Reviewerly/exHarmony huggingface dataset:
A Dataset for GitHub Repository Deduplication

GitHub projects can be easily replicated through the site's fork process or through a Git clone-push sequence. This is a problem for empirical software engineering, because it can lead to skewed results or mistrained machine learning models. We provide a dataset of 10.6 million GitHub projects that are copies of others, and link each record with the project's ultimate parent. The ultimate parents were derived from a ranking along six metrics. The related projects were calculated as the connected components of an 18.2 million node and 12 million edge denoised graph created by directing edges to ultimate parents. The graph was created by filtering out more than 30 hand-picked and 2.3 million pattern-matched clumping projects. Projects that introduced unwanted clumping were identified by repeatedly visualizing shortest path distances between unrelated important projects. Our dataset identified 30 thousand duplicate projects in an existing popular reference dataset of 1.8 million projects. An evaluation of our dataset against another created independently with different methods found a significant overlap, but also differences attributed to the operational definition of what projects are considered as related.
Extracting Deep Local Features to Detect Manipulated Images of Human Faces

Recent developments in computer vision and machine learning have made it possible to create realistic manipulated videos of human faces, raising the issue of ensuring adequate protection against the malevolent effects unlocked by such capabilities. In this paper we propose local image features which are shared across manipulated regions as a key element for the automatic detection of manipulated face images. We also design a lightweight architecture with the correct structural biases for extracting such features and derive a multitask training scheme that consistently outperforms image class supervision alone. The trained networks achieve state-of-the-art results in the FaceForensics++ dataset using significantly reduced number of parameters and are shown to work well in detecting fully generated face images.
Neural Data-Driven Captioning of Time-Series Line Charts

The success of neural methods for image captioning suggests that similar benefits can be reaped for generating captions for information visualizations. In this preliminary study, we focus on the very popular line charts. We propose a neural model which aims to generate text from the same data used to create a line chart. Due to the lack of suitable training corpora, we collected a dataset through crowdsourcing. Experiments indicate that our model outperforms relatively simple non-neural baselines.
On Configurable SCA Countermeasures Against Single Trace Attacks for the NTT

The Number Theoretic Transform (NTT) is a critical sub-block used in several structured lattice-based schemes, including Kyber and Dilithium, which are finalist candidates in the NIST’s standardization process for post-quantum cryptography. The NTT was shown to be susceptible to single trace side-channel attacks by Primas et al. in CHES 2017 and Pessl et al. in Latincrypt 2019 who demonstrated full key recovery from single traces on the ARM Cortex-M4 microcontroller. However, the cost of deploying suitable countermeasures to protect the NTT from these attacks on the same target platform has not yet been studied. In this work, we propose novel shuffling and masking countermeasures to protect the NTT from such single trace attacks. Firstly, we exploit arithmetic properties of twiddle constants used within the NTT computation to propose efficient and generic masking strategies for the NTT with configurable SCA resistance. Secondly, we also propose new variants of the shuffling countermeasure with varying granularity for the NTT. We perform a detailed comparative evaluation of the runtime performances for our proposed countermeasures within open source implementations of Kyber and Dilithium from the pqm4 library on the ARM Cortex-M4 microcontroller. Our proposed countermeasures yield a reasonable runtime overhead in the range of 7%–78% across all procedures of Kyber, while the runtime overheads are much more pronounced for Dilithium, ranging from 12%–197% for the key generation procedure and 32%–490% for the signing procedure.
Simultaneous thickness variation and surface profiling of glass plates using Fizeau interferometer with elimination of offset phase error

Fringe analysis using phase modulation through wavelength scanning is extensively utilized for profiling the surface and thickness variation of glass plates. However, the nonlinearity of the phase-modulation error during wavelength scanning causes an offset error in the calculated phase. Therefore, in this research, two types of 19-frame phase-extraction formulas were derived for simultaneous profiling of the thickness variation and surface of a glass plate, focusing on eliminating the offset phase error. These new formulas were visualized in the frequency domain using the Fourier description. The ability of these formulas to eliminate the offset error was confirmed through numerical error analysis. Finally, the surface and thickness variation of a glass plate were simultaneously profiled using the large-aperture Fizeau interferometer and the new 19-frame formulas. The standard deviations obtained for thickness variation and surface profiling were 4.687 and 14.421 nm, respectively, which were lower than those obtained when using other phase-extraction formulas.
SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction

