Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Amalgam Encryption Approach for Grid Security: A Review

Grid computing is about several processors distributed globally and sharing the computational resources to solve various problems. Grid computing has become an increasingly important research topic within computer science as in academic educational purpose and industrial research to government sector. Grid computing is concerned how to share and coordinated use diverse resources in distributed environments. The dynamic and multi- institutional nature of these environments introduces challenging security issues, which include integration with existing systems and technologies, interoperability with different "hosting environments" and "trust relationships" among interacting hosting environments. The major issues associated with grid computing are coordinating resource sharing and security measures. We need new technical approaches to handle those security issues. Security solution consist of ARC4 (Rivest Cipher 4) algorithm combined with Advance encryption standard (AES) which provides solution for security with whitener (Whitening is used to enhance the security of the cipher). In related study hybrid solution has been proposed but has some overhead while processing security for large distributed networks. In current technology with development of smart grid architecture, we need less overhead to use best resources in grid computing. So in this research we will propose a enhance amalgam encryption solution using AES and RC4 which can overcome overhead and security limitations.
Correlation between Excessive Daytime Sleepiness (EDS) and self-reported and objective nasal characteristics.

BACKGROUND The aim of the present study was to explore the relationship between excessive daytime sleepiness (EDS), Peak Nasal Inspiratory Flow (PNIF), and patient reported symptoms from the nose. METHOLOGY Six hundred and fifty one consecutive patients referred to a hospital on suspicion of sleep related breathing disorder (SRBD) were included in the study. Daytime sleepiness was assessed by Epworth Sleepiness Scale (EpSS). Nasal airflow was measured with Peak Nasal Inspiratory Flow (PNIF). Symptoms of sino-nasal dysfunction and diseases were graded on Visual Analogue Scales (VAS). RESULTS EpSS score was not correlated with nasal flow as measured by PNIF or nasal obstruction - VAS scores. There were significant associations between daytime sleepiness and patient-reported VAS-scores on nasal discharge, headache, coughing, general health and to some extent sneezing when age, gender, BMI and reported co-morbidity levels were adjusted for. CONCLUSION A clinical implication of this is that patients with EDS may be evaluated and treated for sino-nasal disease, while medical and surgical measures to open the nose per se may not be effective therapeutic options. A scientific implication is that the relationship between SRBD and sino-nasal disease should be further investigated.
Error Performance of Maximal-Ratio Combining with Transmit Antenna Selection in Nakagami-m Fading Channels

In this paper, the error performance of an uncoded multiple-input-multiple-output (MIMO) scheme combining single transmit antenna selection and receiver maximal-ratio combining (the TAS/MRC scheme) is investigated for flat Nakagami-m fading channels with binary phase-shift keying (BPSK) modulation. The asymptotic bit error rate (BER) expression is derived, which indicates a diversity order equal to the product of the m parameter, the number of transmit antennas and the number of receive antennas. The exact BER expression is also obtained as an infinite series, which converges for reasonably large signal-to-noise ratios (SNRs). The theoretic analysis is verified by simulation
Interpretable and Scalable Bayesian Models for Advertising and Text

How much should a company be willing to pay to acquire a customer given his or her background characteristics? In practice, the price is often linked to expected lifetime revenues. However, inferring lifetime outcomes is not possible with standard regression models since only partial purchase histories of variable duration are observed. We propose a joint model for the lifetime value of a customer and the rate of purchases over time that makes it possible to extrapolate lifetime outcomes from observation periods of arbitrary length. This model also enables leveraging multiple types of outcomes that measure customer engagement but do not directly produce value. We demonstrate superior predictive performance in a sequential prediction task that replicates real world application of the model where customer purchase histories available before a given day are used to predict the outcomes for customers acquired on that day. In addition, we show that Pareto/NBD and other “exit time” models popular for customer lifetime value estimation are a special case of PPR where the intensity function is constrained to be a mixture of Uniforms. We show that by directly parameterizing the intensity function, our approach is not only more flexible but has much smaller data storage requirements and computational complexity.
Simulation study of fusion output control in DEMO

For fusion reactor power plant, stable output is an absolute requirement. Demonstrating the capability of tokamak to generate stable power is a mandatory task of DEMO. In DEMO, however, available diagnostics and actuators are tightly restricted due to the necessity to secure sufficient tritium breeding rate and the severe conditions under the violent heat and neutron load. Furthermore, economical condition demands the DEMO plasma to be strongly autonomous. Then the external actuators are relatively powerless compared to these autonomous natures. At present, any reliable methodologies to control burning plasma under such special constraints of DEMO have not been established. Inspection of controllability to be considered in the DEMO designing activity should be initiated urgently. Full-passive control of all physical quantities based on precise measurement is apparently hopeless in DEMO because of poor diagnostics. To the contrary, full-active control based on real-time predictive simulation of whole system has no prospect either, because of too heavy computational load. We must find a solution artfully composed of active and passive control. In the present work, we adopted following approaches.
CODE SLICING TO IMPROVE CASE CLASSIFICATION ACCURACY

