Reviewerly/exHarmony huggingface dataset:
Architecture Anti-Patterns: Automatically Detectable Violations of Design Principles

In large-scale software systems, error-prone or change-prone files rarely stand alone. They are typically architecturally connected and their connections usually exhibit architecture problems causing the propagation of error-proneness or change-proneness. In this paper, we propose and empirically validate a suite of architecture anti-patterns that occur in all large-scale software systems and are involved in high maintenance costs. We define these architecture anti-patterns based on fundamental design principles and Baldwin and Clark's design rule theory. We can automatically detect these anti-patterns by analyzing a project's structural relationships and revision history. Through our analyses of 19 large-scale software projects, we demonstrate that these architecture anti-patterns have significant impact on files' bug-proneness and change-proneness. In particular, we show that 1) files involved in these architecture anti-patterns are more error-prone and change-prone; 2) the more anti-patterns a file is involved in, the more error-prone and change-prone it is; and 3) while all of our defined architecture anti-patterns contribute to file's error-proneness and change-proneness, Unstable Interface and Crossing contribute the most by far.
Weakly supervised precise segmentation for historical document images

With the passing of history, precious cultural heritage was left behind to tell ancient stories, especially those in the form of written documents. In this paper, a weakly supervised segmentation system with recognition-guided information on attention area, is proposed for high-precision historical document segmentation under strict intersection-over-union (IoU) requirements. We formulate the character segmentation problem from Bayesian decision theory perspective and propose boundary box segmentation (BBS), recognition-guided BBS (Rg-BBS), and recognition-guided attention BBS (Rg-ABBS), progressively, to search for the segmentation path. Furthermore, a novel judgment gate mechanism is proposed to train a high-performance character recognizer in an incremental weakly supervised learning manner. The proposed Rg-ABBS method is shown to substantially reduce time consumption while maintaining sufficiently high precision of the segmentation result by incorporating both character recognition knowledge and line-level annotation. Experiments show that the proposed Rg-ABBS system significantly outperforms traditional segmentation methods as well as deep-learning-based instance segmentation and detection methods under strict IoU requirements.
Intelligent Kano classification of product features based on customer reviews

Product features can be classified into different categories based on customer opinions. The rapid development of artificial intelligence and machine learning paves the way toward computational analysis of customer reviews for opinion mining. This paper presents an Intelligent Kano framework to extract, quantify, and classify different product features based on customer reviews. The framework is enabled by a novel integration of multiple artificial intelligence and machine learning techniques such as sentiment analysis and anomaly detection. A case study is conducted to validate the framework’s effectiveness. Over 12,000 customer reviews on two coffee machines are analyzed for the classification.
Concept-LDA: Incorporating Babelfy into LDA for aspect extraction

Latent Dirichlet allocation (LDA) is one of the probabilistic topic models; it discovers the latent topic structure in a document collection. The basic assumption under LDA is that documents are viewed as a probabilistic mixture of latent topics; a topic has a probability distribution over words and each document is modelled on the basis of a bag-of-words model. The topic models such as LDA are sufficient in learning hidden topics but they do not take into account the deeper semantic knowledge of a document. In this article, we propose a novel method based on topic modelling to determine the latent aspects of online review documents. In the proposed model, which is called Concept-LDA, the feature space of reviews is enriched with the concepts and named entities, which are extracted from Babelfy to obtain topics that contain not only co-occurred words but also semantically related words. The performance in terms of topic coherence and topic quality is reported over 10 publicly available datasets, and it is demonstrated that Concept-LDA achieves better topic representations than an LDA model alone, as measured by topic coherence and F-measure. The learned topic representation by Concept-LDA leads to accurate and an easy aspect extraction task in an aspect-based sentiment analysis system.
Seismic Phase Picking Using Convolutional Networks

When a seismometer network records an earthquake, operators will manually review the waveforms and identify the wave phases, a task known as phase picking. Manual phase picking is a time-consuming process that can be automated using machine learning; however, automatic methods have not yet achieved human-level performance, and open-source implementations of state-of-the-art algorithms are not always available. Convolutional networks have revolutionized the field of image processing, where the large amounts of readily available data make possible near-human performance in tasks such as classification and segmentation. Fortunately, phase picking is also an area where thousands of phases are manually picked, which makes convolutional networks a good fit for the processing of this type of data. In this paper, we describe Cospy, an open-source convolutional phase picker that uses a two-stage analysis in which the first stage segments a rough area around the phase, and the second stage regresses the precise location. Our approach was evaluated on the Northern California Earthquake Data Center (NCEDC) data set and, when targeting picks closer than 0.1 s, it achieved an F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score of 93.13% for P phases and 91.07% for S phases. Our results show that convolutional networks are on track to achieve human-level performance on the task of seismic phase picking and can contribute to decreasing the need for manual analysis. An open-source implementation of the proposed approach, pretrained on the NCEDC data set, can be downloaded at https://github.com/stbnps/cospy.
Reducing the Overhead of Mapping Quantum Circuits to IBM Q System

