Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering Intrusion Detection Model Based on Grey Fuzzy K-mean Clustering

K-means clustering algorithm is applied to the field of intrusion detection,it has the following problems.First,cluster analysis does not consider the correlation degree of the data flow,so clustering accuracy is not high;the second is prone to packet loss omissions because of data overload.In order to improve the clustering accuracy,this paper introduces grey analysis algorithm to improve the clustering accuracy.Meanwhile,in order to avoid the packet loss and underreporting phenomenon,we introduce clustering technology into the intrusion detection system to process the load balancing of the data stream,thereby overcome the contradiction between the high-speed network data flow and low-speed intrusion detection system processing capabilities.After a comprehensive analysis,this paper proposed an intrusion detection model based on the gray K-means clustering algorithm.
Alternating hands when interacting with a desktop touchscreen can reduce physical efforts of the shoulder muscles

Finger-touch interface has not been widely adopted to desktop touchscreen displays as it requires entire arm movements and causes resultant arm and shoulder discomforts. As one of possible ways to lessen the physical discomfort of the touchscreen users, this study investigated potential benefits of an ‘alternating hands’ strategy in interacting with a desktop touchscreen display. Twenty three participants conducted cyclic reach-tap-return operations on a 23” desktop touchscreen in three different touch strategies (‘left- hand only’, ‘right-hand only’ and ‘alternating hands’) and with the display in three positions (near flat, upright-distant and upright-close). Mean and integrated myoelectric activity (EMG) of the left and right shoulder muscles were compared between the nine conditions. Two-way ANOVA found that the‘alternating hands’ (participants freely switched hands depending on target location) strategy resulted in significantly less amount of total mean and integrated EMG of the shoulder muscles compared to the other two strategies (p<0.01). Among the three display positions, participants used shoulder muscles significantly less when the display was in the ‘near flat’ position than when the display was in the other two positions. Study results suggest that the ‘alternating hands’ strategy should be recommended for desktop touchscreen users to reduce physical efforts of shoulder muscles.
Studies on pyrazolo[3,4-d]pyrimidine derivatives. XVIII, Facile preparation of 1H-pyrazolo[3,4-d]pyrimidin-4[5H]-ones

Reaction of 5-amino-1-phenyl-1H-pyrrazole-4-carboxamide with various ethyl alcanoates in the presence of sodium ethoxide in ethanol gave 1-phenyl-1H-pyrazolo[3,4-d]pyrimidin-4(5H)-one and its 6-substituted derivatives . Similar treatment of 5-amino-1-methyl-1H-pyrazole-4-carboxamide gave the 1-methyl-1H-pyrazolo[3,4-d]-pyrimidin-4(5H)-ones
Optimal relay node placement for throughput enhancement in wireless sensor networks

In this work we consider the relay node placement problem in wireless sensor networks: where to position a limited number of available nodes that can act as additional relays to foward sensor data toward one or more base stations. The objective is to define relay node locations in order to improve network performance in terms of delivery ratio and end-to-end delay, and/or to provide connectivity in partially disconnected areas. Typical application scenarios include the repair of the network in face of failures, or the case of networks used in dynamic environments, such as network characteristics need to be dynamically adapted to the changing conditions. We formalize the problem by defining a linear, mixed integer mathematical programming model. We include a number of constraints and penalty components, aimed at closely modeling the specific characteristics of the wireless environment. Model solutions specify both where to place the relays and the optimal data paths to route the data. Through a comprehensive evaluation in simulation we show that our approach is effective in accomplishing the desired objectives. We compare the solutions provided by our scheme against a state-of-the-art dynamic routing protocol, to assess the quality of the routes, and against a relay node placement heuristic, to evaluate the positioning of the relay nodes.
Development of an Intelligent Car Engine Fault Troubleshooting System (CEFTS)

