Reviewerly/exHarmony huggingface dataset:
Web Service API Anti-patterns Detection as a Multi-label Learning Problem

Anti-patterns are symptoms of poor design and implementation solutions applied by developers during the development of their software systems. Recent studies have identified a variety of Web service anti-patterns and defined them as sub-optimal solutions that result from bad design choices, time pressure, or lack of developers experience. The existence of anti-patterns often leads to software systems that are hard to understand, reuse, and discover in practice. Indeed, it has been shown that service designers and developers tend to pay little attention to their service interfaces design. Web service antipatterns detection is a non-trivial and error-prone task as different anti-pattern types typically have interleaving symptoms that can be subjectively interpreted and hence detected in different ways. In this paper, we introduce an automated approach that learns from a set of interleaving Web service design symptoms that characterize the existence of anti-pattern instances in a service-based system. We build a multi-label learning model to detect 8 common types of Web service anti-patterns. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each anti-pattern type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 815 Web services. The statistical tests of our results show that our approach can detect the eight Web service antipattern types with an average F-measure of 93% achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterize Web service anti-patterns include the number of declared operations, the number of port types, and the number of simple and complex types in service interfaces.
IT Project Management Critical Success Factors

IT Project Management Critical Success Factors (CSFs) are the areas of an Information Technology (IT) project that are essential to its success, and that must be identified and followed throughout the life cycle of the project. The identification, definition and communication of CSFs help to ensure that the project manager and team maintain the focus on what is important while avoiding spending resources on other less important areas. Although each IT project is unique and therefore has a unique set of CFSs, in this article, we propose a framework based on a literature review, whose results provide researchers and project managers with a comprehensive understanding of current CSFs of IT projects.
Burn after reading

Return-oriented programming (ROP) is an effective code-reuse attack in which short code sequences (i.e., gadgets) ending in a ret instruction are found within existing binaries and then executed by taking control of the call stack. The shadow stack, control flow integrity (CFI) and code (re)randomization are three popular techniques for protecting programs against return address overwrites. However, existing runtime rerandomization techniques operate on concrete return addresses, requiring expensive pointer tracking.
A Blockchain Technology-Based for University Teaching and Learning Processes

Contemporary, Indonesia is looking for an innovative solution to securing diploma certificate and transcript from forgery. Several cases had been reported due to the usage of a fake transcript. One modus of using fake transcript is to enter a higher level of education and job promotion. The new technology found called Blockchain believes in solving the problem. Characteristics of Blockchain Technology such as secured, immutable, distributed, transparent seem to be matched with transcript document requirement. Therefore, the research tries to create a process model of the university teaching and learning process. Several studies have been done; however, no detailed process shown especially for teaching and learning activities. The research question is, how are teaching, and learning activities can be supported by Blockchain Technology? Qualitative method used in this research, focus group discussion with 9 reputable universities discussed the academic operation process and the result show that it is possible to adopt Blockchain Technology into the teaching and learning process to avoid forgery of university transcript documents.
Improving Diversity and Reducing Redundancy in Paragraph Captions

The purpose of an image paragraph captioning model is to produce detailed descriptions of the source images. Generally, paragraph captioning models use encoder-decoder based architectures similar to the standard image captioning models. The encoder is a CNN based model, and the decoder is a LSTM or GRU. The standard image captioning models produce unsatisfactory results for the paragraph captioning task due to the lack of diversity in the generated outputs [9]. The paragraphs generated from standard image captioning models lack in language diversity and contain redundant information. In this work, we have proposed an approach with language discriminator for increasing the diversity in language, and dissimilarity score using word mover's distance [4] for reducing redundant information. Using this approach with a state-of-the-art model at testing time, we have improved the METEOR score from 13.63 to 19.01 for the Visual Genome dataset.
Experimental Evaluation of GAN-Based One-Class Anomaly Detection on Office Monitoring

In this paper, we test two anomaly detection methods based on Generative Adversarial Networks (GAN) on office monitoring including humans. GAN-based methods, especially those equipped with encoders and decoders, have shown impressive results in detecting new anomalies from images. We have been working on human monitoring in office environments with autonomous mobile robots and are motivated to incorporate the impressive, recent progress of GAN-based methods. Lawson et al.'s work tackled a similar problem of anomalous detection in an indoor, patrol trajectory environment with their patrolbot with a GAN-based method, though crucial differences such as the absence of humans exist for our purpose. We test a variant of their method, which we call FA-GAN here, as well as the cutting-edge method of GANomaly on our own robotic dataset. Motivated to employ such a method for a turnable Video Camera Recorder (VCR) placed at a fixed point, we also test the two methods for another dataset. Our experimental evaluation and subsequent analyses revealed interesting tendencies of the two methods including the effect of a missing normal image for GANomaly and their dependencies on the anomaly threshold.
Intra-Clip Aggregation For Video Person Re-Identification

