Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Parameter Repurposing Method for Parameter Transfer With Small Dataset and Its Application in Fault Diagnosis of Rolling Element Bearings

Transfer learning is a promising deep learning approach that can be used in applications that suffer from insufficient training data. Parameter transfer, which is a method of improving the accuracy and training speed by training the target network using the parameters of the source network, selects two conventional parameter repurposing methods, such as parameter freezing and fine-tuning depending on the amount of target data and network size. In this paper, we propose a novel method to increase the performance in the target domain in an intermediate approach of both methods. The proposed method, selective parameter freezing (SPF), freezes only a portion of parameters not freeze or fine-tunes all parameters within a layer by choosing output-sensitive parameters from the source network. Freezing only sensitive parameters while training is to reduce the amount of trainable parameter and protect informative parameters from overfitting to a small number of target data. Using two sets of the source–target domain, artificial faults with different fault size and artificial faults—natural faults of rolling element bearing, the proposed SPF allows adaptation to the target domain by choosing the best degree of freezing with various amounts of target data and size of networks compared to conventional approaches.
Iterative Disambiguation Algorithm for Removing Ambiguity Problem in CLIR

This paper describes an English-Marathi CrossLingual Information Retrieval System. The system retrieves Marathi documents in response to a query given in English. Marathi is one of the most prominent regional languages of Indian subcontinent. We take a document translation based approach using bi-lingual dictionaries. Query words not found in the dictionary are transliterated using a simple rule based approach which utilizes the corpus to return the ‘k’ closest Marathi transliterations of the given English word. The resulting multiple translation/transliteration choices for each query word are disambiguated using an iterative disambiguation algorithm which, based on term-term co-occurrence statistics, produces the final translated query.
Reducing the Power Consumption of the Electrodynamic Suspension Levitation System by Changing the Span of the Horizontal Magnet in the Halbach Array

In high-speed magnetic railways, it is necessary to create the forces that lift the train. This effect is achieved by using active (EMS) or passive (EDS) magnetic systems. In a passive system, suspension systems with permanent magnets arranged in a Halbach array can be used. In this paper, an original Halbach array with various alternately arranged horizontally and vertically magnetized magnets is proposed. Correctly selected geometry allows us to obtain higher values of levitation forces and lower braking forces in relation to a system with identical horizontally and vertically magnetized elements. The effect of such a shape of the magnetic arrangement is the reduction of instantaneous power consumption while traveling due to the occurrence of lower braking forces. In order to perform a comparative analysis of the various geometries of the Halbach array, a simulation model was developed in the ANSYS Maxwell program. The performed calculations made it possible to determine the optimal dimensions of horizontally and vertically magnetized elements. The results of calculations of instantaneous power savings for various cruising speeds are also included.
Prediction of Criminal Suspects Based on Association Rules and Tag Clustering

To date, not many studies have been conducted on criminal prediction. In this study, the criminal data related to city S is divided into a training data set and a validation data set at a 1:1 ratio in light of the personal tag data and the travel and accommodation data of criminals and ordinary people in city S. Firstly, the FP-growth algorithm is adopted to calculate association rules between the criminals and the ordinary people in their travel and hotel accommodation data, in order to discover criminal suspects based on association rules. Secondly, the DBSCAN algorithm is employed for clustering of the tag data of the criminals and the ordinary people, followed by similarity calculation, in order to discover criminal suspects based on tag clustering. Lastly, intersection operation is performed on the above two sets of criminal suspects, and the resulting intersection is verified against the criminal validation set for elimination of criminals who appear in the intersection so as to obtain final criminal suspects. Results show that a set of 648 criminal suspects is retrieved based on the association rules calculated by the FP-growth algorithm, while a set of 973 criminal suspects is retrieved based on DBSCAN clustering and cosine similarity of the personal tags; the number of criminal suspects is narrowed down to 567 after the intersection operation of the two sets, and 419 of the 567 criminal suspects are further verified to be criminals using the validation set, thereby leaving the other 148 to be the final criminal suspects and giving a prediction accuracy of 73.9%. The data mining method of criminal suspects based on association rules and tag clustering in this study has been successfully applied to the police system of city S, and the experiment proves the effectiveness of this method in detecting criminal suspects.
Enabling data security in data using vertical split with parallel feature selection using meta heuristic algorithms