Finding a good classification algorithm is an important component of many data mining projects. Data mining researchers often use classifiers to identify important classes of objects within a data repository. The goal of this paper is to improve the case classification accuracy in data mining. The paper achieves this goal by introducing a new approach of similarity-based retrieval based on program slicing techniques and is called Case Slicing Technique (CST). The proposed approach helps identify the subset of features used to compute the similarity measures needed by the classification algorithms. The idea is based on slicing cases with respect to the slicing criterion. Likewise, the paper presents the experimental results of the CST using five real-world datasets which are; Australian Credit Application (AUS), Cleveland Heart Disease (CLEV), Breast Cancer (BCO), German Credit Card (GERM) and Hepatitis Domain (HEPA). The paper compares CST with other selected approaches. The results obtained showed that the classification accuracy can be improved when we use CST.
An 8-bit two-step flash A/D converter for video applications

A novel configuration for two-step analog-to-digital (A/D) flash conversion is described. The coarse and fine conversions are performed with a four-bit multiplexed flash converter, so only 15 comparators are necessary for an eight-bit converter. The D/A conversion and the subtraction required for the circuit operation are performed using the charge redistribution technique. A test chip, integrated with a 3-μm CMOS technology (area=3 mm2), has demonstrated the effectiveness of the proposed configuration
ECG frame classification using dynamic time warping

Presents an electrocardiogram (ECG) frame classification technique realized by a dynamic time warping (DTW) matching technique, which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar nonstationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. A threshold is setup for DWT matching residual either to classify an ECG frame or to add a new class. Classification and establishment of a template set are carried out simultaneously. A frame is classified into a category with a minimal residual and satisfying a threshold requirement. A classification residual of 1.33% is achieved by the DTW for a 10-minute ECG recording.
Simulating a factory online using cloud computing and artificial neural networking

Cloud computing (CM) is bringing opportunities to various fields. In the manufacturing sector, cloud manufacturing (CMfg) borrows many useful concepts from CM. Among them, simulating a factory online is imperative. To this end, the time required for a simulation task needs to be estimated first. However, this topic has rarely been discussed. An artificial neural network (ANN) approach is proposed in this paper. In the proposed methodology, an ANN is constructed to estimate the required execution time for a simulation task. The real data of 90 simulation tasks have been collected to validate the proposed methodology. In addition, several existing methods were also applied to these tasks for a comparison. According to the experimental results, the proposed methodology outperformed the compared existing methods in improving the estimation accuracy. In addition, the planning horizon is the most decisive factor to estimating the execution time of a simulation task.
Using complexity-tracking in software development

CATS (Complexity Analysis and Tracking System) is a complexity-tracking system which uses the McCabe complexity analysis tool to construct and maintain an ongoing database of structural complexity values for a software system as it proceeds through its development and testing. Building on previous work which indicated a correlation between structural complexity and defect corrections, CATS allows for a tighter focus of code review efforts such as walkthroughs and inspections and aids in the design of regression, unit, and system tests. CATS has been implemented into the development and testing process for an operation-system software component denoted here as System A. The process for CATS implementation at System A involves two ongoing groups, the BIT (Build, Inspect, and Test) team and the development reams. The BIT team builds the source files, runs CATS, identifies modules for special attention in review and testing, uses the complexity information to design and execute test suites, and reports results to the development teams through a notes-files conference. The development teams use the information in their code efforts, and report their responses and experiences through replies in the notes-file. This creates a body of data, experience, and lessons-learned for use in further development. A CATS analysis has also been carried out for an operating-system facility in VMS (denoted as Facility B).
Radiation efficiency of handheld phones

Measurements of radiation efficiency for a handheld phone equipped with a patch and a helical antenna operated near the human user have been performed. Both measurements include a simple head plus hand phantom and live persons are considered. The position of the hand on the phone is found to be the main reason for the large variation in radiation efficiency among persons. The tilt angle of the phone and the distance between the head and phone only play a minor role.
Identifying word correspondence in parallel texts