The mass production and wider use of automobiles and the incorporation of complex electronic technologies all indicate that the control of faults should be an integral part of engine design and usage. This paper discusses an expert system application for troubleshooting car engine faults using Auto-mechanic workshops in Calabar metropolis of Cross River State-Nigeria. The method of fact-finding called knowledge acquisition which is an expert system approach to extract facts was adopted in order to achieve good judgment in the use of heuristics among experts. The results are represented as a set of IF – THEN judgments that expert mechanics can rely mostly on in the troubleshooting process. The system depends on an automated matching process between symptoms and procedures. The paper developed a new prototype named Car Engine Fault Troubleshooting System (CEFTS) using C++ programming platform. The purpose of the developed prototype is to assist motorists and auto mechanics in fault troubleshooting of car engines by providing systematic and step-by-step analysis of failure symptoms and offering maintenance or service advice. The result of this development is expected to introduce a systematic and intelligent method in car engine troubleshooting and maintenance environments and also provides a troubleshooting framework for other researchers to work on. Keywords : Expert System, knowledge base, troubleshooting, inference engine, knowledge acquisition, artificial intelligence.
Scheduling and locking in multiprocessor real-time operating systems

With the widespread adoption of multicore architectures, multiprocessors are now a standard deployment platform for (soft) real-time applications. This dissertation addresses two questions fundamental to the design of multicore-ready real-time operating systems: (1) Which scheduling policies offer the greatest flexibility in satisfying temporal constraints; and (2) which locking algorithms should be used to avoid unpredictable delays? With regard to Question 1, LITMUSRT, a real-time extension of the Linux kernel, is presented and its design is discussed in detail. Notably, LITMUSRT implements link-based scheduling, a novel approach to controlling blocking due to non-preemptive sections. Each implemented scheduler (22 configurations in total) is evaluated under consideration of overheads on a 24-core Intel Xeon platform. The experiments show that partitioned earliest-deadline first (EDF) scheduling is generally preferable in a hard real-time setting, whereas global and clustered EDF scheduling are effective in a soft real-time setting. With regard to Question 2, real-time locking protocols are required to ensure that the maximum delay due to priority inversion can be bounded a priori. Several spinlock- and semaphore-based multiprocessor real-time locking protocols for mutual exclusion (mutex), reader-writer (RW) exclusion, and k-exclusion are proposed and analyzed. A new category of RW locks suited to worst-case analysis, termed phase-fair locks, is proposed and three efficient phase-fair spinlock implementations are provided (one with few atomic operations, one with low space requirements, and one with constant RMR complexity). Maximum priority-inversion blocking is proposed as a natural complexity measure for semaphore protocols. It is shown that there are two classes of schedulability analysis, namely suspension-oblivious and suspension-aware analysis, that yield two different lower bounds on blocking. Five asymptotically optimal locking protocols are designed and analyzed: a family of mutex, RW, and k-exclusion protocols for global, partitioned, and clustered scheduling that are asymptotically optimal in the suspension-oblivious case, and a mutex protocol for partitioned scheduling that is asymptotically optimal in the suspension-aware case. A LITMUSRT-based empirical evaluation is presented that shows these protocols to be practical.
Real-Time Face Recognition Method Based on the Threshold Determination of the Positive Face Sequence

Most of the current face recognition methods are not able to ensure the accuracy and real-time performance at the same time. In this paper, a real-time face recognition method based on the threshold determination of the positive face sequence is proposed. The procedure of the proposed method is as following. Firstly, the faces with different angles are detected and tracked. At the same time, the angle and position information of the faces is recorded. Secondly, the faces in different frames are matched. Then the positive faces which are matched as the same person are recognized. Finally, the final results are obtained by using the threshold determination method. The experimental results indicate that this algorithm has good recognition rates in the case of the moderate flow density and is able to satisfy the requirement of the real-time system.
An Algorithm of Wavelets for the Pretreatment of EMG Biomedical Signals

