Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolving a neural network

Although artificial neural networks have been shown to be effective in the computation of solutions to difficult problems a general theory has not yet been developed to provide guidance in their design and implementation. Genetic algorithms have also been shown to be effective in evolving solutions to optimization problems which involve objective functions that are not 'nice'. The approach presented here is the utilization of genetic algorithms to evolve the number of neurons in an artificial neural network, the weights of their interconnects, and the interconnect structure itself. With this approach, no a priori assumptions about interconnect structure, weights, number of layers. or to which neurons the inputs or outputs are connected need to be made. A combined neural network evaluation and genetic algorithm evaluation program has been written in C on a Sun workstation. The method has been successfully applied to the 9*9 bit character recognition problem. <<ETX>>
The Research of Event Detection and Characterization Technology of Ticket Gate in the Urban Rapid Rail Transit

Making events recognition more reliable under complex environment is one of the most important challenges for the intelligent recognition system to the ticket gate in the urban rapid rail transit. The motion objects passing through the ticket gate could be described as a series of moving sequences got by sensors that located in the walkway side of the ticket gate. This paper presents a robust method to detect some classes of events of ticket gate in the urban rapid rail transit. Diffused reflectance infrared sensors are used to collect signals. In this paper, the motion objects are here referred to passenger(s) or (and) luggage(s), for which are of frequent occurrences in the ticket gate of the urban railway traffic. Specifically, this paper makes two main contributions: 1) The proposed recognition method could be used to identify several events, including the event of one person passing through the ticket gate, the event of two consecutive passengers passing through the ticket gate without a big gap between them, and the event of a passenger walking through the ticket gate pulling a suitcase; 2) The moving time sequence matrix is transformed into a one-dimensional vector as the feature descriptor. Deep learning (DL), back propagation neural network (BP), and support vector machine (SVM) are applied to recognize the events respectively. BP has been proved to have a higher recognition rate compared to other methods. In order to implement the three algorithms, a data set is built which includes 150 samples of all kinds of events from the practical tests. Experiments show the effectiveness of the proposed methods.
Fast Human Detection Using Dynamic Contour and Histograms of Oriented Gradients

Human detection is a challenging problem, owing to variations in pose, body shape, appearance, clothing, illumination, and background clutter, in addition, the cameras or backgrounds make it even harder. But even so, it has many potential applications including net-meeting, security, human-computer interaction, gaming, and even health-care. Various new approaches have been proposed to solve this problem. We have studied and implemented a method by using dynamic contour [ and Histograms of Oriented Gradients [ to detecting human body fast and accurately in static images.
Real-time transputter-based measurement apparatus: Performance testing

The authors deal with transputter-based equipment used in online indirect measurements on induction motors, by using an estimation algorithm performed in real-time. Starting with a brief description of the actual equipment, some considerations relating to the multiprocessor architecture are reported to point out hardware and software solutions adopted to fit real-time requirements. The dynamic behavior of the measurement station is discussed to highlight the overall equipment performance in terms of the achievable accuracy and execution speed. Some experimental results concerning the working operations of an actual induction motor fed by an inverter are included. <<ETX>>
An index policy for a stochastic scheduling model with improving/deteriorating jobs

We consider stochastic scheduling models which have the natural character that jobs improve while being processed, but deteriorate (and may possibly leave the system altogether) while processing is diverted elsewhere. Such restless bandit problems are shown to be indexable in the sense of Whittle. A numerical study which elucidates the strong performance of the resulting index policy is complemented by a theoretical study which demonstrates the optimality of the index policy under given conditions and which develops performance guarantees for the index heuristic more generally. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 706–721, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10036
CMOS RF circuits for data communications applications

The author describes the recent development of two analog CMOS circuits operating at RF frequencies with applications to data communications. One is a four-quadrant analog multiplier which exhibits a 100-MHz bandwidth with a measured linearity error of 0.7% for X and Y inputs of 0.6 and 0.8 V, respectively. The other is a 90/spl deg/ phase shifter which maintains the grain and phase errors of less than 0.5 dB and 3/spl deg/, respectively, for a signal within 40-60-MHz frequency range.
Optimized Instrumentation of Runtime Verification

