Miracle-dz/UniFAR-FTUs huggingface dataset:
Word Embeddings for the Software Engineering Domain

The software development process produces vast amounts of textual data expressed in natural language. Outcomes from the natural language processing community have been adapted in software engineering research for leveraging this rich textual information; these include methods and readily available tools, often furnished with pretrained models. State of the art pretrained models however, capture general, common sense knowledge, with limited value when it comes to handling data specific to a specialized domain. There is currently a lack of domain-specific pretrained models that would further enhance the processing of natural language artefacts related to software engineering. To this end, we release a word2vec model trained over 15GB of textual data from Stack Overflow posts. We illustrate how the model disambiguates polysemous words by interpreting them within their software engineering context. In addition, we present examples of fine-grained semantics captured by the model, that imply transferability of these results to diverse, targeted information retrieval tasks in software engineering and motivate for further reuse of the model.
Design and Development of a Cost-Effective Online Electronic Circuits Laboratory

In this paper, we present our work on the design and implementation of a cost-effective remote real time internet-based undergraduate electronic circuits laboratory. The student interacts with the system through a graphical user interface implemented using NI LabVIEW. On the back end, the server processes experiment requests from users and runs them. The proposed online internet-based set of experiments (sixteen experiments are currently available) using NI ELVIS II platform. The laboratory management system is also discussed.
D2K: Scalable Community Detection in Massive Networks via Small-Diameter k-Plexes

This paper studies k-plexes, a well known pseudo-clique model for network communities. In a k-plex, each node can miss at most k-1 links. Our goal is to detect large communities in today's real-world graphs which can have hundreds of millions of edges. While many have tried, this task has been elusive so far due to its computationally challenging nature: k-plexes and other pseudo-cliques are harder to find and more numerous than cliques, a well known hard problem. We present D2K, which is the first algorithm able to find large k-plexes of very large graphs in just a few minutes. The good performance of our algorithm follows from a combination of graph-theoretical concepts, careful algorithm engineering and a high-performance implementation. In particular, we exploit the low degeneracy of real-world graphs, and the fact that large enough k-plexes have diameter 2. We validate a sequential and a parallel/distributed implementation of D2K on real graphs with up to half a billion edges.
A New HEVC In-Loop Filter Based on Multi-channel Long-Short-Term Dependency Residual Networks

In this paper, we propose a new HEVC in-loop filter based on a multi-channel long-short-term dependency residual network (MLSDRN). Inspired by the information storage and information update function of human memory cell, our MLSDRN introduces an update cell to adaptively store and select the long-term and short-term dependency information through an adaptive learning process. In addition, we leverage the block boundary information that recorded in the bit-streams to improve the filter performance, which also makes our MLSDRN to unequally treat the video content. Meanwhile, the multi-channel is introduced to solve the illumination discrepancy problem. We integrate the novel in-loop filter into HM reference software, and applying it to luma and chroma components, simulation results demonstrate that the proposed in-loop filter can save BD-rate reduction up to 15.9% with ALF off. For luma component, the novel in-loop filter achieves 6.0%, 8.1%, 7.4% BD-rate saving for all intra, low delay and random access configurations, respectively.
Aesthetic Highlight Detection in Movies Based on Synchronization of Spectators’ Reactions

Detection of aesthetic highlights is a challenge for understanding the affective processes taking place during movie watching. In this article, we study spectators’ responses to movie aesthetic stimuli in a social context. Moreover, we look for uncovering the emotional component of aesthetic highlights in movies. Our assumption is that synchronized spectators’ physiological and behavioral reactions occur during these highlights because: (i) aesthetic choices of filmmakers are made to elicit specific emotional reactions (e.g., special effects, empathy, and compassion toward a character) and (ii) watching a movie together causes spectators’ affective reactions to be synchronized through emotional contagion. We compare different approaches to estimation of synchronization among multiple spectators’ signals, such as pairwise, group, and overall synchronization measures to detect aesthetic highlights in movies. The results show that the unsupervised architecture relying on synchronization measures is able to capture different properties of spectators’ synchronization and detect aesthetic highlights based on both spectators’ electrodermal and acceleration signals. We discover that pairwise synchronization measures perform the most accurately independently of the category of the highlights and movie genres. Moreover, we observe that electrodermal signals have more discriminative power than acceleration signals for highlight detection.
Multi-view Adversarially Learned Inference for Cross-domain Joint Distribution Matching

