.
Controllable Multi-Attribute Editing of High-Resolution Face Images

In recent years, significant progress has been achieved in face image editing due to the success of Generative Adversarial Network (GAN). However, state-of-the-art face editing methods mainly suffer from the following two limitations: 1) they are only applicable to face images with relative low-resolutions and 2) multi-attribute face editing may generate uncontrollable changes in non-target face attribute categories. To solve these problems, we propose a novel High-Quality Generative Adversarial Network (HQ-GAN) for controllable editing of multiple face attributes in high-resolution images. HQ-GAN has two novel ideas to break the limitations of resolution and controllability correspondingly: 1) fine-grained textures and realistic details of high-resolution face images are better preserved with the aid of textural features extracted by the wavelet transform module and 2) desired multi-attribute targets of face editing are emphasized using a weighted binary cross-entropy (BCE) loss so that the influence on non-target attributes is greatly reduced. To the best of our knowledge, HQ-GAN is the first attempt to achieve continuous editing of multiple face attributes on high-resolution images of the CelebA-HQ using only 28 000 training samples. Extensive qualitative results demonstrate the superiority of the proposed method in rendering realistic high-resolution face images with accurate attribute modification, and comprehensive quantitative results show that the proposed method significantly outperforms state-of-the-art face editing methods.
Privacy-Preserving Classification with Secret Vector Machines

Today, large amounts of valuable data are distributed among millions of user-held devices, such as personal computers, phones, or Internet-of-things devices. Many companies collect such data with the goal of using it for training machine learning models allowing them to improve their services. User-held data is, however, often sensitive, and collecting it is problematic in terms of privacy. We address this issue by proposing a novel way of training a supervised classifier in a distributed setting akin to the recently proposed federated learning paradigm, but under the stricter privacy requirement that the server that trains the model is assumed to be untrusted and potentially malicious. We thus preserve user privacy by design, rather than by trust. In particular, our framework, called secret vector machine (SecVM), provides an algorithm for training linear support vector machines (SVM) in a setting in which data-holding clients communicate with an untrusted server by exchanging messages designed to not reveal any personally identifiable information. We evaluate our model in two ways. First, in an offline evaluation, we train SecVM to predict user gender from tweets, showing that we can preserve user privacy without sacrificing classification performance. Second, we implement SecVM's distributed framework for the Cliqz web browser and deploy it for predicting user gender in a large-scale online evaluation with thousands of clients, outperforming baselines by a large margin and thus showcasing that SecVM is suitable for production environments.
‘I love James Blunt as much as I love herpes’ – ‘I love that you're not ashamed to admit you have both’: Attempted insults and responses on Twitter

To date, little research has examined participants' understandings of insults. We examine (potentially) derogatory talk and its consequences in interactions on Twitter, considering instances on the Twitter feed of one high-profile user. Discourse analysis identifies four forms of response to attempted insults: (1) ascribing category membership to first contributor, (2) taking up first contributor self-identification, (3) syntactic echoing, and (4) co-constructing criticism. These responses treat the original remark as literally accurate, and thereby ‘breach’ the usual expectations of talk. Thus, insulting is an outcome that must be accomplished in interaction. Otherwise, derogatory talk can become no more than failed insults.
Unsupervised feature learning for self-tuning neural networks

In recent years transfer learning has attracted much attention due to its ability to adapt a well-trained model from one domain to another. Fine-tuning is one of the most widely-used methods which exploit a small set of labeled data in the target domain for adapting the network. Including a few methods using the labeled data in the source domain, most transfer learning methods require labeled datasets, and it restricts the use of transfer learning to new domains. In this paper, we propose a fully unsupervised self-tuning algorithm for learning visual features in different domains. The proposed method updates a pre-trained model by minimizing the triplet loss function using only unlabeled data in the target domain. First, we propose the relevance measure for unlabeled data by the bagged clustering method. Then triplets of the anchor, positive, and negative data points are sampled based on the ranking violations of the relevance scores and the Euclidean distances in the embedded feature space. This fully unsupervised self-tuning algorithm improves the performance of the network significantly. We extensively evaluate the proposed algorithm using various metrics, including classification accuracy, feature analysis, and clustering quality, on five benchmark datasets in different domains. Besides, we demonstrate that applying the self-tuning method on the fine-tuned network help achieve better results.
Split Behavior of Supervised Machine Learning Algorithms for Phishing URL Detection

Phishing is a fraudulent technique that is an effort by an individual or a group of the individual over the Internet to defraud users to steal their personal information such as username, password, bank account, and ATM card information. Personal information may be used by 'phishers' for their gain, and it is the primary objective of the phishing. It costs the online company and the numerous stakeholders hundreds of million dollars per year. This paper deals with methods to detect phishing URLs by examining different components of URLs using machine learning and deep learning techniques. We have discussed different supervised learning methods used for phishing URL detection based on lexical feature, WHOIS properties, PageRank, Traffic Rank details and page importance properties. Further, we have analyzed how different volumes of training data affect the classification accuracy. The algorithms being analyzed are support vector machine (SVM), K-NN, decision tree classification (DTC), random forest classification (RFC), and artificial neural network (ANN).
Domain-Oriented Semantic Embedding for Zero-Shot Learning

