Miracle-dz/UniFAR-FTUs huggingface dataset:
Rule-based Monitoring Framework for Business Process Compliance

Business processes compliance monitoring can be viewed as a task of detecting and reacting to the compliance of running business processes with compliance rules, which are the semantic constraints originated from norms, standards, and laws, etc. Normally, compliance rules not only refer to normal process perspectives, like control ow, data ow, and time, but also perspectives of data aggregation as well as their mixtures. Such characteristics as well as potentially high number of concurrently running process instances, post challenges for processes compliance monitoring from the aspects of specification and monitoring efficiency. In this work, we address these challenges by proposing a compliance monitoring framework (bpCMon), including an event-based compliance language (ECL) and event reaction system (ERS), wherein ECL is a formal language enabling specifying compliance rules of multi-perspective, and ERS is a powerful rule-based system enriched with events indexing structure, and fully supports the monitoring for compliance rules in ECL. Experiments on a real life datasets indicate the applicability of bpCMon, and the comparisons with three related works over benchmarks demonstrate the efficiency of bpCMon.
A Secure High-Order Lanczos-Based Orthogonal Tensor SVD for Big Data Reduction in Cloud Environment

Singular value decomposition (SVD) has been applied to cyber security and cyber forensics since it can reduce data. However, SVD is hard to reduce high-order big data because it is designed for only matrix data initially. Reducing high-order big data is desired for cyber security applications, and is a very challenging issue. In this paper, we propose a novel orthogonal tensor SVD method using big data techniques for high-order big data (naturally represented as tensors) reduction, which can be extensively used in big data applications of cyber security and cyber forensics. More specifically, we first present a high-order lanczos-based orthogonal tensor SVD algorithm to reduce high-order data. Then, for utilizing the incomparable benefits of cloud, we develop a secure orthogonal tensor SVD method to outsource the computation task of the orthogonal tensor SVD algorithm to cloud. The secure orthogonal tensor SVD method can protect data security from untrusted cloud by applying garbled circuits to the orthogonal tensor SVD algorithm. This is, to our best knowledge, the first work to address high-order big data reduction by employing cloud computing. Finally, we analyze the security and efficiency of our proposed orthogonal tensor SVD on synthetic dataset and real network intrusion detection dataset, and the results demonstrate that our proposed method is very promising for big data reduction.
A Robust Multi-Biometric System with Compact Code for Iris and Face

Multimodal biometric can overcome the limitations of the single biometric trait and gives better classification accuracy. This paper proposes a face-iris multimodal biometric system based on fusion at the matching score level. The iris recognition system is composed of segmentation, normalization, feature encoding and matching. The wavelet transform is used in feature extraction to generate a compact feature vector length of 128 bits; this reduces the computational time and storage of the iris code. The face recognition system is composed of enhancement, feature encoding and matching. The new method called ‘Phase-based Gabor Fisher Classifier (PBGFC)’ is used in feature extraction; PBGFC employs only 16 Gabor filters, i.e., filters with 2 scales and eight orientations. This fact makes the resulting feature vector for the PBGFC method very compact. The scores from iris and face are then combined using several score normalization and fusion techniques. To validate our approach, experiments are conducted on the iris and face images obtained from the CASIA and ORL datasets respectively. The results show that our multimodal biometric system achieves higher accuracy than both single biometric approaches and the other existing multi-biometric systems based on fusion of iris and face.
Statistical Machine Learning and Data Analytic Methods for Risk and Insurance

Lecture series on Statistical Machine Learning for Risk and Insurance. PART I. Unsupervised Learning Methods In Risk and Insurance: The goal of this part of the lecture series is to develop core aspects of dimension reduction and feature extraction methodology from modern machine learning and data analytic approaches that can be directly applicable to working with Risk and Insurance modelling contexts. Feature Extraction and Dimension Reduction Methods in Risk and Insurance: In this section we will cover aspects of feature extraction methodology and dimension reduction methods that have the following core attributes: 1. capable of extracting relevant feature information that can be understood from a clear statistical perspective; 2. capable of treating outliers in the data (robust methods); 3. capable of treating missing data in the records; 4. computationally efficient to extract features. Detailed Real Applications to Illustrate the Ideas: a) Examples are developed in areas of: b) Premium and rate making applications in insurance c) Cyber risk and Operational Risk d) Claims reserving e) Home and contents insurance f) Mortality and Demographics in life insurance and annuities g) Telematics h) Catastrophe insurance i) Agricultural Insurance
A review on comparative performance analysis of associative classifiers