Video-based person re-identification has drawn massive attention in recent years due to its extensive applications in video surveillance. While deep learning based methods have led to significant progress, these methods are limited by ineffectively using complementary information, which is blamed on necessary data augmentation in training process. Data augmentation has been widely used to mitigate the overfitting trap and improve the ability of network representation. However, the previous methods adopt image-based data augmentation scheme to individually process the input frames, which corrupts the complementary information between consecutive frames and causes performance degradation. In this paper, we propose a novel video-based data augmentation scheme, termed as Synchronous Data Augmentation, to address the challenge above. In order to represent discriminative clip-level features, we also propose a cascade integration module which hierarchically aggregates the intra-clip features with a linear-nonlinear combining projection. Extensive experiments on three benchmark datasets demonstrate that our framework outperforms the most recent state-of-the-art methods. We also perform cross-dataset validation to prove the generality of our method.
A Semantic Engine and an Ontology Visualization Tool for Advanced Crime Analysis

Crime has been and still is one of the major threats and issues for every government and society around the world. Nowadays, a new generation of (cyber)criminals exploits modern and state-of-the-art technology and tools, especially social media channels, in order to achieve their malicious purposes. Thus, Law Enforcement Agencies (LEAs), analysts and security practitioners are facing the need to engage new methods and tools which can support them in the fight and prevention of crime by minimizing the prediction and response time. This paper focuses on two specific functionalities of a crime prediction and fighting software framework which assists LEAs to adopt and utilize emerging technologies such as Data mining and Big Data tools, Semantic Analysis, Visual Intelligence and more in the context of their everyday operations. In this light, the authors of this paper present an innovative Semantic Engine with a person fusion tool as well as an ontology visualization tool as parts of a discussed framework. Both of these tools are presented in detail in separate sections so that the readers can understand better their architecture and their integration into this common framework alongside with other tools. These two tools use state-of-the-art components and software libraries in order to offer robust and future proof solutions for LEAs. Also, they can be integrated and expanded in future research and commercial projects both as independent tools as well as parts of other integrated frameworks.
Dynamic Path-decomposed Tries

A keyword dictionary is an associative array whose keys are strings. Recent applications handling massive keyword dictionaries in main memory have a need for a space-efficient implementation. When limited to static applications, there are a number of highly compressed keyword dictionaries based on the advancements of practical succinct data structures. However, as most succinct data structures are only efficient in the static case, it is still difficult to implement a keyword dictionary that is space efficient and dynamic . In this article, we propose such a keyword dictionary. Our main idea is to embrace the path decomposition technique, which was proposed for constructing cache-friendly tries. To store the path-decomposed trie in small memory, we design data structures based on recent compact hash trie representations. Experiments on real-world datasets reveal that our dynamic keyword dictionary needs up to 68% less space than the existing smallest ones, while achieving a relevant space-time tradeoff.
Automated Object Labeling For Cnn-Based Image Segmentation

Deep learning-based methods for classification and segmentation require large training sets. Generating training data is often a tedious and expensive task. In industrial applications, such as automated visual inspection of products in an assemble line, objects for classification are well defined yet labeled data are difficult to obtain. To alleviate the problem of manual labeling, we propose to train a convolutional neural network with an automatically generated training set using a naive classifier with handcrafted features. We show that when the naive classifier has high precision then the trained network has both high precision and recall despite the low recall of the naive classifier. We demonstrate the proposed methodology on real scenario of detecting a car coolant tank. However, the proposed methodology facilitates collection of train data for a wider type of CNN based methods such as near-duplicate image detection or segmenting tampered areas of images.
Large-Scale Manual Validation of Bugfixing Changes

Context: Accurate data about bug fixes is important for different venues of research, e.g., program repair. While automated procedures are able to identify bug fixing activities, they cannot distinguish between the bug fix and other activities that are happening in parallel, e.g., refactorings or the addition of features. Objective: The creation of a large corpus of manually validated bug fixes and to gain insights into the limitations of manual validation. Method: We use a crowd working approach to manually validate bug fixing commit and analyze the limitations. Limitations: Insights limited to the Java programming language and possibly by the participants in the crowd working.
What constitutes Software?