Zero-Shot Learning (ZSL) targets to recognize images from new classes. Existing methods focus on learning a projection function to associate the visual features and category descriptions in the seen domain, which is directly transferred to the unseen domain. However, due to the inherent domain shift, a single shared projection cannot fully capture the domain difference and similarity, thereby making the unseen samples tend to be recognized as seen categories. In this paper, we propose a novel Domain-Oriented Semantic Embedding (DOSE) network that learns specific projections for different domains to better capture the domain characteristics for unbiased ZSL. Besides a domain-shared projection, DOSE learns two auxiliary domain-specific sub-projections to model the semantic-visual association in respective seen and unseen domains. Specifically, the domain-specific projections are learned in a cycle consistency way to capture domain characteristics, and a domain division constraint is developed to penalize the margin between two domain embeddings. Furthermore, to boost semantic-visual association, a semantic-visual dual attention module is designed to automatically remove trivial information in both visual and semantic embeddings under a co-guidance learning manner. Experiments on four public benchmarks prove that the proposed DOSE is robust to the domain shift problem in ZSL and obtains an averaged 5.6% improvement in terms of harmonic mean.
On the use of product-line variants as experimental subjects for clone-and-own research

Software is often released in multiple variants to address the needs of different customers or application scenarios. One frequent approach to create new variants is clone-and-own, whose systematic support has gained considerable research interest in the last decade. However, only few techniques have been evaluated in a realistic setting, due to a substantial lack of publicly available clone-and-own projects which could be used as experimental subjects. Instead, many studies use variants generated from software product lines for their evaluation. Unfortunately, the results might be biased, because variants generated from a single code base lack unintentional divergences that would have been introduced by clone-and-own. In this paper, we report about ongoing work towards a more systematic investigation of threats to the external validity of such experimental results. Using n-way model matching as a representative technique for supporting clone-and-own, we assess the performance of state-of-the-art algorithms on variant sets exposing increasing degrees of divergence. We compile our observations into four hypotheses which are meant to serve as a basis for discussion and which need to be investigated in more detail in future research.
Parametrized topological complexity of collision-free motion planning in the plane

Abstract Parametrized motion planning algorithms have high degrees of universality and flexibility, as they are designed to work under a variety of external conditions, which are viewed as parameters and form part of the input of the underlying motion planning problem. In this paper, we analyze the parametrized motion planning problem for the motion of many distinct points in the plane, moving without collision and avoiding multiple distinct obstacles with a priori unknown positions. This complements our prior work Cohen et al. [3] (SIAM J. Appl. Algebra Geom. 5 , 229–249), where parametrized motion planning algorithms were introduced, and the obstacle-avoiding collision-free motion planning problem in three-dimensional space was fully investigated. The planar case requires different algebraic and topological tools than its spatial analog.
Towards Pervasive Containerization of HPC Job Schedulers

In cloud computing, elasticity is defined as "the degree to which a system is able to adapt to workload changes by provisioning and de-provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible". Adding elasticity to HPC (High Performance Computing) clusters management systems remains challenging even if we deploy such HPC systems in today's cloud environments. This difficulty is caused by the fact that HPC jobs scheduler needs to rely on a fixed set of resources. Every change of topology (adding or removing computing resources) leads to a global restart of the HPC jobs scheduler. This phenomenon is not a major drawback because it provides a very effective way of sharing a fixed set of resources but we think that it could be complemented by a more elastic approach. Moreover, the elasticity issue should not be reduced to the scaling of resources issues. Clouds also enable access to various technologies that enhance the services offer to users. In this paper, our approach is to use containers technology to instantiate a tailored HPC environment based on the user's reservation constraints. We claim that the introduction and use of containers in HPC job schedulers allow better management of resources, in a more economical way. From the use case of SLURM, we release a methodology for 'containerization' of HPC jobs schedulers which is pervasive i.e. spreading widely throughout any layers of job schedulers. We also provide initial experiments demonstrating that our containerized SLURM system is operational and promising.
Asymptotically Optimal and Near-Optimal Aperiodic Quasi-Complementary Sequence Sets Based on Florentine Rectangles

Quasi-complementary sequence sets (QCSSs) can be seen as a generalized version of complete complementary codes (CCCs), which enables multicarrier communication systems to support more users. The contribution of this work is two-fold. First, we propose a systematic construction of Florentine rectangles. Secondly, we propose several sets of CCCs and QCSSs, using Florentine rectangles. The CCCs and QCSSs are constructed over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{N}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N\geq 2$ </tex-math></inline-formula> is any integer. The cross-correlation magnitude of any two of the constructed CCCs is upper bounded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> . By combining the proposed CCCs, we propose asymptotically optimal and near-optimal QCSSs with new parameters.