We propose an efficient approach to optimize the number of necessary SWAP gates when we perform a quantum circuit on IBM Q system. Our idea is to change the order of quantum gates (if possible) so that each sub-circuit has only gates performing on adjacent qubits. For each sub-circuit, we utilize a SAT solver to find the best qubit placement such that the subcircuit has only gates on adjacent qubits. Each sub-circuit may have a different qubit placement such that we do not need SWAP gates for the sub-circuit. Thus, we insert SWAP gates between two sub-circuits to change the qubit placement which is desirable for the following sub-circuit. To reduce the number of such SWAP gates between two sub-circuits, we utilize A* algorithm.
Smart electronic gastroscope system using a cloud–edge collaborative framework

Computer-aided gastroscopic image analysis is capable of reducing the work intensity of gastroscopists, and it is of great significance for improving the sensitivity and specificity of upper gastrointestinal disease screening. However, most computer-aided gastroscope systems provide intelligent image analysis services that rely on public cloud platforms and suffer from high communication and computing costs. Moreover, these systems are normally unavailable for offline clinical practice. In this study, we propose a smart electronic gastroscope system based on a cloud–edge collaborative framework. In this system, edge computing platforms and cloud platforms work collaboratively to achieve real-time lesion localization and fine-grained disease classification of gastroscopic videos. In addition, we propose a novel approach called cloud–edge collaborative dynamic lesion detection for upper gastrointestinal disease inference. First, to assist real-time lesion detection in the offline mode or discover a suspicious frame in the online mode, we develop a Tinier–YOLO algorithm based on the k-DSC module in edge computing platforms. Second, to further improve the modeling performance, we integrate lesion ROI segmentation strategy into the YOLOv3 algorithm in the cloud platform. By testing clinical data, we prove that our approach exhibits superior performance in mAP and IOU of lesion detection and response time of service.
Can We See More? Joint Frontalization and Hallucination of Unaligned Tiny Faces

In popular TV programs (such as CSI), a very low-resolution face image of a person, who is not even looking at the camera in many cases, is digitally super-resolved to a degree that suddenly the person's identity is made visible and recognizable. Of course, we suspect that this is merely a cinematographic special effect and such a magical transformation of a single image is not technically possible. Or, is it? In this paper, we push the boundaries of super-resolving (hallucinating to be more accurate) a tiny, non-frontal face image to understand how much of this is possible by leveraging the availability of large datasets and deep networks. To this end, we introduce a novel Transformative Adversarial Neural Network (TANN) to jointly frontalize very-low resolution (i.e., 16 × 16 pixels) out-of-plane rotated face images (including profile views) and aggressively super-resolve them (8×), regardless of their original poses and without using any 3D information. TANN is composed of two components: a transformative upsampling network which embodies encoding, spatial transformation and deconvolutional layers, and a discriminative network that enforces the generated high-resolution frontal faces to lie on the same manifold as real frontal face images. We evaluate our method on a large set of synthesized non-frontal face images to assess its reconstruction performance. Extensive experiments demonstrate that TANN generates both qualitatively and quantitatively superior results achieving over 4 dB improvement over the state-of-the-art.
Multivariate anomaly detection for ensuring data quality of dendrometer sensor networks

Ensuring the integrity of data from large sensor networks is a challenging task that is relevant in many domains. Precision agriculture is one instance of this challenge, where dendrometer sensors provide data used for plot-specific irrigation decisions, with critical implications for yields and water savings. To aid the identification of malfunctioning dendrometer sensors, we introduce a pipeline for detecting various types of anomalies and investigating their root causes using visual analytics. Our pipeline is unique not only in that it borrows from web technologies to provide interactivity, but also because it incorporates detection algorithms from several fields, such as robust multivariate statistics, unsupervised machine learning, and social-network analysis.
Intrinsic Dimensionality Estimation within Tight Localities

Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many data mining and machine learning tasks, including dimensionality reduction, outlier detection, similarity search and subspace clustering.However, since their convergence generally requires sample sizes (that is, neighborhood sizes) on the order of hundreds of points, existing ID estimation methods may have only limited usefulness for applications in which the data consists of many natural groups of small size.In this paper, we propose a local ID estimation strategy stable even for 'tight' localities consisting of as few as 20 sample points.The estimator applies MLE techniques over all available pairwise distances among the members of the sample, based on a recent extreme-valuetheoretic model of intrinsic dimensionality, the Local Intrinsic Dimension (LID).Our experimental results show that our proposed estimation technique can achieve notably smaller variance, while maintaining comparable levels of bias, at much smaller sample sizes than state-of-the-art estimators.
Renewable energy based mine reclamation strategy: A hybrid fuzzy-based network analysis

Earth and its resources are the most important assets of humankind; they are valuable not only for the present generation but also for future communities. Therefore, a mined land area should be closed and reclaimed to protect the environment from probable contaminants. This study develops a new approach by integrating an intelligent multi-goal fuzzy cognitive map and a fuzzy analytical network process to evaluate and prioritize mined land reclamation strategies. The approach is exemplified based on the data of a limestone mine area by examining sustainable development goals, several criteria and related sub-criteria, and important reclamation strategies. Results of the fuzzy cognitive map indicate that the criteria have nearly similar effects on sustainable goals. The prioritization results of the developed approach indicate that the strategy of "mine closure and installation of solar cells" is the most appropriate alternative for reclaiming the studied mine area. An expert team has approved results along with an evaluation of the effective factors and conditions of the area.
Graph-based clustering of extracted paraphrases for labelling crime reports

Paraphrases are well-known as synonyms that express the same context in different articulations. Extracting paraphrases from a large text corpus is a challenging task in Natural Language Processing applications. The present work proposes a graph based clustering technique for discovering labels of crime reports based on extracted paraphrases from large untagged crime corpora. Initially, the entity pairs are represented as shallow parse trees where the headword in each tree reflects the actual meaning of the phrase between the entities. Though the phrases having similar headwords have been collected together, there exist many phrases between the entities that express similar context without sharing the same headword. Therefore, clustering is done to create a group of similar meaning phrases termed as paraphrases. A complete weighted graph is constructed with the phrases as nodes and cosine similarity between pair of phrases as the weight of an edge with the phrases as terminal nodes. The graph is made sparse by removing edges with weights less than a threshold value and clustering coefficient has been calculated for each node. The subgraph(s) comprising node(s) with the highest clustering coefficient has been extracted with their adjacent edges. The remaining nodes with their adjacent edges in the graph are added one at a time to an extracted subgraph, if and only if the average clustering coefficient of the resultant subgraph increases and an agglomerative merging technique is applied to merge the extracted subgraphs until no merging takes place. Finally, each subgraph represents a cluster of phrases, yields one aspect of crime. Based on the extracted paraphrases, the reports can be easily labelled. The proposed work deals with crime reports for United States of America (USA), United Arab Emirates (UAE) and India and the evaluation is performed in terms of various supervised and unsupervised techniques.
Deep Multi-view Information Bottleneck

In many classification problems, the predictions can be enhanced by fusing information from different data views. In particular, when the information from different views complement each other, it is expected that multi-view learning will lead to improved predictive performance. In this paper, we proposed a supervised multi-view learning framework based on the information bottleneck principle to filter out irrelevant and noisy information from multiple views and learn an accurate joint representation. Specifically, our proposed method maximizes the mutual information between the labels and the learned joint representation while minimizing the mutual information between the learned latent representation of each view and the original data representation. As the relationships between different views are often complicated and nonlinear, we employed deep neural networks to learn the latent representation and to disentangle their complex dependencies. However, since the computation of mutual information can be intractable, we employed the variational inference method to efficiently solve the optimization problem. We performed extensive experiments on various synthetic and real-world datasets to demonstrate the effectiveness of the framework.
PTZ-Surveillance coverage based on artificial intelligence for smart cities

Surveillance cameras have a plethora of usages in newly born cities including smart traffic, healthcare, monitoring, and meeting security needs. One of the most famous new cites is the Egypt's new administration capital "New Cairo". The new administration capital of Egypt mainly characterizes with the green life style via the "Green River ". In this paper, a new enhanced Artificial Intelligence (AI) algorithm is introduced for adjusting the orientation of Pan–Tilt–Zoom (PTZ) surveillance cameras in new Cairo. In other words, the new proposed algorithm is used for improving the field of view (FOV) coverage of PTZ cameras network. For validating the proposed algorithm, it is tested on many scenarios with different criterions. After that, the proposed algorithm is applied to adjust the PTZ monitoring cameras in the green river which locates on new administrative capital as an equivalent to the river Nile. In addition, it compared with several other AI algorithms through the appropriate statistical analysis. The overall experimental results indicate the prosperity of the proposed algorithm for increasing the coverage of the PTZ surveillance system.
Faculty Training for Online Teaching: Roles, Competences, Contents