Researchers in both machine translation (e.g., Brown et al, 1990) and bilingual lexicography (e.g., Klavans and Tzoukermann, 1990) have recently become interested in studying parallel texts (also known as bilingual corpora), bodies of text such as the Canadian Hansards (parliamentary debates) which are available in multiple languages (such as French and English). Much of the current excitement surrounding parallel texts was initiated by Brown et al. (1990), who outline a self-organizing method for using these parallel texts to build a machine translation system.
An approach for intention-driven, dialogue-based Web search

ion [76]. The top-level goal is abstract, whereas lower-level goals are more concrete. Progressively more detailed levels are passed all of the linguistic and nonlinguistic evidence observed by the system. Another approach, Probabilistic StateDependent Grammars (PSDGs), represents the probabilities of having specific plans using production rules [140]. Subgoals are modelled as non-terminals in a grammar. The recognition procedure keeps track of the current plans and state variables as a DBN, choosing the most likely string of plans as the current goal structure. In [141], Bauer employed Dempster-Shafer Theory to represent and combine the probability of goals given observed actions. The system explicitly accounted for ignorances due to incomplete information about the situation and/or a lack of knowledge about the agent’s typical behaviours. The basic procedure involves the reallocation of probability mass to goals that become more likely as indicated by the a priori goal probabilities and the probabilities of goals given observations. Figure 12: Bayesian network representing the possibility that going to the liquor store (ls2) is part of a liquor store shopping event (lss2) [123].
Statistical distributions of partially developed speckle based on a small number of constant scatterers with random phase

The speckle phenomenon is observed in coherent imaging systems such as synthetic aperture radar. The authors deal with situations in which the speckle arises from a small random number of constant amplitude scattering points within the resolution cell. Speckle in this context is referred to as partially developed. They first model the surface scattering statistics as a marked point process. They then derive closed form expressions and accurate approximations for the intensity probability density function of single look and multilook speckle using an orthogonal Gram-Charlier type series expansion having gamma-weighted generalized Laguerre polynomials as the basis functions. Using the Kolmogorov-Smirnov test, they show that the series expansions are a good approximation to the actual pdfs. <<ETX>>
Computational Foundations of Natural Intelligence

New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.
Bid-deficient fish delay grass carp reovirus (GCRV) replication and attenuate GCRV-triggered apoptosis

Bid, BH3-interacting domain death agonist, is a pro-apoptotic BH3-only member of Bcl-2 family, playing an important role in apoptosis. In the study, Bid genes from grass carp (Ctenopharyngodon idellus) and rare minnow (Gobiocypris rarus), named CiBid and GrBid, were cloned and analyzed. Bid was constitutively expressed in all examined tissues of grass carp, but the expression level varied in different tissues. Following grass carp reovirus (GCRV) stimulation in vivo, Bid and apoptosis related genes Caspase-9 and Caspase-3 was up-regulated significantly at the late stage of infection. Moreover, we generated a Bid-deficient rare minnow (Bid-/-) to investigate the possible role of Bid in GCRV-triggered apoptosis. We found that the survival time of Bid-/- rare minnow after GCRV infection was extended when compared with wild-type fish, the relative copy number of GCRV in Bid-/- rare minnow was lower than that in wild-type fish, and the expression level of Caspase-9 and Caspase-3 in Bid-/- rare minnow were significantly lower than that in the wild-type fish. Collectively, the current data revealed the important role of Bid during virus-induced apoptosis in teleost fish. Our study would provide new insight into understanding the GCRV induced apoptosis and may provide a target gene for virus-resistant breeding in grass carp.
Electromyographic Bridge—a multi-movement volitional control method for functional electrical stimulation: prototype system design and experimental validation

The voluntary participation of the paralyzed patients is crucial for the functional electrical stimulation (FES) therapy. In this study, we developed a strategy called “EMG Bridge” (EMGB) for volitional control of multiple movements using FES technique. The surface electromyography (sEMG) signals of the agonist muscles were transformed to stimulation pulses with various pulse width and frequency to stimulate the target paralyzed muscles using MAV/NSS co-modulation (MNDC) algorithm we proposed recently. Motion pattern classification based on linear discriminant analysis (LDA) was included to recognize the motion status and mapping the sEMG detection channel to the corresponding stimulation channel. A prototype EMGB system was built for real-time control of four hand movements. The test results showed that the movements can be reproduced with a successful rate of 92.5±3.5%. The angle trajectory of wrist joint and metacarpal-phalangeal joint can be mimicked with a maximum cross-correlation coefficient > 0.84 and a latency less than 300 ms.
A model-based 3-D vision system for bin-picking