This paper presents a new analytical method for the denoising and the detection of the signal biomedical parameters for Electromyogram signals EMG type. In practice, the EMG signals are noising, so it is very difficult for the doctor or the specialist to analyze these signals. Thus, in this context, the present work aims to give an algorithm based on the mathematical model of the EMG signals and the wavelets concept, for the denoising and the estimation of the waves present in the EMG studied signals. The choice of the adapted wavelets for the pretreatment of the EMG signals is justified by mathematical proofs. we have realized, in Matlab, the simulations on academics signals and the numerical results are satisfactory. Our calculation method- ology and an algorithm which facilitate the research of the signal peaks is presented.
A Remote Real-time Data Collection System Based on Embedded uCLinux

In this paper,we propose a new design project of data acquisition system based on embedded operating-system and can be supervised remotely.As the control kernel of data acquisition and data storage,an embedded processor S3C44B0X and a multitasking operation system uCLinux are adopted.This system not only has many functions,such as high data acquisition speed,strong performance,bulk data storage,friendly interface,voice alarm and so on,but also can be controlled and monitored through Internet.
Provenance Management for Frequent Itemsets

Provenance has been studied extensively for relational queries and shown to be important in revealing the origin and creation process of data that has been produced by potentially complex relational transformations. Provenance for the results of data mining operators in contrast has not been considered. We argue that provenance offers the same benefits for mining as for relational queries, e.g., it allows us to track errors caused by incorrect input data. We consider the most common mining operator, frequent itemset mining, and introduce two types of provenance (whyand i-provenance) for this operator. We argue that the concept of why-provenance for relational queries can be adapted for frequent itemsets, but that it poses new computational challenges due to the nature of itemset mining and the size of why-provenance. We address these challenges in two ways. First, we propose combining why-provenance computation with SQL querying to permit users to select small and more intuitive representations of the provenance, and second by proposing new compression techniques for the why-provenance. Next, we introduce a new provenance type called i-provenance (itemset provenance) that succinctly represents the interdependencies between items and transactions that explain how a frequent itemset was derived (intuitively giving insight into the structure of the data that provides the evidence for the itemset). We present techniques for efficient storage and use of both types of provenance information and experimentally evaluate the scalability of our approach. We argue through a set of examples that whyand i-provenance can add significant value to mining results and can be used to analyze the context of the transactions that caused an itemset to be frequent and to understand how combinations of itemsets contribute to a result.
Impact of Computers on Structural Biology ofProteins

As more genome sequencing projects are underway on a large scale, more protein sequences are obtained and analysed. The modular units termed protein domains, associate in different ways giving rise to proteins of varied function. In the process of evolution, these combinations have produced many proteins that have new or modified functions. Thus, multidomain proteins are studied to understand the specific or more complex functions. Mutation of the domains potentially reveal disruptions of protein function which is necessary for cancer development.
Research on Service Component Retrieval with Hadoop

It remains both important and difficult for service-oriented computing to retrieve accurately and efficiently the service components fulfilling the client’s need. This paper intends to present a method for use in retrieving the personalized service components based on Hadoop architecture. It extends the semantic ontology of service components, and proposes the methods for building and inquiring the index files based on Lucene. By utilizing the HDFS and adopting the QoS computation and personalized recommendation techniques, the working principles of Map and Reduce of Hadoop are expanded. Experiment results show that the proposed method is applicable in realizing the retrieval of service components in distributed computing environment.
Automated problem generation in Learning Management Systems: a tutorial