Runtime verification checks whether one system execution conforms to a group of specific requirements. One of the many problems concerned is how to lower the costly instrumentation overhead, because the performance is as important as correctness . The solutions to cope with the problem include static checking, and hardware based instrumentation . What makes a difference to our approach is that it takes into account the modular structure of the observed run, and our approach makes a tradeoff between observability and cost by means of simulated annealing algorithm. T he paper gives a definition of instrumentation observab ility in detail . The simulated annealing a pproach works out a n optimized instructive instrum entation solution contain ing part of all the observing points . An experiment is conducted on the LwIP protocol stack to prove the e ffect of our observing strategy.
Classification: An Overview of Selected Methodological Issues

Classification methods have two fundamentally different but related goals. The first is to build new classifications. The second is to assign unknown cases to existing classifications. Many traditional offender criminological classification systems are of questionable value, partly because of confusion concerning purposes and roles of classification. The link between purpose and method has recently become more important because of the emergence of a vast range of quantitative methods. The multidisciplinary origins of these methods have meant that some of their embedded concepts and logic may be inconsistent with criminological theory or data. No single method is best for all purposes. Used correctly, new methods may dramatically upgrade the quality of our classifications. A framework for creating and validating classifications in criminology is urgently needed. The framework should include interacting stages for choosing a content domain, constructing the classification, checking internal validity, and establishing external validity.
AUTOMATIC DETECTION OF MICROANEURYSMS AND HEMORRHAGES IN COLOR EYE FUNDUS IMAGES

This paper presents an approach for automatic detection of microaneurysms and hemorrhages in fundus images. These lesions are considered the earliest signs of diabetic retinopathy. The diabetic retinopathy is a disease caused by diabetes and is considered as the major cause of blindness in working age population. The proposed method is based on mathematical morphology and consists in removing components of retinal anatomy to reach the lesions. This method consists of five stages: a) pre-processing; b) enhancement of low intensity structures; c) detection of blood vessels; d) elimination of blood vessels; e) elimination of the fovea. The accuracy of the method was tested on a public database of fundus images, where it achieved satisfactory results, comparable to other methods from the literature, reporting 87.69% and 92.44% of mean sensitivity and specificity, respectively.
Time Series Analysis in the Task of Oil Equipment Malfunctions Forecasting

The task of oilfield equipment malfunctions’ early prevention is considered in the paper. Input data for the analysis are the indications of sensors, established in the well and represented as time series set. Due to the forecasting analysis (predictive analytics) of the given set it is possible to discover a new data vector, which will allow to classify the possible equipment failure and to eliminate it proactively. The main stage of this task is the prediction of a single time series. In this paper, we propose a new method of forecasting using r/φ algorithm and the artificial neural network training algorithm (GAhNN) [1], previously developed by the authors. In the paper a new forecasting method, based on the r/φ algorithm and the algorithm of artificial neural network training (GAhNN) [1], developed by authors before, is offered. Comparison with the known mostly applied forecasting algorithms – Kalman filter and ARIMA is fulfilled. Index Terms Forecasting, neural network, oilfield equipment, malfunction, Kalman filter.
Optical services in future broadband networks

Optical transmission technology is progressing to the point where it can deliver data at rates that can strain conventional electronic broadband networks. We discuss how optical networks may play a role in relieving this strain. Different optical network architectures are discussed according to the services they provide, the technologies used to implement those services, and the geographical size of the network. We first discuss broadband network architectures and where optics might play a role in the protocol stack. We then discuss in detail the services optical networks can provide, the technologies used to implement those services, and some of the major technological limitations. Finally, we use this knowledge base to analyze the potential role of optics in WANs, MANs, and then local area networks (LANs).
A comparison of ICA algorithms in surface EMG signal processing

Recent research has resulted in development of number of different Independent Component Analysis (ICA) technique. While there are some researchers who have compared their techniques with the existing methods for audio examples, there is no comparison of performance between ICA algorithms for biosignal applications. With ICA being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five ICA algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of surface electromyogram (sEMG) to identify subtle wrist actions.
A 300 MHz digital double-sideband to single-sideband converter in 1 μm CMOS

A 43-tap FIR Hilbert transform digital filter chip is described which implements both a double-sideband (DSB) to single-sideband (SSB) conversion with a decimation-by-2 and the converse operation of a SSB to DSB conversion with an interpolation-by-2. Over 70 dB of image rejection is achieved by the Hilbert transform filter. The 3.57/spl times/7.07 mm/sup 2/, 45 000 transistor chip was fabricated in a 1 /spl mu/m N-well CMOS process and operates at sample rates in excess of 300 MHz. >
Application of Using Simulated Annealing to Combine Clustering with Collaborative Filtering for Item Recommendation

tem-item collaborative filtering was widely used in item recommender system because of good recommend effects. However when facing a large amount of items, there would be performance reduction, because of building a very large item comparison dataset in order to find the similar item. K-means cluster had a very good effect in classification and a good performance even though the dataset being processed is very large. But the cold start was a problem to k-means and we must do some extra work to use it in item recommendation. By using the simulated annealing theory to combine the two methods to fixed the problems of the two methods mentioned above and take use of their advantages for better recommendation effect and performance. The experimental results show that, using simulated annealing to combine the clustering and collaborative filtering in item recommendation system can get more stable recommendation results of better quality.
Computer matching of areas in stereo images.