Class imbalance is a prevalent problem in machine learning which affects the prediction performance of classification algorithms. Software Defect Prediction (SDP) is no exception to this latent problem. Solutions such as data sampling and ensemble methods have been proposed to address the class imbalance problem in SDP. This study proposes a combination of Synthetic Minority Oversampling Technique (SMOTE) and homogeneous ensemble (Bagging and Boosting) methods for predicting software defects. The proposed approach was implemented using Decision Tree (DT) and Bayesian Network (BN) as base classifiers on defects datasets acquired from NASA software corpus. The experimental results showed that the proposed approach outperformed other experimental methods. High accuracy of 86.8% and area under operating receiver characteristics curve value of 0.93% achieved by the proposed technique affirmed its ability to differentiate between the defective and non-defective labels without bias.
Chatbots in Healthcare Curricula: The Case of a Conversational Virtual Patient

Artificial Intelligence (AI) is among the top technological trends that are expected to shape the future of teaching and learning. Chatbots, or conversational agents, are software that can interact with a human user turn by turn using natural language, and they are considered as exemplar utilization of AI and Machine Learning (ML) in education. Despite the increased interest around educational applications of chatbots, there is a lack of chatbot integration into formal learning settings. This work introduces the case of a Conversational Virtual Patient (CVP) prototype for training decision-making skills regarding thromboembolism in medical students. In contrast to typical virtual patients which rely on predefined, multiple-choice input, the proposed CVP employs Natural Language Processing and ML techniques to allow students to formulate their own utterances and interact with the virtual patient in natural language. In view of employing participatory design and co-creating open access chatbots for healthcare curricula, this CVP prototype will form the basis for the co-creation sessions, by familiarizing stakeholders with the chatbot technology and enabling the requirement elicitation process. Future steps in further co-developing the CVP prototype are discussed.
Piranha

Feature flags are commonly used in mobile app development and can introduce technical debt related to deleting their usage from the codebase. This can adversely affect the overall reliability of the apps and increase their maintenance complexity. Reducing this debt without imposing additional overheads on the developers necessitates the design of novel tools and automated workflows.
Universal Adversarial Attack Via Enhanced Projected Gradient Descent

It has been shown that there exist small and image-independent perturbations, called universal perturbations, that can fool deep-learning-based classifiers, resulting in a significant decrease in classification accuracy. In this paper, we propose a novel method to compute more effective universal perturbations via enhanced projected gradient descent on targeted classifiers. By maximizing the original loss function of the targeted model, we update the adversarial example with back-propagation and optimize the perturbation by accumulating small updates on perturbed images consecutively. We generate our attack for several modern CNN classifiers using ImageNet and compare the attack performance with other state-of-the-art universal adversarial attack methods. Performance results show that our proposed adversarial attack method can achieve much higher fooling rates as compared to state-of-the-art universal adversarial attack methods and can realize good generalization on cross-model evaluation.
OntoDisco: Improving Web Service Discovery by Hybridization of Ontology Focused Concept Clustering and Interface Semantics

Web services can be regarded as the most vital constituent of the World Wide Web as each of the web services is associated with an important role. With the paradigm shift toward Service Oriented Architecture, Service computing has become quite predominant across the globe. Web service discovery is not just important but also crucial and cumbersome. This paper proposes a new web service discovery approach that is semantics aware. This approach helps to extract the underlying semantics and enables users to utilize web services. The strategy focuses on the creation of a web database comprising of dataset crawled from well-known registry sites. A word-level semantics is applied using clustering based on the NPMI value. The library creation happens based on the semantic similarity values. An ontology for the principal classes of web services is modeled to facilitate clustering based on the initial aggregation of the data and the concepts. Finally, a web service discovery approach is designed that furnished related web services based on user queries. The proposed strategy yields an accuracy of 90.4 % which is much better than the existing approaches.
Can financial inclusion reduce the presence of corruption? Evidence from selected countries in Africa

Purpose The purpose of this paper is to evaluate the impact of financial inclusion (FI) on control of corruption in selected African countries. Design/methodology/approach The study employs secondary data spanning over a period of 2005–2016. These data are sourced from IMF's International Financial Statistics, World Bank Development Indicators, Global Financial Development Database, Transparency International and International Country Risk Guide. The author uses Sarma (2008) approach to construct the FI index for 13 countries in Africa. The author applies random effect, robust least square and instrumental variable (IV) estimations to examine the impact of FI on control of corruption in Africa. Findings The author finds that financial inclusion improves the control of corruption. The author tests for possible FI threshold to avoid the case of extreme FI in Africa. The results show that there is a threshold level if reached, FI would have negative impacts in the control of corruption. This may likely happen mainly due to weak institutions in Africa. The results are robust to alternative proxy for control of corruption and various alternative estimation techniques. Practical implications The finding indicates that FI can serve as part of toolkits for reducing corruption in Africa. Originality/value This study stresses the important role of FI in the economic system. It is the first paper that empirically suggests the role of FI in controlling corruption in Africa.
A deep learning–based, unsupervised method to impute missing values in electronic health records for improved patient management