Many important data mining problems can be modeled as learning a (bidirectional) multidimensional mapping between two data domains. Based on the generative adversarial networks (GANs), particularly conditional ones, cross-domain joint distribution matching is an increasingly popular kind of methods addressing such problems. Though significant advances have been achieved, there are still two main disadvantages of existing models, i.e., the requirement of large amount of paired training samples and the notorious instability of training. In this paper, we propose a multi-view adversarially learned inference (ALI) model, termed as MALI, to address these issues. Unlike the common practice of learning direct domain mappings, our model relies on shared latent representations of both domains and can generate arbitrary number of paired faking samples, benefiting from which usually very few paired samples (together with sufficient unpaired ones) is enough for learning good mappings. Extending the vanilla ALI model, we design novel discriminators to judge the quality of generated samples (both paired and unpaired), and provide theoretical analysis of our new formulation. Experiments on image-to-image translation, image-to-attribute generation (multi-label classification), attribute-to-image generation tasks demonstrate that our semi-supervised learning framework yields significant performance improvements over existing ones. Results on cross-modality retrieval show that our latent space based method can achieve competitive similarity search performance in relative fast speed, compared to those methods that compute similarities in the high-dimensional data space.
An Exploratory Study on the Influence of Developers in Technical Debt

Software systems are often developed by many developers who have a varying range of skills and habits. These developers have a big impact on software quality. Understanding how different developers and developer characteristics impact the quality of a software is crucial to properly deploy human resources and help managers improve quality outcomes which is essential for software systems success. Addressing this concern, we conduct a study on how different developers and developer characteristics such as developer seniority in a system, frequency of commits, and interval between commits relate to Technical Debt (TD). We performed a large-scale analysis on 19,088 commits from 38 Apache Java systems and applied multiple statistical analysis tests to evaluate our hypotheses. Our empirical evaluation suggests that developers unequally increase and decrease TD, a developer seniority in a software system and frequency of commits are negatively correlated with the TD the developer induces, and a developer commit interval has a positive correlation with the TD the developer induces.
Hyperspectral Anomaly Detection Through Spectral Unmixing and Dictionary-Based Low-Rank Decomposition

Anomaly detection has been known to be a challenging problem due to the uncertainty of anomaly and the interference of noise. In this paper, we focus on anomaly detection in hyperspectral images (HSIs) and propose a novel detection algorithm based on spectral unmixing and dictionary-based low-rank decomposition. The innovation is threefold. First, due to the highly mixed nature of pixels in HSI data, instead of using the raw pixel directly for anomaly detection, the proposed algorithm applies spectral unmixing to obtain the abundance vectors and uses these vectors for anomaly detection. We show that the abundance vectors possess more distinctive features to identify anomaly from background. Second, to better represent the highly correlated background and the sparse anomaly, we construct a dictionary based on the mean shift clustering of the abundance vectors to improve both the discriminative and representative powers of the algorithm. Finally, a low-rank matrix decomposition method based on the constructed dictionary is proposed to encourage the coefficients of the dictionary, instead of the background itself, to be low rank, and the residual matrix to be sparse. Anomalies can then be extracted by summing up the columns of the residual matrix. The proposed algorithm is evaluated on both synthetic and real data sets. Experimental results show that the proposed approach constantly achieves high detection rate, while maintaining low false alarm rate regardless of the type of images tested.
RAFIKI: Retrieval-Based Application for Imaging and Knowledge Investigation

Medical exams, such as CT scans and mammograms, are obtained and stored every day in hospitals all over the world, including images, patient data, and medical reports. It is paramount to have tools and systems to improve computer-aided diagnoses based on such huge volumes of stored information. The Content-Based Image Retrieval (CBIR) is a powerful paradigm to help reaching such a goal, providing physicians with intelligent retrieval tools to present him/her with similar or complementary cases, in which visual characteristics improve textual data. Employing comparative inspection on previous cases, the physician can obtain a more comprehensive understanding of the case he/she is working on. Current hospital systems do not carry native CBIR functionalities yet, relying on add-on subsystems, which often do not adhere to the existing relational database infrastructures. In this work, we propose RAFIKI, a software prototype that extends the Relational Database Management System (RDBMS) PostgreSQL, providing native support for CBIR functionalities, modular extensibility, and seamless integration for data science tools, such as Python and R. We show the applicability of our system by evaluating three clinical scenarios, performing queries over a real-world image dataset of lung exams. Our results spot actual potential in promoting informed decision-making from the physician's perspective. Besides, the system exhibited a higher performance when compared to previous systems found in the literature. Moreover, RAFIKI contributes with a model to establish how to put together CBIR concepts and relational data, providing a powerful design for further development of theoretical and practical concepts and tools.
Dynamic Embeddings for User Profiling in Twitter