Abstract : This dissertation describes techniques for efficiently matching corresponding areas of a stereo pair of images. Measures of match which are suitable for this purpose are discussed, as are methods for pruning the search for a match. The mathematics necessary to convert a set of matchings into a workable camera model are given, along with calculations which use this model and a pair of image points to locate the corresponding scene point. Methods are included to detect some types of unmatchable target areas in the original data and for detecting when a supposed match is invalid. Region growing techniques are di9 for extend matching areas into regions of constant parallax and for delimiting uniform regions in an image. Also, two algorithms are presented to show some of the ways in which these techniques can be combined to perform useful tasks in the processing of stereo images.
INTERACTIVE MACHINE LEARNING TOOLS FOR DATA ANALYSIS

In this work we propose a scientific data exploration methodology and software environment that permits to obtain both data clustering/labeling and visualization. The approach is based on an elaboration pipeline, going from data importing to cluster analysis and assessment with each stage supported by dedicated visualization and interaction tools. Supported techniques include a stability based procedure for the algorithmic parameters estimation (i.e. number of centers in K-means or map dimension in SelfOrganizing Maps); a set of models realizing the fuzzy membership analysis and allowing users to select sub-clusters according to a given stability threshold; a tool for studying cluster reliability based on resampling; a tool for the interactive hierarchical agglomeration of clusters. Finally, a novel technique allowing the exploration and visualization of the space of clustering solutions is introduced. All implemented techniques are supported through appealing visualizations in a highly interactive environment.
Flight and fire control knowledge representation

The development of a conceptual framework for an operational, onboard, real-time multiprocessing computer system, capable of assisting the pilot in flight and fire control decisions, i.e. a tactical decision aiding expert system, is discussed. Air combat is considered as a game problem in which two opponent teams mutually endeavor to maximize their opportunities to destroy each other, while minimizing their own risk of loss. The authors describe their approach to generating and solving this game through semantic control theory, which is introduced to make such a problem accessible. Specifically, they introduced logic into the model to assist in breaking it down sufficiently so that the important aspects of the classical approach can be used at the low level of the solution hierarchy, while the high-level mechanism of the solution generates the minimal space of all possible missions, assuming team cooperation, and devises an efficient search procedure on this space to find the best mission. The difference between classical control and semantic control is essentially the introduction of a correlator block, which uses the system input and output to generate the classical feedback control law by symbolic manipulation. <<ETX>>
Design, Implementation and Simulation of an Experimental Multi-Camera Imaging System for Terrestrial and Multi-Purpose Mobile Mapping Platforms: A Case Study

Mobile Mapping Systems (MMS’s) are powerful tools for rapidly and massively collecting imagery in various environments (e.g., rural, semi-urban, urban). In particular, the data collected at terrestrial level can be exploited to complement aerial acquisitions for extending GIS databases,visualizing and modeling urban environments and studying environment morphogenesis over time. Hence, the development of mobile mapping platforms is a topic of great interest for many mapping agencies and surveying companies. Moreover, the experimental imaging systems of mapping vehicles are equipped with varied infrastructures in part resulting from the wide range of targeted applications. Determining a detailed design procedure for such imaging systems is of critical importance, and can be both arduous and time-consuming. Although commercial imaging systems can be exploited directly they are often pre-configured for specific applications. For these reasons, we propose a case study that deals with the development of an orientable and scalable imaging system. In particular, this paper focusses on the primary stage of the mechanical implementation of a multi-camera infrastructure. To this effect, we provide a design document that includes full technical specifications and drawings for creating a roof-mounted camera mast from a vehicle platform. We also provide an inventory of resources required for creating the system. As part of the design process we analyse some well-known MMS’s paying particular attention to their imaging systems. Finally, we evaluate the proposed design by creating the complete 3D mock-up of the camera system at real-scale and by simulating camera configurations from an experimental van platform. Experimental results clearly show the usefulness of this study in the design of an experimental and multi-purpose imaging system.
Modeling Multi-node Network under Heterogeneous M2M Services Arrival ⋆