Big data is the emerging trend in modern science that deals with datasets larger and more complex that cannot be dealt by the traditional data processing techniques. This seems to be the core of current technology and business. In practice, many criteria should be considered in the implementation of this technique. The way of the search space for finding potential subsets of features and prediction performance of classifiers are major important issue. To solve this issue, feature selection methods are introduced in the recent work. In the feature selection algorithm, Non‐deterministic Polynomial (NP) Hard, and searching the space has been becomes more difficult task. To solve this problem, this work provides a new approach toward feature selection based on Vertical Split Group FireFly (VSGFF) algorithm. FF algorithm gets its inspiration from social aspects of real fireflies. At the same time, VSGFF is proposed with the principle of multiple clusters to avoid privacy problem. Finally, Naïve Bayes (NB), K Nearest Neighbor (KNN), and Multi‐Layer Perceptron Neural Network (MLPNN) classification algorithms are proposed for big data classification. Experimental outcomes depicts that proposed technique improves classification accuracy by 4% compared to traditional vertical split firefly algorithm.
A Systems Science Approach to Organizational Integrity. Case: Services Small and Medium Enterprises

Abstract This study developed a model oriented to organizational integrity in services small and medium-sized enterprises (SMEs). A systemic approach was adopted to articulate methodologies as follows: A) We used the soft systems methodology to frame the problem and formulate a conceptual model. B) Partial least squares path modeling was applied to validate the construct statistically. C) The viable system model was used to realign interactions within SMEs to foster organizational integrity. The results suggest that the relationships proposed in the construct may promote organizational integrity. The ideas developed are constrained to the organizational domain, and although the results apply to the Mexican context, this study provides an opportunity to discuss methods for improving the analysis of and viewpoints for rethinking viability in these organizations; while suggesting an analytic protocol to support academics and managers for addressing the study of integrity.
Delineation of Land Management Units in Tropical South Deccan Plateau of India

Soils are highly variable spatially due to soil forming and processes. Spatial variation of soil properties cause uneven patterns in soil fertility and crop growth, and use efficiency of externally applied resources. Application of variable rates of inputs needs partitioning of land units into homogenous land and management units (LMU), for production benefits and to minimize the adverse environmental impacts. The soil and site characteristics namely texture, depth and gravelliness, land use and climate are integrated to identify land units. Crop based production systems varied in different land units, production systems were super imposed on the delineated land units in GIS to identify the LMUs. Methodology was tested in Mysore district of Karnataka, where in 12 LMUs identified and delineated at district level. To verify the methodology, performance of crops vis-à-vis management practices followed in different LMUs was compared to plan appropriate land management decisions for current and future land use to ensure its sustainability.
Secure and privacy-preserving fog-assisted vehicular crowdsensing

Vehicular crowdsensing (VCS) is an emerging paradigm where vehicles use onboard sensors to collect and share data with the aim of measuring phenomena of common interest. Great attention has been recently directed towards road surface condition monitoring systems (RSCMS). Such an endeavor is of critical importance in transportation infrastructure management. As a response, multiple recommendations have been proposed. These recommendations make use of mobile sensing, more specifically contemporary applications and architectures that are used in both crowdsensing and vehicle-based sensing. This has allowed for automated control as well as analysis of road surface quality. These innovations have thus encouraged and shown the importance of the cloud in providing reliable transport services to clients. Nonetheless, these initiatives have not been without challenges, ranging from mobility support, location awareness, low latency and geo-distribution. In order to address these challenges, a new term, known as fog computing, has been coined as a novel paradigm. Therefore, this present work exploits the advantages of VCS and fog computing paradigms in order to propose a promising framework, which is referred to as fog-assisted vehicular crowdsensing (FVCS). Although FVCS has addressed the aforementioned challenges, it may encounter various security threats and privacy concerns that could jeopardize public safety and become the main barrier to the acceptance of such a new technology. This thesis presents the proposal of a secure and privacy-preserving framework for FVCS. The objective of the proposed framework is to allow vehicles to share their resources while preserving their privacy by preventing private information from being disclosed. A thorough search of the relevant literature suggests that the proposed framework is the first work that attempts to address critical security and privacy challenges in FVCS. Attention is first focused on investigating the threat towards the data generated by vehicles, which is then forwarded to cloud servers and organizations by roadside units (RSUs). The generated data can be exploited by an adversary to reveal vehicle privacy. Protecting the privacy of participants is essential to the success of FVCS applications. Therefore, this work presents a privacy-preserving protocol for enhancing security in a VCS-based road surface condition monitoring system using fog computing. This protocol presents a highly efficient certificateless aggregate signcryption scheme (CLASC). On the basis of the proposed CLASC scheme, a data transmission protocol for monitoring road surface conditions is designed with security aspects such as information confidentiality, mutual authenticity,
Transcriptome analysis reveals normalization effect of nicotinamide and butyrate sodium on breast muscles of broilers under high stocking density