An important trend in higher education is an increasing use of digital technologies and an expansion of online-learning formats, which poses new challenges for university faculty to master the pedagogical competences in teaching online. Universities are forced to expand their educational activities in the online environment, and to involve an increasing number of teachers in the design and delivery of online courses. However, faculty members often do not have the necessary skills and competencies, and their experience in the use of digital technology is insufficient. Meanwhile, the success of online teaching depends not only on advanced methods and technologies, but first of all on the quality of faculty involved. An important issue is how teachers are trained to perform these tasks. To ensure the effectiveness of online education, the Institute of Further Professional Education of the Kazan National Research Technological University is developing approaches to training faculty for the transition to virtual learning environment. They should take into account factors, incentives and barriers affecting faculty’s participation in online teaching, and analyze changes in the activities of teaching stuff in the online environment. The paper dwells on the foreign experience in training faculty for online teaching. We analyzed publications considering new roles and competencies of online teachers, barriers and motivations that encourage faculty to participate in online learning. The paper gives an overview of the content of foreign training courses aimed at the formation of a complex of subject, pedagogical and technological competences of faculty related to online teaching. The main goal of the analysis was to determine the core competencies of online teachers, so that to reflect them in the program of training faculty for teaching online.
GConsent - A Consent Ontology Based on the GDPR

Consent is an important legal basis for the processing of personal data under the General Data Protection Regulation (GDPR), which is the current European data protection law. GPDR provides constraints and obligations on the validity of consent, and provides data subjects with the right to withdraw their consent at any time. Determining and demonstrating compliance to these obligations require information on how the consent was obtained, used, and changed over time. Existing work demonstrates feasibility of semantic web technologies in modelling information and determining compliance for GDPR. Although these address consent, they currently do not model all the information associated with it. In this paper, we address this by first presenting our analysis of information associated with consent under the GDPR. We then present GConsent, an OWL2-DL ontology for representation of consent and its associated information such as provenance. The paper presents the methodology used in the creation and validation of the ontology as well as an example use-case demonstrating its applicability. The ontology and this paper can be accessed online at https://w3id.org/GConsent .
Blinded and confused

The always-on, always-connected nature of smart home devices complicates Internet-of-Things (IoT) security and privacy. Unlike traditional hosts, IoT devices constantly send sensor, state, and heartbeat data to cloud-based servers. These data channels require reliable, routine communication, which is often at odds with an IoT device's storage and power constraints. Although recent efforts such as pervasive encryption have addressed protecting data intransit, there remains little insight into designing mechanisms for protecting integrity and availability for always-connected devices. This paper seeks to better understand smart home device security by studying the vendor design decisions surrounding IoT telemetry messaging protocols, specifically, the behaviors taken when an IoT device loses connectivity. To understand this, we hypothesize and evaluate sensor blinding and state confusion attacks, measuring their effectiveness against an array of smart home IoT device types. Our analysis uncovers pervasive failure in designing telemetry that reports data to the cloud, and buffering that fails to properly cache undelivered data. We uncover that 22 of 24 studied devices suffer from critical design flaws that (1) enable attacks to transparently disrupt the reporting of device status alerts or (2) prevent the uploading of content integral to the device's core functionality. We conclude by considering the implications of these findings and offer directions for future defense. While the state of the art is rife with implementation flaws, there are several countermeasures IoT vendors could take to reduce their exposure to attacks of this nature.
Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

Mobile robot motion planning in an unstructured, static, and dynamic environment is faced with a large amount of uncertainties. In an uncertain working area, a method should be selected to address the existing uncertainties in order to plan a collision-free path between the desired two points. In this paper, we propose a mobile robot path planning method in the visualize plane using an overhead camera based on interval type-2 fuzzy logic (IT2FIS). We deal with a visual-servoing based technique for obstacle-free path planning. It is necessary to determine the location of a mobile robot in an environment surrounding the robot. To reach the target and for avoiding obstacles efficiently under different shapes of obstacle in an environment, an IT2FIS is designed to generate a path. A simulation of the path planning technique compared with other methods is performed. We tested the algorithm within various scenarios. Experiment results showed the efficiency of the generated path using an overhead camera for a mobile robot.