With the development of IoT (Internet of Things), M2M (Machine to Machine, Man to Machine, Machine to Mobile network) services have found an increasingly wide utilization in many fields. In this paper, we investigate the network performance of heterogeneous M2M services. The multi-node bearing network is modeled as a tandem queuing system. Superposition of multiple MMBP-2 is made to model the arrival process in order to describe the characteristics of mixed M2M traffic arrival. To simplify resolving procedure, we innovatively propose a mathematical method to synthesize N MMBP-2 as one MMBP2 N . Then solving a multi-input tandem queuing system is converted to solving that with an ordinary single input. A decomposition technique and a parameter fitting algorithm are used to solve the tandem queuing system in this paper. We explore network performance of heterogeneous M2M services under two simulation scenarios. Some reliable measures to guarantee QoS (Quality of Service) of the network are suggested in this paper.
Convolutional interleavers for stream-oriented parallel concatenated convolutional codes

The design of convolutional interleavers (CIs) is considered for a stream-oriented parallel concatenated convolutional coding (PCCC). Design rules are developed for CIs using analysis of the interleaving equations as well as simulation results. Results show that CIs are viable design options which can offer improved performance for low latency designs as well as implementation advantages for PCCC.
Twenty years of MALL project implementation: A meta-analysis of learning outcomes

Abstract Despite the hundreds of Mobile-Assisted Language Learning (MALL) publications over the past twenty years, statistically reliable measures of learning outcomes are few and far between. In part, this is due to the fact that well over half of all MALL-related studies report no objectively quantifiable learning outcomes, either because they did not involve MALL implementation projects, or if they did, learning gains were only based on subjective teacher assessments and/or student self-evaluations. Even more so, the paucity of statistically reliable learning outcome data stems from the short duration of projects and small numbers of students involved. Of the 291 distinct studies examined in this review only 35 meet minimal conditions of duration and sample size, i.e., ten experimental subjects over a period of at least a month. Sixteen of these suffer from serious design shortcomings, leaving only nineteen MALL studies that can reliably serve as a basis for determining the learning outcomes of mobile-based language applications. Of these studies, fifteen can be considered to report unequivocal positive results, with those focusing on reading, listening and speaking without exception evidencing a MALL application advantage. Four studies, all focusing on vocabulary, reported no significant differences.
Efficient distributed power control for cellular mobile systems

Various distributed carrier-to-interference (C/I) balancing procedures have been developed to cope with implementational drawbacks of centralized power-control schemes. However, it would be inevitable for these distributed schemes to lose some system performance compared to a centralized one since they control their power level based on only the local C/I measurements. An efficient distributed power-control (DPC) scheme is proposed to maximize the system performance. A distributed C/I balancing algorithm is suggested, which converges fast and is robust to C/I estimation errors. A simple instability detection rule is also suggested to accelerate the balancing phase of DPC schemes. Numerical results indicate that our algorithm achieves improvements in terms of the outage probability as well as the algorithm speed. Robustness to the C/I measurement errors is also explored.
Research on Machine Translation Technology in English Translation Process

Translation plays an important role in the world economic and cultural exchanges. Translation is divided into machine translation and human translation, which is complement each other in promoting world economic and social development process. In this paper, Collaborative Translation gets much attention, along with the growth of collaborative translation, English translation technology also towards a new milestone, the characteristics of collaborative translation process and scientific literature are briefly introduced, and collaborative translation technology English Translation applications made a brief explanation. From the perspective of the development of machine translation, comparative analysis of the characteristics of human translation machine translation strengths and weaknesses, and we make relevant response measures and selection criteria translation approach. The specific translation system is analyzed from the perspective of textual and the Collaborative Translation shortcomings, as well as interpretation of collaborative translation features, functions and its impact on the meaning and sentence meaning.
Logical Data Deletion in High-Performance De-Duplication Backup

Data de-duplication divides backup stream into chunks and eliminates duplicate chunks across the entire system, thus remarkably reduces the storage and bandwidth requirement for backups. However, this technique also introduced many new problems among which the performance problem has been resolved by many of the existing solutions, while the logical data deletion problem is not well studied till now. This paper studied the logical data deletion mechanism in de-duplication backup systems, analyzed the memory overhead of the Bloom filter, which supports both high performance de-duplication and logical data deletion, and proposed a lazy deletion method to minimize the influence of logical data deletion on de-duplication performance.