Background In recent years, increased attention has been focused on breast muscle yield and meat quality in poultry production. Supplementation with nicotinamide and butyrate sodium can improve the meat quality of broilers. However, the potential molecular mechanism is not clear yet. This study was designed to investigate the effects of supplementation with a combination of nicotinamide and butyrate sodium on breast muscle transcriptome of broilers under high stocking density. Methods A total of 300 21-d-old Cobb broilers were randomly allocated into 3 groups based on stocking density: low stocking density control group (L; 14 birds/m 2 ), high stocking density control group (H; 18 birds/m 2 ), and high stocking density group provided with a combination of 50 mg/kg nicotinamide and 500 mg/kg butyrate sodium (COMB; 18 birds/m 2 ), raised to 42 days of age.Results The H group significantly increased cooking losses, pH decline and activity of lactate dehydrogenase in breast muscle while COMB showed a significant decrease in these indices ( P < 0.05). The transcriptome results showed that key genes involved in glycolysis, proteolysis and immune stress were up-regulated whereas those relating to muscle development, cell adhesion, cell matrix and collagen were down-regulated in the H group. In contrast, genes related to muscle development, hyaluronic acid, mitochondrial function, and redox pathways were up-regulated while those associated with inflammatory response, acid metabolism, lipid metabolism, and glycolysis pathway were down-regulated in the COMB group.Conclusions The combination of nicotinamide and butyrate sodium may improve muscle quality by enhancing mitochondrial function and antioxidant capacity, inhibiting inflammatory response and glycolysis, and promoting muscle development and hyaluronic acid synthesis.
Review of Nonlinear Conductivity Theory Research of Modified Composite Materials

Nonlinear conductivity dielectric is widely used today to solve high energy discharge problems in many fields (such as spacecraft charging and motor insulation). This paper summarized the research about the nonlinear conductivity theory of composite materials. In particular, the key issues about nonlinear conductivity studies of composite materials are proposed. The nonlinear conductivity mechanisms of two typical filler particles (SiC and ZnO) are introduced. In addition, two kinds of nonlinear conductivity theory, including the traditional dielectric conduction theory and the polymer conduction theory, are reviewed. Several mechanisms in each of the conduction theories are analyzed, and also, the advantages and disadvantages of these theories are presented in detail. Thus, the purpose of this paper is to let the researchers know the nonlinear conductivity research status fully. We believe this paper is of great significance for the nonlinear dielectric conductivity researchers.
Dynamic cybersecurity modelling and analysis.

It is difficult to assess the security of modern networks, such as Cloud and software defined networks, because they are usually dynamic with configuration changes (e.g., changes in topology, firewall rules, etc). Graphical security models, such as Attack Graphs and Attack Trees, are widely used to systematically analyse the security posture of network systems using various security metrics. However, there are challenges in using them (i.e., the graphical security models and security metrics) to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. As a result, there is a lack of techniques to efficiently capture and manage the security changes that are happening in dynamic networks. Secondly, the existing security metrics which are used with the models are not designed for the analysis of dynamic networks, and hence their effectiveness to the dynamic changes in the network remains unclear. Moreover, they may not quantitatively represent the changes in the security posture of the dynamic networks. Thirdly, finding the optimal security solution for the dynamic networks is a difficult task due to their complexity and uncertainty of changes made. That is, an optimal solution for the current network configuration may not be optimal when the dynamic network changes in the future. As a result, it is difficult to select the best set of security solutions to deploy for modern networks that are dynamic. This thesis aims to address the aforementioned issues in three primary goals: (1) to develop an adaptable graphical security model to
Probabilistic principal component analysis-based dimensionality reduction and optimization for arrhythmia classification using ECG signals

Abstract Electrocardiogram (ECG) is an electrical signal that contains data about the state and functions of the heart and can be used to diagnose various types of arrhythmias effectively. The modeling and simulation of ECG under different conditions are significant to understand the function of the cardiovascular system and in the diagnosis of heart diseases. Arrhythmia is a severe peril to the patient recovering from acute myocardial infarction. The reliable detection of arrhythmia is a challenge for a cardiovascular diagnostic system. As a result, a considerable amount of research has focused on the development of algorithms for the accurate diagnosis of arrhythmias. In this paper, a system for the classification of arrhythmia is developed by employing the probabilistic principal component analysis (PPCA) model. Initially, the cluster head is selected for the effective transmission of ECG signals of patients using the adaptive fractional artificial bee colony algorithm, and multipath routing for transmission is selected using the fractional bee BAT algorithm. Features such as wavelet features, Gabor transform, empirical mode decomposition, and linear predictive coding features are extracted from the ECG signal with high dimension (which are reduced using PPCA) and finally given to the proposed classifier called adaptive genetic-bat (AGB) support vector neural network (which is trained using the AGB algorithm) for arrhythmia detection. The experimentation of the proposed system is done based on evaluation metrics, such as the number of alive nodes, normalized network energy, goodput, and accuracy. The proposed method obtained a classification accuracy of 0.9865 and a goodput of 0.0590 and provides a better classification of arrhythmia. The experimental results show that the proposed system is useful for the classification of arrhythmias, with a reasonably high accuracy of 0.9865 and a goodput of 0.0590. The validation of the proposed system offers acceptable results for clinical implementation.
Role of propofol with total intravenous anesthesia in intra-abdominal surgeries: A prospective study

