Reviewerly/exHarmony huggingface dataset:
Geographic information system for mapping public service location

Abstract This article aims to analyze, design, and development public service location mapping system based on Geographic Information System (GIS). GIS technology is a useful information system used in making information systems for mapping areas and locations accurately so that it can make it easier for people to obtain information on the distribution of places of public services and facilitate the path to the site. Data used is sourced from external data and observational data in the field. The software development model uses the Rapid Application Development approach which consists of four phases namely, planning requirements, user design, construction, and cutover. The results of this study are applications for mapping the location of public services that have features of fast search, geo-maps, know the nearest public service, add data by users with the login feature via social media, and features easy access via Android smartphone. With the development of this application, hoped that it could provide benefits to facilitate and accelerate the community towards the location of public services without being limited by space and time.
An overview of recent distributed algorithms for learning fuzzy models in Big Data classification

Abstract Nowadays, a huge amount of data are generated, often in very short time intervals and in various formats, by a number of different heterogeneous sources such as social networks and media, mobile devices, internet transactions, networked devices and sensors. These data, identified as Big Data in the literature, are characterized by the popular V s features, such as Value, Veracity, Variety, Velocity and Volume. In particular, Value focuses on the useful knowledge that may be mined from data. Thus, in the last years, a number of data mining and machine learning algorithms have been proposed to extract knowledge from Big Data. These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark. In the context of Big Data, fuzzy models are currently playing a significant role, thanks to their capability of handling vague and imprecise data and their innate characteristic to be interpretable. In this work, we give an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data. In particular, we first show some design and implementation details of these learning algorithms. Thereafter, we compare them in terms of accuracy and interpretability. Finally, we argue about their scalability.
Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers

One of the most prominent problems in machine learning in the age of deep learning is the availability of sufficiently large annotated datasets. For specific domains, e.g. animal species, a long-tail distribution means that some classes are observed and annotated insufficiently. Additional labels can be prohibitively expensive, e.g. because domain experts need to be involved. However, there is more information available that is to the best of our knowledge not exploited accordingly. In this paper, we propose to make use of preexisting class hierarchies like WordNet to integrate additional domain knowledge into classification. We encode the properties of such a class hierarchy into a probabilistic model. From there, we derive a novel label encoding and a corresponding loss function. On the ImageNet and NABirds datasets our method offers a relative improvement of $$10.4\%$$ and $$9.6\%$$ in accuracy over the baseline respectively. After less than a third of training time, it is already able to match the baseline's fine-grained recognition performance. Both results show that our suggested method is efficient and effective.
Incrementally Updating the Discovered High Average-Utility Patterns With the Pre-Large Concept

High average-utility itemset mining (HAUIM) is an extension of high-utility itemset mining (HUIM), which provides a reliable measure to reveal utility patterns by considering the length of the mined pattern. Some research has been conducted to improve the efficiency of mining by designing a variety of pruning strategies and effective frameworks, but few works have focused on the maintenance algorithms in the dynamic environment. Unfortunately, most existing works of HAUIM still have to rescan databases multiple times when it is necessary. In this paper, the pre-large concept is used to update the discovered HAUIs in the newly inserted transactions and reduce the time of the rescanning process. To further improve the performance of the developed algorithm, two new upper-bounds are also proposed to decrease the number of candidates for HAUIM. Experiments were performed to compare the previous Apriori-like method and the proposed APHAUP algorithm with the two new upper-bounds in terms of the number of maintenance patterns and runtime in several datasets. The experimental results show that the proposed APHAUP algorithm has excellent performance and good potential to be applied in real applications.
The meaning and measurement of bias