A vision system to recognize 3-D objects in a scene and to evaluate their attitude is presented. This system uses a model-based approach to determine the attitude of 3-D industrial parts in the context of bin packing. It is based on the matching of the extended Gaussian image (EGI) extracted from the surface normal map (needle map) of an observed object with the EGI generated from a 3-D model using geometric modeling. It is shown that geometric modeling can be used to provide an object description (visible surface orientation referred to as a needle map) suited to the problem of 3-D object recognition and attitude evaluation. >
Experimental Evaluation of RGB-D Visual Odometry Methods

RGB-D cameras that can provide rich 2D visual and 3D depth information are well suited to the motion estimation of indoor mobile robots. In recent years, several RGB-D visual odometry methods that process data from the sensor in different ways have been proposed. This paper first presents a brief review of recently proposed RGB-D visual odometry methods, and then presents a detailed analysis and comparison of eight state-of-the-art real-time 6DOF motion estimation methods in a variety of challenging scenarios, with a special emphasis on the trade-off between accuracy, robustness and computation speed. An experimental comparison is conducted using publicly available benchmark datasets and author-collected datasets in various scenarios, including long corridors, illumination changing environments and fast motion scenarios. Experimental results present both quantitative and qualitative differences between these methods and provide some guidelines on how to choose the right algorithm for an indoor mobile robot according to the quality of the RGB-D data and environmental characteristics.
Modelling disinfection by-products in contact tanks

Numerical modelling has been extensively used in the field of environmental engineering as an efficient method for predicting the fate of contaminants. For chlorine disinfection contact tanks, current numerical models predict the disinfection processes as well as first-order functions for chlorine demand. In recent years, the study of the formation of Disinfection By-Products (i.e. DBPs) in drinking water has been a cause for public concern. Since both chemical analyses and monitoring of DBPs are very expensive and not yet widely available, the establishment of an efficient numerical model has become a priori for the analysis of DBPs. This study includes a second-order kinetic representation for chlorine consumption in the disinfection processes and incorporates this representation in a numerical model to predict the formation of DBPs. The model has been refined to predict the chlorine demand in the disinfection process and the distribution of the main DBPs in contact tanks, including primarily total trihalomethanes (TTHMs), dichloroacetic acid (DCAA) and trichloroacetic acid (TCAA).
A new approach to adaptive observer design for MIMO systems

The authors propose a novel approach to the design of adaptive observers for multi-input-multi-output (MIMO) systems. It guarantees global convergence for joint state and parameter estimation, provided some persistent excitation condition is satisfied. It is conceptually simple and computationally efficient. It can be applied to MIMO linear systems for online continuous-time system identification, fault detection and isolation, and adaptive control.
A simple downlink antenna array algorithm based on a hybrid scheme of transmit diversity and conventional beamforming

A hybrid scheme of conventional downlink beamforming and open loop transmit diversity using a dual polarized antenna array is studied. The performance of the proposed scheme is determined based on simple theoretical considerations. The scheme is found to provide a significant gain over conventional beamforming with the same complexity. It is furthermore shown that the scheme is applicable within the limitations of the UMTS specifications.
A machine learning methodology for the analysis of workplace accidents

Abstract This article proposes a methodology for the analysis of the causes and types of workplace accidents (in this paper we focus specifically on floor-level falls). The approach is based on machine learning techniques: Bayesian networks trained using different algorithms (with and without a priori information), classification trees, support vector machines and extreme learning machines. The results obtained using the different techniques are compared in terms of explanatory capacity and predictive potential, both factors facilitating the development of risk prevention measures. Bayesian networks are revealed to be the best all-round technique for this type of study, as they combine a powerful interpretative capacity with a predictive capacity that is comparable to that of the best available techniques. Moreover, the Bayesian networks force experts to apply a scientific approach to the construction and progressive enrichment of their models and also enable the basis to be laid for an accident prevention policy that is solidly grounded. Furthermore, the procedure enables better variable definition, better structuring of the data capture, coding, and quality control processes.
Simulation in International Relations Education

For the past 50 years, scholars and practitioners of international relations have used simulations as experimental, predictive, and educational tools to model real-world environments. This article will focus on the educational applications of simulations in international relations (IR), first reviewing the development of IR simulations and then tracing this history by examining the International Communication and Negotiation Simulations (ICONS) Project at the University of Maryland as a representative example of this genre. It will examine in particular the use of information technologies in facilitating and delivering simulations, and conclude with a brief discussion of how computer-assisted simulations have, in some cases, anticipated trends in the real world of diplomacy, and in others, attempted to respond to new trends.