In this study we provided comparative study of associative classifiers which can be exploited for the discovery of business rules from the huge structured and unstructured data that can be used in the business analytic. Associative classification is a hybrid approach combining the classification rules mining and association rules mining that are two important data mining tasks. There are various emerging classification problems in various domains of knowledge like medical data, images, audio, video and textual data. Associative Classification approaches are exploited in various fields for the classification purposes. We compare the selective associative classification methods namely CBA, CBA2, CMAR-C, CFAR-C, CPAR-C, and Fuzzy-FARCHD-C by exploiting the implementation of these methods in KEEL data mining tool on public datasets. Our experimental results reveals that the performance of the Fuzzy-FARCHD-C is promising than other methods in terms of accuracy. The performance of the associative classifiers drastically decreases on the datasets with higher number classes and attributes.
Dynamic Load Balancing Method Based on PBT Tree

With the development of computer technology and GPU technology, hardware and software technology has been greatly developed, but a single PC machine still cannot meet the real-time computing requirements of high-complexity scenes. In this paper, we propose a dynamic load balancing strategy based on PBT tree for the load balancing problem of Sort-first architecture cluster computing system. The dynamic load balancing strategy is dynamically adjusted in real time according to the change of computing scenarios, Balance caused by the system performance cannot be improved, make full use of system resources and improve the overall performance of the system. Keywords-cluster; load balancing; sort-first; parallel computing
Designing a Change-Driven Language for Model Consistency Repair Routines

Ein Software-System kann wahrend der Entwicklung durch verschiedene Modelle beschrieben werden, um unterschiedliche Teile oder Abstraktionen des Systems darzustellen. Diese Modelle konnen voneinander abhangige oder sogar redundante Informationen enthalten. Wenn ein Softwareentwickler an solchen Modellen Anderungen vornimmt ohne diese Abhangigkeiten zu beachten, konnen die Modelle inkonsistent werden. Ein Entwickler ist kaum in der Lage einen Uberblick uber alle Modelle eines Entwicklungsprozesses und deren Abhangigkeiten zu behalten. Daher sind automatisierte Verfahren zur Wiederherstellung von Konsistenz nach Modellanderungen notwendig. Existierende Methoden erlauben die deklarative Spezifikation von Abhangigkeiten zwischen Modellen, aus denen automatisiert Mechanismen zur Konsistenzerhaltung abgeleitet werden. Diese Ansatze sind in ihrer Ausdrucksmachtigkeit beschrankt und bieten nur eingeschrankten Einfluss auf die Art und Weise in der Konsistenz wiederhergestellt wird. Sie legen automatisiert eine Art der Wiederherstellung der Konsistenz fest, obwohl es verschiedene Moglichkeiten dafur gabe. In dieser Arbeit stellen wir die anderungsgetriebene Response-Sprache fur die Konsistenzerhaltung von Modellen vor. Sie erlaubt es, die Art und Weise der Wiederherstellung von Konsistenz explizit in imperativen Programmen festzulegen, welche als Reaktion auf festgelegte Anderungen ausgefuhrt werden. Zusatzlich bietet sie Sprachkonstrukte an, die wiederkehrende Reaktionen kapseln und wiederverwendbar machen. Wir fuhren einen Konsistenzbegriff mit dem Fokus auf Verstandlichkeit ein und stellen eine Kategorisierung von moglichen Modellanderungen vor. Die Sprache ist entsprechend einer allgemeingultigen Struktur fur die anderungsgetriebene Wiederherstellung von Konsistenz aufgebaut, welche wir aus unserem Konsistenzbegriff herleiten. Wir stellen eine Evaluation unseres Ansatzes anhand einer Fallstudie zur Konsistenzerhaltung von Architekturbeschreibungen und deren Implementierung in objektorientiertem Code vor. Die Evaluation zeigt die Anwendbarkeit der vorgestellten Sprache fur die Sicherstellung von Modellkonsistenz in diesem konkreten Fall und verdeutlicht einige Vorteile gegenuber einer manuellen Implementierung der Mechanismen zur Konsistenzerhaltung.
On Modeling Murder Crimes in Nigeria