The recent interest in identifying and mitigating bias in computational systems has introduced a wide range of different---and occasionally incomparable---proposals for what constitutes bias in such systems. This tutorial introduces the language of measurement modeling from the quantitative social sciences as a framework for examining how social, organizational, and political values enter computational systems and unpacking the varied normative concerns operationalized in different techniques for measuring "bias." We show that this framework helps to clarify the way unobservable theoretical constructs---such as "creditworthiness," "risk to society," or "tweet toxicity"---are turned into measurable quantities and how this process may introduce fairness-related harms. In particular, we demonstrate how to systematically assess the construct validity and reliability of these measurements to detect and characterize specific types of harms, which arise from mismatches between constructs and their operationalizations. We then take a critical look at existing approaches to examining "bias" in NLP models, ranging from work on embedding spaces to machine translation and hate speech detection. We show that measurement modeling can help uncover the implicit constructs that such work aims to capture when measuring "bias." In so doing, we illustrate the limits of current "debiasing" techniques, which have obscured the specific harms whose measurements they implicitly aim to reduce. By introducing the language of measurement modeling, we provide the FAT* community with a framework for making explicit and testing assumptions about unobservable theoretical constructs embedded in computational systems, thereby clarifying and uniting our understandings of fairness-related harms.
Improved heralded schemes to generate entangled states from single photons

We present a semianalytical methodology to construct optimal linear-optical circuits for the heralded production of three-photon Greenberger-Horne-Zeilinger (3-GHZ) and two-photon Bell states. We provide a detailed description and analysis of the resulting optical schemes, which deliver success probabilities of 1/54 and 2/27 for dual-rail encoded 3-GHZ and Bell-state generation, respectively. Our results improve the known constructive bounds on the success probabilities for 3-GHZ states and are of particular importance for a ballistic quantum computing model [M. Gimeno-Segovia et al., Phys. Rev. Lett. 115, 020502 (2015)], for which these states provide an essential resource.
A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data

Abstract Background Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data. Methods The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy. Results Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%). Conclusions Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future.
Towards Complete Decentralised Verification of Data with Confidentiality: Different ways to connect Solid Pods and Blockchain

Over-centralisation of data leads to tampering and sharing user information without the consent of the owners. This problem has been studied extensively in recent times providing separate solutions involving distributed storage, Blockchain technology and Solid Pods. Individually these solutions are not sufficient to build realistic applications in a decentralised environment; however, a combination of them can effectively provide more powerful and useful use-cases. In this paper, we propose the methods of combining Solid Pods and distributed ledgers in introducing complete decentralisation of data with total user-control, keeping the integrity of the stored information intact through Blockchain-based verification. We demonstrated multiple configurations of our solutions, offering several new use-cases in various sectors. These configurations introduce new dimensions on the Web and mobile applications’ data storage that developers can benefit from building Distributed Applications (DApps) in a complete decentralised environment.
Multi-View Clustering With the Cooperation of Visible and Hidden Views

Multi-view data are becoming common in real-world applications and many multi-view clustering algorithms have thus been proposed. The existing algorithms usually focus on the cooperation of different visible views in the original space but neglect the influence of the hidden information among these visible views, or they only consider the hidden information among the views. The algorithms are therefore not efficient since the available information is not fully exploited, particularly the otherness information in different views and the consistency information among them. In practice, the otherness and consistency information in multi-view data are both very useful for effective clustering analyses. In this study, a Multi-View clustering algorithm with the Cooperation of Visible and Hidden views, i.e., MV-Co-VH, is proposed. The MV-Co-VH algorithm first projects the multiple views from different visible spaces to the common hidden space by using non-negative matrix factorization to obtain the common hidden view data. Collaborative learning is then implemented in the clustering procedure based on the visible views and the shared hidden view. The experimental results of extensive experiments on UCI multi-view datasets and real-world image multi-view datasets show that the clustering performance of the proposed algorithm is competitive with or even better than that of the existing algorithms.
Orthogonal Uncertainty Modeling in the Engineering of Cyber-Physical Systems