• We develop a deep learning–based, unsupervised method to impute missing values in patient data. • Our method accounts for essential relationships and important characteristics of patient data. • We examine imputation effectiveness and predictive efficacy for peritonitis patient care and management. • Our method exhibits better imputation effectiveness and predictive efficacy. Electronic health records (EHRs) often suffer missing values, for which recent advances in deep learning offer a promising remedy. We develop a deep learning–based, unsupervised method to impute missing values in patient records, then examine its imputation effectiveness and predictive efficacy for peritonitis patient management. Our method builds on a deep autoencoder framework, incorporates missing patterns, accounts for essential relationships in patient data, considers temporal patterns common to patient records, and employs a novel loss function for error calculation and regularization. Using a data set of 27,327 patient records, we perform a comparative evaluation of the proposed method and several prevalent benchmark techniques. The results indicate the greater imputation performance of our method relative to all the benchmark techniques, recording 5.3–15.5% lower imputation errors. Furthermore, the data imputed by the proposed method better predict readmission, length of stay, and mortality than those obtained from any benchmark techniques, achieving 2.7–11.5% improvements in predictive efficacy. The illustrated evaluation indicates the proposed method’s viability, imputation effectiveness, and clinical decision support utilities. Overall, our method can reduce imputation biases and be applied to various missing value scenarios clinically, thereby empowering physicians and researchers to better analyze and utilize EHRs for improved patient management.
Scalable Ciphertext Compression Techniques for Post-quantum KEMs and Their Applications

A multi-recipient key encapsulation mechanism, or $$\mathsf {m}\mathsf {KEM}$$ , provides a scalable solution to securely communicating to a large group, and offers savings in both bandwidth and computational cost compared to the trivial solution of communicating with each member individually. All prior works on $$\mathsf {m}\mathsf {KEM}$$ are only limited to classical assumptions and, although some generic constructions are known, they all require specific properties that are not shared by most post-quantum schemes. In this work, we first provide a simple and efficient generic construction of $$\mathsf {m}\mathsf {KEM}$$ that can be instantiated from versatile assumptions, including post-quantum ones. We then study these $$\mathsf {m}\mathsf {KEM}$$ instantiations at a practical level using 8 post-quantum $$\mathsf {KEM}$$ s (which are lattice and isogeny-based NIST candidates), and CSIDH, and show that compared to the trivial solution, our $$\mathsf {m}\mathsf {KEM}$$ offers savings of at least one order of magnitude in the bandwidth, and make encryption time shorter by a factor ranging from 1.92 to 35. Additionally, we show that by combining $$\mathsf {m}\mathsf {KEM}$$ with the TreeKEM protocol used by MLS – an IETF draft for secure group messaging – we obtain significant bandwidth savings.
Vehicle Detection and Classification using Improved Faster Region Based Convolution Neural Network

Over the last few years, object detection has arisen as a powerful tool for developing reliable systems like detection of vehicles using computer vision. Convolutional neural network can be used to employ feature synthesis methods to concatenate low level and high level features as well as to detect vehicles of different size and scale. In this paper, an improved faster R-CNN method has been proposed. The proposed method has been evaluated using FLIR_ADAS dataset for both thermal and RGB images. Experiments have been performed on thermal images and results demonstrated significant increase in accuracy as well as non-ambiguous detections as compared to conventional Faster R-CNN method.
Adversarial Feature Training for Generalizable Robotic Visuomotor Control

Deep reinforcement learning (RL) has enabled training action-selection policies, end-to-end, by learning a function which maps image pixels to action outputs. However, it's application to visuomotor robotic policy training has been limited because of the challenge of large-scale data collection when working with physical hardware. A suitable visuomotor policy should perform well not just for the task-setup it has been trained for, but also for all varieties of the task, including novel objects at different viewpoints surrounded by task-irrelevant objects. However, it is impractical for a robotic setup to sufficiently collect interactive samples in a RL framework to generalize well to novel aspects of a task. In this work, we demonstrate that by using adversarial training for domain transfer, it is possible to train visuomotor policies based on RL frameworks, and then transfer the acquired policy to other novel task domains. We propose to leverage the deep RL capabilities to learn complex visuomotor skills for uncomplicated task setups, and then exploit transfer learning to generalize to new task domains provided only still images of the task in the target domain. We evaluate our method on two real robotic tasks, picking and pouring, and compare it to a number of prior works, demonstrating its superiority.
The Bang Calculus Revisited