In this paper, we study the problem of dynamic user profiling in Twitter. We address the problem by proposing a dynamic user and word embedding model (DUWE), a scalable black-box variational inference algorithm, and a streaming keyword diversification model (SKDM). DUWE dynamically tracks the semantic representations of users and words over time and models their embeddings in the same space so that their similarities can be effectively measured. Our inference algorithm works with a convex objective function that ensures the robustness of the learnt embeddings. SKDM aims at retrieving top-K relevant and diversified keywords to profile users' dynamic interests. Experiments on a Twitter dataset demonstrate that our proposed embedding algorithms outperform state-of-the-art non-dynamic and dynamic embedding and topic models.
Investigation of SMAP Active–Passive Downscaling Algorithms Using Combined Sentinel-1 SAR and SMAP Radiometer Data

The aim of this paper was to test the capabilities of the Sentinel-1 radar data in downscaling Soil Moisture Active Passive (SMAP) radiometer data for high-resolution soil moisture estimation. Three different active–passive downscaling algorithms, including the brightness temperature-based downscaling algorithm (BTBDA), the soil moisture-based downscaling algorithm (SMBDA), and a change detection method (CDM), were analyzed using pairs of Sentinel-1 active and SMAP passive observations collected over a semiarid landscape in southeastern Australia from May 2015 to May 2016. While these algorithms have been tested previously, this is the first study to evaluate the three algorithms using real Sentinel-1 radar and SMAP radiometer data. The SMAP passive observations were disaggregated to 9-, 3-, and 1-km scales and then compared with ground soil moisture measurements. The results suggest that the root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.057, 0.056, and 0.067 cm3/cm3 for the BTBDA, SMBDA, and CDM, respectively. The accuracy of downscaling methods was generally decreased when applied at the finer spatial resolution. The SMBDA had overall better performance in terms of correctly detecting the soil moisture pattern and relatively lower RMSE values, and is, therefore, recommended for the combined Sentinel-1 radar and SMAP radiometer setup for soil moisture monitoring. The influence of incidence angle normalization of Sentinel-1 SAR data on downscaled soil moisture was also investigated and found to be minimal.
A Design Structure Matrix Approach for Measuring Co-change-Modularity of Software Products

Several authors have quantified the modularity of software systems in terms of coupling and cohesion metrics. Most of these approaches focus on functional and procedural dependencies in the system. Although highly relevant at the design phase, these static dependencies alone do not account for how a software product evolves over time. Instead, this is also dictated by logical and hidden dependencies between system files. To a large extent, the co-change (co-commit) relation captures these different types of dependencies. In this paper, we define two measures of co-change-modularity of a software product based on a weighted design structure matrix (DSM). The first metric, called the weighted propagation cost, uses matrix exponential to measure how changes to one system file potentially affect the whole product. The second metric, called the weighted clustering cost, uses the output of the first metric to measure the partitionability of the system based on the co-change relation. In addition, we provide a visual representation of how the co-change structure of a system evolves over time. We discuss the theoretical foundation of our work and highlight its advantages over existing methodologies. We apply our approach to GNU Octave and show the findings to be consistent with the available literature on the evolution of Octave. Our analysis is extensible and applicable to a range of scenarios including open source systems.
The Effect of Common Signals on Power, Coherence and Granger Causality: Theoretical Review, Simulations, and Empirical Analysis of Fruit Fly LFPs Data

When analyzing neural data it is important to consider the limitations of the particular experimental setup. An enduring issue in the context of electrophysiology is the presence of common signals. For example a non-silent reference electrode adds a common signal across all recorded data and this adversely affects functional and effective connectivity analysis. To address the common signals problem, a number of methods have been proposed, but relatively few detailed investigations have been carried out. As a result, our understanding of how common signals affect neural connectivity estimation is incomplete. For example, little is known about recording preparations involving high spatial-resolution electrodes, used in linear array recordings. We address this gap through a combination of theoretical review, simulations, and empirical analysis of local field potentials recorded from the brains of fruit flies. We demonstrate how a framework that jointly analyzes power, coherence, and quantities based on Granger causality reveals the presence of common signals. We further show that subtracting spatially adjacent signals (bipolar derivations) largely removes the effects of the common signals. However, in some special cases this operation itself introduces a common signal. We also show that Granger causality is adversely affected by common signals and that a quantity referred to as “instantaneous interaction” is increased in the presence of common signals. The theoretical review, simulation, and empirical analysis we present can readily be adapted by others to investigate the nature of the common signals in their data. Our contributions improve our understanding of how common signals affect power, coherence, and Granger causality and will help reduce the misinterpretation of functional and effective connectivity analysis.
What did Really Change with the New Release of the App?