The benefits of solving problems have been widely acknowledged by literature. Its implementation in e–learning platforms can make easier its management and the learning process itself. However, its implementation can also become a very time– consuming task, particularly when the number of problems to generate is high. In this tutorial we describe a methodology that we have developed aiming to alleviate the workload of producing a great deal of problems in Moodle for an undergraduate business course. This methodology follows a six-step process: exercise design, data generation, exercise computations, design of interpretation rules, wording generation and generation of cloze questions. It allows evaluating student’s skills in problem solving, minimizes plagiarism behaviors and provides immediate feedback to students (thus improving their results). Additionally, it also reduces the workload of teachers in large groups and helps evaluating the student's learning more objectively. We have designed this methodology based upon our experience in Economics curricula, where we have applied it at undergraduate and graduate courses. However, we consider that it can be applied with minor modifications in a very wide range of college courses and e–learning platforms. We expect that that this tutorial encourage other educators and educational developers to apply our six steps process, thus benefiting themselves and their students of its advantages.
Assessment of phosphorus availability in soil by introducing P-solubilizing novel bacterial and fungal strains of Lower Himalaya

ABSTRACT Phosphorus (P) is an essential element for soil quality and plant growth. But little is known about P fractions consequent to microbial inoculation in soils amended with inorganic phosphorus. In this regard, the overall soil P fractions after microbial treatments varied as hydrochloric acid – P (HCl-P) > Sodium hydroxide – P (NaOH-P) > Sodium bicarbonate – P (NaHCO3-P) > Water – P (H2O-P) determined through sequential fractionation. Inorganic P amendment of soil increased residual P fractions indicating non-availability of chemical P fertilizers added in soil. Bacterial–fungal co-inoculation, significantly increased NaHCO3-P and H2O-P fractions, as compared to mono inoculations and un-inoculated controls. Inoculated microbial populations increased in numbers contributing to the modification of the soil pH. In soil without TriCalcium Phosphate (TCP), microbial treatments showed a reduced pH compared to the control, with a maximum pH decrease in mono bacterial inoculations. In conclusion, tested bacterial and fungal strains exhibited efficient P solubilization in soil, and thus, have potential to be used as biofertilizers, subject to other necessary trials.
Trapping of HCl and oxidized, organic trace-gases in growing ice at temperatures relevant for cirrus clouds

Abstract. The uptake of hydrochloric acid (HCl), ethanol (C2H5OH), 1-butanol (1-C4H9OH), formic acid HC(O)OH and tri-fluoro acetic (CF3C(O)OH) acid to growing ice surfaces was investigated at temperatures between 194 and 228 K. HCl displayed extensive, continuous uptake during ice growth, which was strongly dependent on the ice growth velocity, the temperature of the ice surface and the gas phase concentration of HCl. Tri-fluoro acetic acid was also observed to be trapped in growing ice, albeit approximately an order of magnitude less efficiently than HCl, whereas the adsorption and desorption kinetics of ethanol, 1-butanol, formic acid on ice was not measurably different to that for non-growing ice, even at very large ice-growth rates. We present a parameterisation of the uptake coefficient for HCl on growing ice films (gamma_trap) and compare the results to an existing framework that describes the non-equilibrium trapping of trace gases on ice. The trapping of HCl in growing ice crystals in the atmosphere is assessed and compared to the gas- and ice-phase partitioning resulting from equilibrium surface adsorption and solubility.
Implementing and Engineering an NDGPS Network in the United States

La Coast Guard des Etats-Unis (USCG) fait partie de l'equipe qui met en oeuvre le service le plus vaste au monde d'augmentation de GPS par stations au sol, le NDGPS (Nationwide DGPS). A l'origine, la USCG avait la responsabilite de fournir une couverture DGPS de tous les ports des Etats-Unis et de leurs approaches. Le reseau de stations maritimes charge de remplir cette tâche fournissait des corrections differentieeles pour l'Atlantique, le Pacifique, et les cotes du Golfe du Mexique, les Grands Lacs, les iles Havaii, Porto-Rico et le sud de l'Alaska. Deux stations de controle ont egalement ete construites pour verifier les performances et l'integrite des diffusions de chaque emetteur. Avec l'augmentation du nombre d'usagers du DGPS, il a fallu etendre sa couverture. Par accord avec le Corps des Ingenieurs de l'Armee des Etats-Unis, la couverture DGPS de la Coast Guard a ete etendue aux voies de navigation interieures. L'effort d'extension du NDGPS, prevoit de depassser 126 sites d'emission et devra fournir une couverture DGPS sur l'ensemble des Etats-Unis et de l'Alaska, et repondre a tous les besoins du transport par voies de surface.
Opportunistic maintenance based on CUSUM control charts