This paper examines the modelling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twenty-nine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first-order differencing to make them Stationary. The Stationarities were confirmed with time series plots. Statistical analysis was performed using GRETL software package from which, ARIMA (0, 1, 0) was found to be the best and adequate model for Murder crimes. Forecasted values suggest that Murder would slightly be on the increase.
The Nuts and Bolts of Automated Text Analysis. Comparing Different Document Pre-Processing Techniques in Four Countries

Automated text analytic techniques have taken on an increasingly important role in the study of parties and political speech. Researchers have studied manifestos, speeches in parliament, and debates at party national meetings. These methods have demonstrated substantial promise for measuring latent characteristics of texts. In application, however, scaling models require a large number of decisions on the part of the researcher that likely hold substantive implications for the analysis. Past researchers proposed discussion of these implications, but there is no clear prescription or systematic examination of these choices with the goal of establishing a set of best practices based on their implications for speeches at parties’ national meetings in a comparative setting. We examine the implications of these choices with data from intra-party meetings in Germany, Italy, the Netherlands, and prime minister speeches in Denmark. We conclude with considerations for those undertaking political text analyses.
Reverse Measurement as a Tool for Management Method Evaluation

Strategic planning is a procedure that is supposed to define organizational development through the identification of strategic goals. Models can help in understanding the business technology and in creation of formal specifications of the functionality, structure and behavior of real system appearances. By giving the entities in the model numerical values, possible improvements can be simulated, measured and evaluated according to the measurements, and then use for managing the organization. The issue on this approach is that the results that are gotten from the model measurements can be compared to the real values that the model results represent only after they are already accomplished. In this paper the use of reverse measurement is being introduced as a tool for evaluating management methods, in order to ensure the usability of a method prior to its application for managing the organization.
Too Cute to Be Healthy: How Cute Packaging Designs Affect Judgments of Product Tastiness and Healthiness

In the packaged food market, several brands use cute packaging designs, for example, by displaying playful colors or funny cartoon-like pictures. However, prior research has not systematically examined how cute packaging designs might affect product perception. In this research, we show that cute packaging designs increase perceptions of product tastiness and, at the same time, decrease perceptions of product healthiness. Importantly, as perceptions of both tastiness and healthiness jointly determine purchase intention, we further examine the role of the product type in order to demonstrate how marketers may benefit from using cute packaging designs. We find that cute packaging designs increase consumer purchase intention for relative vice products but decrease consumer purchase intention for relative virtue products. This article thus extends the current packaging design literature and demonstrates how marketers can strategically use cute packaging designs to communicate their desired product attributes. Furthermore, we outline implications for public policy makers.
XFOR (Multifor) : nouvelle structure de programmation pour faciliter la formulation des optimisations efficaces de boucles

We propose a new programming structure named XFOR (Multifor), dedicated to data-reuse aware programming. It allows to handle several for-loops simultaneously and map their respective iteration domains onto each other. Additionally, XFOR eases loop transformations application and composition. Experiments show that XFOR codes provides significant speed-ups when compared to the original code versions, but also to the Pluto optimized versions. We implemented the XFOR structure through the development of three software tools: (1) a source-to-source compiler named IBB for Iterate-But-Better! , which automatically translates any C/C++ code containing XFOR-loops into an equivalent code where XFOR-loops have been translated into for-loops. IBB takes also benefit of optimizations implemented in the polyhedral code generator CLooG which is invoked by IBB to generate for-loops from an OpenScop specification; (2) an XFOR programming environment named XFOR-WIZARD that assists the programmer in re-writing a program with classical for-loops into an equivalent but more efficient program using XFOR-loops; (3) a tool named XFORGEN, which automatically generates XFOR-loops from any OpenScop representation of transformed loop nests automatically generated by an automatic optimizer.
Speech Emotion Recognition Based on Feature Fusion

Speech emotion recognition is mainly based on the differences of characteristics between different emotions. The traditional recognition method is based on the manual extracted features, such as MFCC and LPCC, etc., and also achieved well. But it is unclear what kind of feature are able to reflect the characteristics of human emotion from speech. With Convolution Neural Network (CNN) shows strong ability in the field of image classification, attracting more researchers to apply CNN to the learning of the spectrogram feature. However, the study of speech emotion either according to the characteristics of the traditional manual extraction or completely dependent on spectrogram of speech. There is still no combination of traditional features and spectrogram feature. In this paper, we propose a fusion neural network model combining the characteristics of traditional with spectrogram features. This multimodal CNN is trained with two stages. First, two CNN models pre-trained are fine-tuning respectively on the corresponding labeled audio datasets. Second, the outputs of the two CNN models are connected to a fusion network of fully-connected layers. The fusion network is trained to obtain a joint feature representation for emotion recognition. From the recognition results of emotional speech database, the proposed algorithm has higher speech emotion recognition rate and robustness.
Improving approximation of domain-focused, corpus-based, lexical semantic relatedness

Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in many domain-specific scenarios. The problem of most state-of-the-art methods for calculating domain-specific semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the fields such as Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this dissertation, three new corpus-based methods for approximating domain-specific textual semantic relatedness are presented and evaluated with a set of standard benchmarks focused on the field of biomedicine. Nonetheless, the proposed measures are general enough to be adapted to other domain-focused scenarios. The evaluation involves comparisons with other relevant state-of-the-art measures for calculating semantic relatedness and the results suggest that the methods presented here perform comparably or better than other approaches. Additionally, the dissertation also presents an experiment, in which one of the proposed methods is applied within an ontology matching system, DisMatch. The performance of the system was evaluated externally on a biomedically themed ‘Phenotype’ track of the Ontology Alignment Evaluation Initiative 2016 campaign. The results of the track indicate, that the use distributional semantic relatedness for ontology matching is promising, as the system presented in this thesis did stand out in detecting correct mappings that were not detected by any other systems participating in the track. The work presented in the dissertation indicates an improvement achieved w.r. t. the stat-of-the-art through the domain adapted use of the distributional principle (i.e. the presented methods are corpus-based and do not require additional resources). The ontology matching experiment showcases practical implications of the presented theoretical body of work.
Electronic document distribution system and an electronic document distribution methods

The present invention relates generally individuals with corporate / organization, an electronic document distribution system and an electronic document distribution method capable also to build an electronic document distribution scheme reliability can be secured small business. Electronic document distribution system according to the present invention, the electronic address to send and receive messages to base, receiving individual and circulating an electronic document through the circulation messaging server to issue and manage the distribution certificate for message transmission and reception; the transceiver individual electronic address registration / managed by, set the flow path of the electronic document between the transmitting and receiving individual, it provides a standard format for electronic documents to the transceiver individual error occurs during distribution of the electronic document between the transmitting and receiving individual when the intercepts the message transfer, distribution hubs to issue a distribution certificate; and receives the transmission of distribution certificate store, trusted third party custodian; including.
How Not to Fight About Theory: The Debate Between Biometry and Mendelism in Nature, 1890–1915

Evolutionary theory in the late nineteenth century was marked by controversy over the way in which Darwin’s insights could be reconciled with theories of heredity, particularly following the rediscovery of the work of Mendel. Darwin’s proposal of common descent was quickly accepted, but natural selection’s adoption was delayed as a result of this debate. Two schools of thought rapidly developed: one (the “biometricians”) using statistics to describe patterns of inheritance, and the other (the “Batesonians,” later the “Mendelians”) using morphological study and searching for discontinuous variation. The traditional history of this debate has focused on its key players (most notably William Bateson, Karl Pearson, and W.F.R. Weldon). Sociologists of science have helpfully expanded this picture to include a wider array of biologists and emphasize the role of the “conversion” of lesser-known biologists from biometry to Mendelism. In this chapter, I will expand our picture further, by considering the structure of the debate as it developed in the articles and correspondence of Nature. The “network of discourse” in this literature, as revealed by digital tools, shows us surprising features of the way this argument unfolded. Far from being a central topic in the biology literature of the day, as historians often have assumed, those arguing over theories of heredity recede into the background, engaging with fewer other members of their fields. While this digital analysis is not the end of the story, it offers us a variety of suggestive questions for future historical, philosophical, and sociological work on this crucial period in the development of evolution.
Cycle Time and Idle Time Reduction in an Engine Assembly Line

This paper focuses on productivity improvement of a tiller assembly line by using operational analysis and assembly line balancing . The existing standard time in the assembly line is too old and inaccurate. A proposal of new standard time has been given to reduce the ineffective time. A heuristic method called Ranked Positional Weighted method is used for assembly line balancing. After the analysis of the existing time required for each tasks non value added time, value added time and production time has been found using stop watch time study. Micro motion study is done to find the ineffective time in each operation.