Background: Postoperative pain can be severe post multiple abdominal surgeries due to the upper abdominal incision and pain management can be difficult for this group of patients, therefore total intravenous anaesthesia is a technique involving infusion and maintenance of anaesthetic state with intravenous drugs alone. But no agent has all the characteristics of ideal TIVA agent. Propofol has three important characteristics of ideal TIVA agent, i.e. rapid induction, rapid metabolism and rapid recovery. In this communication we studied the characteristics of propofol in total intravenous anaesthesia in various intra-abdominal surgeries. Methods: Here our sample size was 52 patients aged 18 to 60 years belonging to ASA gr 1 and gr 11 undergoing various elective intra- abdominal surgery took part in this prospective study. All patients were induced with 2mg/kg body weight 1% propofol after premedication 0.03 mg midazolam and pentazocine. Anesthesia maintained with 0.1% propofol at rate of 100Ug/kg/min with 100% oxygen and incremental dose muscle relaxant. Based on haemodynamic sings and sympathetic over activity dose of propofol was adjusted in intraoperative period. At skin closure infusion was discontinued and neuromuscular blockade reversed. Result: In our results, TIVA with propofol enabled smooth and rapid induction in all patients with induction time of 29.7 seconds. In maintenance phase propofol provides adequate depth of anaesthesia assessed by haemodynamic changes and sympathetic over activity in 47 (92%) of the patients. Five patients (8%) needed higher dose of infusion to maintain adequate depth of anaesthesia. Recovery was good with short response time of 10.3 minutes and orientation time of 14.54 minutes. Conclusion: Our study concludes like others, Propofol based TIVA provides smooth and rapid induction and acceptable haemodynamic status during maintenance period in most of the patients. All patients were alert and clear headed with smooth recovery from anaesthesia effect.
Evaluation of the Difficulties in the Internet of Things (IoT) with Multi-Criteria Decision-Making

The rapid development of technology has increased the desire of all to be on the Internet. The discovery that objects born of the Internet communicate with each other without external factors revealed, with the fourth industrial revolution, the concept of the Internet of Things (IoT). The communication of objects with each other means minimum labor and minimum cost for enterprises. Enterprises that want to transition to the Internet of Things face many difficulties. Identifying and correcting these difficulties can lead to both lost time and high cost. In this study, we investigated the difficulties encountered in the Internet of Things. As a result of the study, the degree of importance of the factors causing these difficulties was determined by multi-criteria decision-making methods and was presented to the enterprises. The main criteria, and the sub-criteria related to these main criteria, were determined. The main purpose of the enterprises transitioning to Industry 4.0 is the communication of things with each other. In this study, we aimed to determine which criteria caused difficulties in the transition to Industry 4.0. Then, the degree of importance of the criteria was determined using the analytic hierarchy process (AHP) and analytic network process (ANP) methods, in the multi-criteria decision-making. Through the study, we determined which criteria should be taken into consideration by the enterprises that want to transition to the Internet of Things. In this way, enterprises will be able to accelerate that transition by minimizing time and monetary loss.
The Oxford Latin Syntax

Volume II of the Oxford Latin Syntax deals with the syntax and pragmatics of complex sentences in Latin and other topics that transcend the simple clause (which is the content of Volume I). The volume starts with a chapter on subordination in general, followed by chapters on subordinate clauses that function as argument or as satellite in their sentence. Separate chapters are devoted to subordinate clauses governed by nouns and adjectives and to relative clauses. In addition there are chapters on coordination, comparison, secondary predicates, information structure of clauses and sentences including the use of emphatic particles, word order, and various discourse phenomena such as sentence connection. As in Volume I, the description of the Latin material is based upon texts from roughly 200 BC to AD 450. The Latin texts that are discussed are provided with an English translation. Supplements contain further examples to illustrate the main text. The grammatical framework used is mainly that of Functional Grammar but the description is accessible for readers without a modern linguistic background.