The use of a Ship Maintenance Management System is fundamental for the good performance of equipments and the entire platform. Over the systematic maintenance, the opportunistic maintenance is a concept that aims to minimize outages and costs preventing undesirable failures. To implement this kind of maintenance statistical methodologies must be used. The Cumulative Sum charts have a very good performance applied to processes control in quality control. We proposed the use of Modified Cumulative Sum control charts to equipment maintenance. The data under study are observations of cooling water and oil temperatures from a diesel generator. In the first phase, we will apply traditional control charts, and, in the second phase, the Cumulative charts with a certain Average Run Length will be used. Then we will compare the results and extract conclusions, presenting measures for improvement. be applied. The techniques to be applied are the same as those used for independent data in traditional charts, but applied to the residues from the adjustment. Based on the ARIMA model, adjusted by AR(p), MA(q) or ARMA(p,q)), the μ and dispersion σ parameters estimation must be calculated. If there is some outliers those observations must be replaced by the expected values for the corresponding instant; the new residues should be calculated and the same for the reviewed charts. (Pereira & Requeijo, 2012) If the model is satisfactory, its residues are estimated with t t t X̂ X e   ( t X̂ is the expected value for the period t). Control charts are built from these residues, obtaining the mean and standard deviation For the model   p AR , the parameters are estimated from the expressions:
DMCloud: a Cloud-enabling DataMining Framework An architectural perspective

Cloud is a framework that extends the current Weka Grid-enabler Toolkits for supporting parallel data mining algorithms and distributed data mining on a Cloud environment. Service Oriented Architecture (SOA) approach is used to implement all the data mining applications independent services, each capable of carrying out a set of predefined tasks. DMCloud architecture relies on the Cloud IaaS OpenNebula Infrastructure extended with a specific workflows-based SLA broker, and other open technology and standards. It provides tools and services facilitating the user development, deployment and transparent utilization of data mining applications in a cloud environment.
The Research of Improved Apriori Mining Algorithm in Bank Customer Segmentation

The This paper studies bank customers segmentation problem. Improved Apriori mining algorithm is a kind of data mining technology which is an important method in bank customers segmentation. In practical application, the traditional algorithm has shortcomings of the initial values sensitive and easy to fall into local optimal value, which will lead to low accuracy rate of silver class customer classification. According to the shortcomings of traditional algorithm, this paper puts forward a bank customer segmentation method based on improved Apriori mining algorithm in order to improve the bank customer segmentation accuracy. Experimental results show that the algorithm can effectively overcome the traditional algorithms shortcomings of easy to fall into local optimal value, improve the customer classification accuracy, make mining results more reasonable, lay down different customer service strategies for different client base, improve effective reference opinions of bank decision makers, and bring more benefits for the bank.
Fovea and optic disk detection and key performance indicators process automation

This work, integrated in Critical Health, presents a new process to detect fovea and optic disk in retinal images and the application of some technologies to do the automation of Key Performance Indicators process (KPI). The proposed method consists in five steps: selection of an area in the image where the optic disk is located using Sobel operator, extraction of optic disk boundaries applying the Hough transform to detect center and diameter of optic disk, detection of the ROI (region of interest) where the fovea is located based on the optic disk center and its diameter and detection of the fovea within the ROI. The developed algorithm has been tested in a proprietary dataset with 1464 images (with ground truth generated by experts) and with some public datasets. Retmarker is an image processing product developed by Critical Health. The KPI is a process implemented in Critical Health to test Retmarker with the goal to reach the optimal performance. This process is currently highly manual and performed on a weekly basis, demanding a considerable amount of man-hours per year. A new plan was implemented to make this process fully automated.