The term software is ubiquitous, however, it does not seem as if we as a community have a clear understanding of what software actually is. Imprecise definitions of software do not help other professions, in particular those acquiring and sourcing software from third-parties, when deciding what precisely are potential deliverables. In this paper we investigate which artifacts constitute software by analyzing 23 715 repositories from Github, we categorize the found artifacts into high-level categories, such as, code, data, and documentation (and into 19 more concrete categories) and we can confirm the notion of others that software is more than just source code or programs, for which the term is often used synonymously. With this work we provide an empirical study of more than 13 million artifacts, we provide a taxonomy of artifact categories, and we can conclude that software most often consists of variously distributed amounts of code in different forms, such as source code, binary code, scripts, etc., data, such as configuration files, images, databases, etc., and documentation, such as user documentation, licenses, etc.
An Implementation of IoT-Based Product Tracking with Blockchain Integration for a B2B Platform

Digital Business-to-Business (B2B) platforms are increasingly playing an influential role in shaping business collaboration. Companies are relying more on such platforms to approach suppliers, customers, and service providers hence improving cross-sectoral collaboration and market reach. Digital platforms are including disruptive technologies such as the Internet of Things (IoT) as well as Blockchain to improve transparency and trust in business processes. However, notable challenges occur in integrating IoT information with product tracking information as well as storing them on Blockchain. This paper provides an implementation of integrating IoT-based product tracking with Blockchain in an open-source multi-sided B2B platform to address integration challenges. It focuses on product-tracking events and sensor data from the factory environment. A Blockchain service keeps cryptographic hash values for this information to avoid manipulation after the data storage.
Quantitative estimation of clay minerals in airborne hyperspectral data using a calibration field

We evaluated the opportunities and performance of a new type of systematic pixel sharp calibration site for airborne hyperspectral mineral mapping in the environment of an operational copper deposit in Mongolia. The calibration site was designed to be used for estimation of sensitivity and quantification of key minerals in individual pixels in specific geological scenarios. The layout of the calibration site was done with two different copper-containing rock samples, a low copper-containing rock material from the mine, tailing material from the mine, and calibration materials with well-defined known spectral features. The scaled coverage of the sample materials was designed to develop statistical approaches to quantify target minerals in airborne surveys on a pixel-based approach. The data collection included the description of the calibration materials with geochemical, x-ray diffraction, and microscopic and electron raster microscopic methods. Using visible and near-infrared airborne sensors and shortwave infrared airborne sensors, data of the calibration site were collected with multiple repeats from six altitudes. After rectification and atmospheric correction of pixels, sharp measurements of absorption features of clay minerals at 1400, 1900, and 2200 nm were performed and statistically analyzed. Correlations between coverage and absorption features especially around 2200 nm are shown, and influences of flight altitude on sensitivity of the detection and the stability of the measurements are investigated. The results of the calibration field are used for the quantitative estimations of clay minerals in an exploration area near the mine site. The results are also shining new light on methodologies for ground truthing in hyperspectral surveys.
Towards Systemic Handling of Requirements in the Oil and Gas Industry – a Case Study

Abstract This paper presents valuable insights on implementation of requirement management system in the oil and gas industry. The focus on cost reduction in this industry has led to an urgent need for a more systematic requirement handling. The paper presents a survey asking international oil and gas actors for status and needs with respect to requirement management. Furthermore, the paper presents experiences from implementing a Requirement Management System at a major supplier in the industry. The international survey confirms that the oil and gas industry is lagging behind other industries when it comes to implementation of systematic requirement handling. A second result is that the paper shows a clear positive effect when implementing a requirement management system at a single supplier. In conclusion, by implementing such a system, the industry will limit the number of requirement, save cost, and reduce the need for testing and validation. To achieve this, companies will need management support and collaboration across the supply chain. A cross‐industry implementation would result in a significant effect, both in terms of reduced cost and increased flexibility.
A recursive methodology for radical communities’ detection on social networks