The mobile app market is evolving at a very fast pace. In order to stay in the market and fulfill user's growing demands, developers have to continuously update their apps either to fix issues or to add new features. Users and market managers may have a hard time understanding what really changed in a new release though, and therefore may not make an informative guess of whether updating the app is recommendable, or whether it may pose new security and privacy threats for the user. We propose a ready-to-use framework to analyze the evolution of Android apps. Our framework extracts and visualizes various information -such as how an app uses sensitive data, which third-party libraries it relies on, which URLs it connects to, etc.- and combines it to create a comprehensive report on how the app evolved. Besides, we present the results of an empirical study on 235 applications with at least 50 releases using our framework. Our analysis reveals that Android apps tend to have more leaks of sensitive data over time, and that the majority of API calls relative to dangerous permissions are added to the code in releases posterior to the one where the corresponding permission was requested.
Neural Memory Streaming Recommender Networks with Adversarial Training

With the increasing popularity of various social media and E-commerce platforms, large volumes of user behaviour data (e.g., user transaction data, rating and review data) are being continually generated at unprecedented and ever-increasing scales. It is more realistic and practical to study recommender systems with inputs of streaming data. User-generated streaming data presents unique properties such as temporally ordered, continuous and high-velocity, which poses tremendous new challenges for the once very successful recommendation techniques. Although a few temporal or sequential recommender models have recently been developed based on recurrent neural models, most of them can only be applied to the session-based recommendation scenario, due to their short-term memories and the limited capability of capturing users' long-term stable interests. In this paper, we propose a streaming recommender model based on neural memory networks with external memories to capture and store both long-term stable interests and short-term dynamic interests in a unified way. An adaptive negative sampling framework based on Generative Adversarial Nets (GAN) is developed to optimize our proposed streaming recommender model, which effectively overcomes the limitations of classical negative sampling approaches and improves both effectiveness and efficiency of the model parameter inference. Extensive experiments have been conducted on two large-scale recommendation datasets, and the experimental results show the superiority of our proposed streaming recommender model in the streaming recommendation scenario.
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach

Tax fraud is the intentional act of lying on a tax return form with intent to lower one's tax liability. Under-reporting is one of the most common types of tax fraud, it consists in filling a tax return form with a lesser tax base. As a result of this act, fiscal revenues are reduced, undermining public investment. Detecting tax fraud is one of the main priorities of local tax authorities which are required to develop cost-efficient strategies to tackle this problem. Most of the recent works in tax fraud detection are based on supervised machine learning techniques that make use of labeled or audit-assisted data. Regrettably, auditing tax declarations is a slow and costly process, therefore access to labeled historical information is extremely limited. For this reason, the applicability of supervised machine learning techniques for tax fraud detection is severely hindered. Such limitations motivate the contribution of this work. We present a novel approach for the detection of potential fraudulent tax payers using only unsupervised learning techniques and allowing the future use of supervised learning techniques. We demonstrate the ability of our model to identify under-reporting taxpayers on real tax payment declarations, reducing the number of potential fraudulent tax payers to audit. The obtained results demonstrate that our model doesn't miss on marking declarations as suspicious and labels previously undetected tax declarations as suspicious, increasing the operational efficiency in the tax supervision process without needing historic labeled data.
Governing Technology Debt: Beyond Technical Debt

Technical debt has successfully captured the interest of practitioners and researchers alike. We argue that the concept of technical debt holds much more currency within the strategic Information Systems literature. Hence, we have developed a research framework for expanding the concept of technical debt into a new concept we dub “technology debt”. This expanded concept aims at capturing the path-dependencies reported in literature in regard to digital investments, and to make these both researchable and manageable. Technology debt is defined as the constraining effects of previous governance decisions on future decisions, including technical debt as important factor, but not unique. According to the findings, technology debt is a feasible method for highlighting the constraining aspects of IT investments and including these in the investment evaluation by governing body. This offers support for corporate stakeholders involved in the decision-making surrounding IT related investments, particularly in IT governance and management processes.