Software-intensive cyber-physical systems (CPS) perform essential tasks such as controlling automated production processes in industrial production plants. The required levels of autonomy, openness, and self-adaptation, as well as the dynamic nature of the context of such CPS, result in challenging tasks for their engineering. During operation, unexpected situations in which the system has insufficient knowledge about the current state of the system itself as well as its context may occur. Engineering CPS, e.g., for industrial production sites, must account for such uncertainties the system will have to cope with during its lifetime in a structured and systematic way. Since the development of CPS requires consideration of different system perspectives, current uncertainty modeling approaches cannot be applied right away, as they do not explicitly consider uncertainty aspects that affect different artifacts. To aid the engineering of CPS, this article presents a model-based approach to document uncertainty. We propose “Orthogonal Uncertainty Models,” which closely integrate with other engineering artifacts from different perspectives, as a means for capturing a dedicated uncertainty viewpoint. Our approach has been evaluated in the industry automation domain. The application shows that the idea of regarding uncertainty within a dedicated perspective is highly beneficial. Particularly, our approach helps to uncover and document uncertainties related to behavioral, functional, and structural properties of a system, as well as uncertainties related to business models that would otherwise possibly remain covert. Note to Practitioners-Identifying and documenting uncertainties, which may occur during operation of a system, is a common problem in engineering processes. Such uncertainties may lead to severe damage, and thus need to be mitigated appropriately. It is crucial to account for these uncertainties during engineering, especially in the early phases. Depending on the specific project characteristics, a multitude of different diagram types are used to model a system. Uncertainties thus reflect in many artifacts, which leads to: 1) redundancies in the specified uncertainty attached to diagram elements and 2) uncertainty information (e.g., about the cause or effect of uncertainty) that is spread across different diagrams. The latter makes it difficult to structure uncertainty information and trace it throughout the engineering process so that uncertainty can be systematically considered. Our approach provides a graphical modeling language that employs a dedicated perspective on uncertainty in separate diagrams that can be linked to any engineering artifact.
Automated defect identification via path analysis-based features with transfer learning

Recently, artificial intelligence techniques have been widely applied to address various specialized tasks in software engineering, such as code generation, defect identification, and bug repair. Despite the diffuse usage of static analysis tools in automatically detecting potential software defects, developers consider the large number of reported alarms and the expensive cost of manual inspection to be a key barrier to using them in practice. To automate the process of defect identification, researchers utilize machine learning algorithms with a set of hand-engineered features to build classification models for identifying alarms as actionable or unactionable. However, traditional features often fail to represent the deep syntactic structure of alarms. To bridge the gap between programs' syntactic structure and defect identification features, this paper first extracts a set of novel fine-grained features at variable-level, called path-variable characteristic, by applying path analysis techniques in the feature extraction process. We then raise a two-stage transfer learning approach based on our proposed features, called feature ranking-matching based transfer learning, to increase the performance of cross-project defect identification. Our experimental results for eight open-source projects show that the proposed features at variable-level are promising and can yield significant improvement on both within-project and cross-project defect identification.
The Atbara porphyry gold–copper systems in the Red Sea Hills, Neoproterozoic Arabian–Nubian Shield, NE Sudan

Atbara city is located between the Nile and Atbara rivers, approximately 300 km northeast of Khartoum. The Atbara porphyry gold–copper system is situated at eastern part of Atbara about 70 km in the Red Sea Hills, Neoproterozoic Arabian–Nubian Shield, NE Sudan. The gold‑copper system is observed within different colored quartz veins that fill the tectonic zones in metasomically altered felsic-acidic igneous rocks Gold mineralization in the Atbara is observed in an area of 200 km2 and represented with pyrite, pyrrhotite, chalcopyrite, gold, galena, chalcocite-covellite, tetrahedrite-tennantite, hematite and goethite in the mineralized zone of the study area. The white and brown color quartz veins had E-W, NE-SW and NW-SE directions in these sectors and thickness between 5 and 300 cm. White color quartz samples had low concentration between 0.2 and 1.5 ppm and brown color quartz samples had high gold concentrations ranging from 1.0 to 26.63 ppm. Au does not have positive correlation with other elements, only except for Te and W. In contrast, there is no significant relationship between gold and copper in areas where copper is observed intensively. Cu has positive correlation with Bi, Cd, Pb, U, Hg and Ag. The average ΣREE contents in the hydrothermally altered wall rocks, copper mineralization and the quartz veins ranged from 10.2 ppm for Sector SA (SA, SB, SC, SD, SE and SF are the mining operation areas in the study area), to 14.27 ppm for Sector SB, 1.05 ppm for Sector SC, 20.89 ppm for Sector SD, 3.49 ppm for Sector SE, and 36.33 ppm for the Sector SF. The most of the studied samples have negative Ce and positive Eu anomalies with the LREE enrichment. This showed that it have low oxygen fugacity in the resource area of the hydrothermal solutions and an alkali magmatic source indicating an early stage of magmatic differentiation.
Sentiment Analysis of Teachers Using Social Information in Educational Platform Environments