In the recent years, radical communities have become very aware of the enormous impact of social networks around the world. Thus, these latter are being frequently explored by these groups. Therefore, penetrating into these communities by analyzing both their interactions and their shared content is a considerably challenging task that serves to counter the online radicalization. For this purpose, in this paper, we propose a new recursive methodology for radical communities’ detection on social networks based on the analysis and extraction of their violent used vocabulary. Our methodology consists mainly on extracting recursively a set of dangerous profiles from Twitter based on their suspicious interactions. Then, we analyze their textual shared data in order to construct a rich glossary containing their violent used vocabulary. This glossary is exploited and enriched to detect recursively radical communities. Finally, in order to evaluate the performance of our methodology, we resort to an expert who verifies both the list of the dangerous extracted profiles and the violent constructed glossary. The given results show the effectiveness and efficiency of our method.
Let the Margin SlidE<sup>±</sup> for Knowledge Graph Embeddings via a Correntropy Objective Function

Embedding models based on translation and rotation have gained significant attention in link prediction tasks for knowledge graphs. Most of the earlier works have modified the score function of Knowledge Graph Embedding models in order to improve the performance of link prediction tasks. However, as proven theoretically and experimentally, the performance of such Embedding models strongly depends on the loss function. One of the prominent approaches in defining loss functions is to set a margin between positive and negative samples during the learning process. This task is particularly important because it directly affects the learning and ranking of triples and ultimately defines the final output. Approaches for setting a margin have the following challenges: a) the length of the margin has to be fixed manually, b) without a fixed point for center of the margin, the scores of positive triples are not necessarily enforced to be sufficiently small to fulfill the translation/rotation from head to tail by using the relation vector. In this paper, we propose a family of loss functions dubbed SlidE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">±</sup> to address the aforementioned challenges. The formulation of the proposed loss functions enables an automated technique to adjust the length of the margin adaptive to a defined center. In our experiments on a set of standard benchmark datasets including Freebase and WordNet, the effectiveness of our approach is confirmed for training Knowledge Graph Embedding models, specifically TransE and RotatE as a case study, on link prediction tasks.
Longest Square Subsequence Problem Revisited

The longest square subsequence (LSS) problem consists of computing a longest subsequence of a given string S that is a square, i.e., a longest subsequence of form XX appearing in S. It is known that an LSS of a string S of length n can be computed using \(O(n^2)\) time [Kosowski 2004], or with (model-dependent) polylogarithmic speed-ups using \(O(n^2 (\log \log n)^2 / \log ^2 n)\) time [Tiskin 2013]. We present the first algorithm for LSS whose running time depends on other parameters, i.e., we show that an LSS of S can be computed in \(O(r \min \{n, M\} \log \frac{n}{r} + n + M \log n)\) time with O(M) space, where r is the length of an LSS of S and M is the number of matching points on S.
Efficient Detection of Pixel-Level Adversarial Attacks

Deep learning has achieved unprecedented performance in object recognition and scene understanding. However, deep models are also found vulnerable to adversarial attacks. Of particular relevance to robotics systems are pixel-level attacks that can completely fool a neural network by altering very few pixels (e.g. 1-5) in an image. We present the first technique to detect the presence of adversarial pixels in images for the robotic systems, employing an Adversarial Detection Network (ADNet). The proposed network efficiently recognize an input as adversarial or clean by discriminating the peculiar activation signals of the adversarial samples from the clean ones. It acts as a defense mechanism for the robotic vision system by detecting and rejecting the adversarial samples. We thoroughly evaluate our technique on three benchmark datasets including CIFAR-10, CIFAR-100 and Fashion MNIST. Results demonstrate effective detection of adversarial samples by ADNet.
Usability and UX of Learning Management Systems: An Eye- Tracking Approach

Learning Management Systems (LMSs) are widely used in higher education. Eye-tracking technology is one technology that could help objectively evaluate the influence of familiarity with an LMS on the usability and user experience (UX) of the LMS. This study utilized such technology to measure user efficiency, user effectiveness, eye-movement patterns, and the quantitative area of interest (AOI) metrics of eye-movement patterns that show how fast the correct area for the task is located. Six students participated in an experiment, whereby they were asked to accomplish ten tasks in the usability testing of a popular open-source LMS called Moodle. The results showed that those who were familiar with the course performed about half of the tasks more efficiently and had better AOI metrics. Additionally, it was found that the participants' eye movement patterns tended to be correlated with their AOI metrics, efficiency and effectiveness of accomplishing the tasks in the usability tests. However, the correlations were not absolute. We found different eye-movement patterns among participants regarding some user interface elements, such as text hyperlinks or images.