Call-by-Push-Value (CBPV) is a programming paradigm subsuming both Call-by-Name (CBN) and Call-by-Value (CBV) semantics. The paradigm was recently modelled by means of the Bang Calculus, a term language connecting CBPV and Linear Logic. This paper presents a revisited version of the Bang Calculus, called $$\lambda !$$ , enjoying some important properties missing in the original system. Indeed, the new calculus integrates commutative conversions to unblock value redexes while being confluent at the same time. A second contribution is related to non-idempotent types. We provide a quantitative type system for our $$\lambda !$$ -calculus, and we show that the length of the (weak) reduction of a typed term to its normal form plus the size of this normal form is bounded by the size of its type derivation. We also explore the properties of this type system with respect to CBN/CBV translations. We keep the original CBN translation from $$\lambda $$ -calculus to the Bang Calculus, which preserves normal forms and is sound and complete with respect to the (quantitative) type system for CBN. However, in the case of CBV, we reformulate both the translation and the type system to restore two main properties: preservation of normal forms and completeness. Last but not least, the quantitative system is refined to a tight one, which transforms the previous upper bound on the length of reduction to normal form plus its size into two independent exact measures for them.
Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planning

We study the problem of sequential task assignment and collision-free routing for large teams of robots in applications with inter-task precedence constraints (e.g., task A and task B must both be completed before task C may begin). Such problems commonly occur in assembly planning for robotic manufacturing applications, in which sub-assemblies must be completed before they can be combined to form the final product. We propose a hierarchical algorithm for computing makespan-optimal solutions to the problem. The algorithm is evaluated on a set of randomly generated problem instances where robots must transport objects between stations in a "factory" grid world environment. In addition, we demonstrate in high-fidelity simulation that the output of our algorithm can be used to generate collision-free trajectories for non-holonomic differential-drive robots.
On Predictive RAHT For Dynamic Point Cloud Coding

We studied predictive coding applied to the region-adaptive hierarchical transform (RAHT) which is used for point cloud compression (PCC). RAHT is part of MPEG’s geometry-based PCC test model and an intra-frame prediction scheme for RAHT (URAHT), wherein the prediction residual is encoded rather than the voxel attributes themselves, has been shown to deliver large gains. We extend the scheme to inter-frame prediction and show that a combination of simple zero-motion-vector (ZMV) inter-frame and intra-frame predictions can provide sizeable gains over pure RAHT or over intra-frame-only prediction when compressing dynamic point clouds. An adaptive method is used such that sections where ZMV does not yield good prediction switch to intra-frame prediction, assuring the performance to be at least that of the intra-frame case. Be the gains large (in steady parts) or very small (where there is rapid motion) results show consistent positive gains coming from a simple inter- and intra-frame prediction combination.
Accelerating Sparse CNN Inference on GPUs with Performance-Aware Weight Pruning

Weight pruning is a popular technique to reduce the size and computation complexity of the Convolutional Neural Networks (CNNs). Despite its success in reducing the model size, weight pruning has brought limited benefit to the CNN inference performance, due to the irregularity introduced in the sparse convolution operations. In this work, we aim to improve the performance of sparse convolutions on GPUs by mitigating the irregularity. We find that the existing performance optimization techniques for sparse matrix computations fail to accelerate sparse convolutions, and we observe that the main performance bottleneck is caused by the heavy control-flow instructions. Based on the observation, we proposed a new GEMM-based implementation of sparse convolutions. Our main idea is to extract dense blocks of non-zeros in the sparse convolution kernels, and use dense matrix-matrix multiplication for these dense blocks to achieve high throughput. For cases where many non-zero weights cannot be grouped into dense blocks, we propose a performance-aware re-pruning strategy that removes the least important weights in the sparse kernels to further improve the throughput. The experimental results with five real-world pruned CNN models show that our techniques can significantly improve the layer-wise performance of sparse convolution operations as well as the end-to-end performance of CNN inference.
A survey on text classification and its applications

Text classification (a.k.a text categorisation) is an effective and efficient technology for information organisation and management. With the explosion of information resources on the Web and corporate intranets continues to increase, it has being become more and more important and has attracted wide attention from many different research fields. In the literature, many feature selection methods and classification algorithms have been proposed. It also has important applications in the real world. However, the dramatic increase in the availability of massive text data from various sources is creating a number of issues and challenges for text classification such as scalability issues. The purpose of this report is to give an overview of existing text classification technologies for building more reliable text classification applications, to propose a research direction for addressing the challenging problems in text mining.