Learners’ opinions constitute an important source of information that can be useful to teachers and educational instructors in order to improve learning procedures and training activities. By analyzing learners’ actions and extracting data related to their learning behavior, educators can specify proper learning approaches to stimulate learners’ interest and contribute to constructive monitoring of learning progress during the course or to improve future courses. Learners-generated content and their feedback and comments can provide indicative information about the educational procedures that they attended and the training activities that they participated in. Educational systems must possess mechanisms to analyze learners’ comments and automatically specify their opinions and attitude towards the courses and the learning activities that are offered to them. This paper describes a Greek language sentiment analysis system that analyzes texts written in Greek language and generates feature vectors which together with classification algorithms give us the opportunity to classify Greek texts based on the personal opinion and the degree of satisfaction expressed. The sentiment analysis module has been integrated into the hybrid educational systems of the Greek school network that offers life-long learning courses. The module offers a wide range of possibilities to lecturers, policymakers and educational institutes that participate in the training procedure and offers life-long learning courses, to understand how their learners perceive learning activities and specify what aspects of the learning activities they liked and disliked. The experimental study show quite interesting results regarding the performance of the sentiment analysis methodology and the specification of users’ opinions and satisfaction. The feature analysis demonstrates interesting findings regarding the characteristics that provide indicative information for opinion analysis and embeddings combined with deep learning approaches yield satisfactory results.
Subspace Energy Monitoring for Anomaly Detection @Sensor or @Edge

The amount of data generated by distributed monitoring systems that can be exploited for anomaly detection, along with real time, bandwidth, and scalability requirements leads to the abandonment of centralized approaches in favor of processing closer to where data are generated. This increases the interest in algorithms coping with the limited computational resources of gateways or sensor nodes. We here propose two dual and lightweight methods for anomaly detection based on generalized spectral analysis. We monitor the signal energy laying along with the principal and anti-principal signal subspaces, and call for an anomaly when such energy changes significantly with respect to normal conditions. A streaming approach for the online estimation of the needed subspaces is also proposed. The methods are tested by applying them to synthetic data and real-world sensor readings. The synthetic setting is used for design space exploration and highlights the tradeoff between accuracy and computational cost. The real-world example deals with structural health monitoring and shows how, despite the extremely low computations costs, our methods are able to detect permanent and transient anomalies that would classically be detected by full spectral analysis.
A Differentially Private Big Data Nonparametric Bayesian Clustering Algorithm in Smart Grid

Smart systems, including smart grid (SG) and Internet of Things (IoT), have been playing a critical role in addressing contemporary issues. Taking full advantage of the big data generated by the smart grid can enhance the system stability and reliability, increase asset utilization, and offer better customer experience. To better support the data-driven smart grid, the machine learning technologies such as cluster analysis can be applied to process the massive data generated in smart grid. However, the process of cluster analysis may cause the disclosure of personal private information. In this paper, to achieve privacy-preserving cluster analysis in smart grid, we propose IDPC, a Differentially Private Clustering algorithm based on the Infinite Gaussian mixture model (IGMM). IDPC uses a combination of nonparametric Bayesian method and differential privacy. The nonparametric Bayesian method allows certain parameters to change along with the data and it is usually adopted in a clustering algorithm without a fixed number of clusters. The Laplace mechanism is used in data releasing process to make IDPC differentially private. We present how to make the nonparametric Bayesian clustering algorithm differentially private by adding Laplace noise. By security analysis and performance evaluation, IDPC is proved to be privacy-preserving as well as efficient.