Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPARISON OF TRAINING STRATEGIES FOR CONVNETS ON MULTIPLE SIMILAR DATASETS FOR FACADE SEGMENTATION

Abstract. In this paper, we analyze different training strategies and accompanying architectures for Convolutional Networks (ConvNets) when multiple similar datasets are available using the semantic segmentation of rectified facade images as example. Additionally to direct training on the target dataset we analyze multi-task learning and fine-tuning. When using multi-task learning to train a ConvNet, multiple objectives are optimized in parallel. Fine-tuning optimizes these objectives sequentially. For both strategies, the tasks share a common part of the ConvNet for which we vary the depth. We present results for all strategies and compare them regarding the overall pixel-wise accuracy and show that for the special case of facade segmentation there are no significant differences using multiple datasets or not or training a ConvNet with different strategies.
From Data Mining to Knowledge Discovery in Databases

■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.
Review Paper on 4-Neighbour Parametric Route Selection Approach to Reduce Communication in Mobile Networks

Routing is about to generate the optimum communication path in terms of energy and distance. But in an adhoc network, the network is having limitations. It has limitations such as congestion, the attack over some particular nodes. In such case to perform the effective communication there is the requirement of some such routing approach that can provide efficient path with optimized energy and distance. To achieve this we can implement ACO based routing approach is defined to generate the intruder safe path over the network.
An MOS four-quadrant analog multiplier using simple two-input squaring circuits with source followers

An MOS four-quadrant analog multiplier is described. It is based on the square-law dependence of the MOS-transistor drain current on the gate-to-source voltage in the saturation region. One input is applied to the gate directly while the other input is applied to the source through a source-follower buffer stage. The circuit is realized with only 12 MOS transistors and two resistors. The circuit has been fabricated using a metal-gate NMOS process which has separate p-wells to eliminate the substrate bias effect. The multiplier achieves less than 0.45% nonlinearity when the input voltage range is 40% of the supply voltages, and it achieves a -3-dB bandwidth of 30 MHz. The total harmonic distortion is less than 0.6%. The power consumption and chip size are 8 mW and 1.2 mm/sup 2/, respectively. The second-order effects for this type of multiplier are considered in detail. >
A speech controlled artificial limb based on DSP chip

A speech signal pattern recognition DSP-based system is constructed for real-time control of artificial limb through precise identification of speech and speech command. The portable DSP-based system provides a more effective method to provide multiple control commands than the electromyographic signals from the amputation stump or residual limb. The traditional prosthetic arm can not achieve many complex actions because the EMG of the residual limb is hard to detach from the many different control signals. Goals of multiple command recognition and low cost were achieved by using a portable DSP-based system.
Effects Of Environmental Uncertainty On Perception Of Information Systems Issues

The current study reports the results of a survey of CEOs and senior information systems managers to examine the relationship between perceived environmental uncertainty (PEU) and IS issues that have appeared repeatedly in MIS Quarterly. Senior Information Systems managers in highPEU firms ranked a number of issues differently than those in low-PEU firms. Additionally, information systems issues overall are more important on average in high-PEU firms than in lowPEU firms. Also, high PEU results in some issues concerning external information to be deemed more important. A comparison of factor analyses for the two groups indicates firms reporting high levels of PEU view external IS issues differently than those firms with low levels of PEU; external IS issues load on a separate construct for high-PEU firms. That construct is not found in low-PEU firms.
Men Who Batter: Personality Variables, Relationship Variables, and Treatment Outcome

MEN WHO BATTER: PERSONALITY VARIABLES, RELATIONSHIP VARIABLES, AND TREATMENT OUTCOME. Lisa M. Petrica Virginia Consortium Program in Clinical Psychology, 1998 Director: Dr. Barbara A. Winstead This study investigated the relationship between the personality characteristics o f male batterers and treatment outcome. It also examined a pattern of communication found in violent couples where the male pursues the female and the female withdraws in an argument. The study also compared alcohol use with treatment outcome. Twenty-one men who attended a group psychoeducational treatment program for batterers completed the Minnesota Multiphasic Personality Inventory (MMPI-2). The men were divided into three groups based on their scores: non-pathological (normal profile), narcissistic/antisocial (elevated psychopathic deviate scale), and severely disordered (elevations on several scales). Pre and post-test measures were completed: Dyadic Adjustment Scale (DAS), Relationship Style Questionnaire (RSQ), Modified Conflict Tactics Scale (CTS), and a measure of minimizing and rationalizing (Min/Rat). The men in the present sample were similar demographically to previous descriptions of court-referred batterers except for a higher percentage of non-white participants. The results did not support any differences in treatment outcome for the three MMPI-2 subtypes of male batterers. The data supported the results o f Gondolf (1977) who completed a very extensive research project in this area of domestic violence. The present research also found no change in the male pursuit/female withdrawal communication R eproduced with perm ission of the copyright owner. Further reproduction prohibited without perm ission. pattern as a result of treatment. Finally, alcohol use was also found not to impact treatment outcome. Participants were easily categorized into the three personality groups: nonpathologicaL, antisocial/narcissistic, severely disordered. The non-pathological MMPI-2 group appeared “better” overall than the severely disordered group; less self pursuit, less partner withdrawal, more satisfaction with the relationship, and more affectional expression. The antisocial/narcissistic group generally fell in the middle of the other two categories, not significantly different from either. Implications of the results are discussed and suggestions for future research are outlined. R eproduced with perm ission of the copyright owner. Further reproduction prohibited without perm ission.
Paradoxes of Social Networking in a Structured Web 2.0 Language Learning Community

This paper critically inquires into social networking as a set of mechanisms and associated practices developed in a structured Web 2.0 language learning community. This type of community can be roughly described as learning spaces featuring (more or less) structured language learning resources displaying at least some notions of language learning methodology. Taking Livemocha as an example, and especially the Culture space, social networking will be analyzed in terms of the extent to which it is used and taken up by language learners by means of social networking technologies comparable to those of a Social Networking Site (SNS). In addition, we critically examine the role of social networking architectures in sustaining peer language learning in highly networked spaces. We argue that social networking, both as a conceptual framework and as a technical incarnation, can be put in the service of collective activity and instrumented in a way that leads to effective language learning, beyond merely being a trendy or fun component of an SNS-like community.
Subspace tracking based blind multiuser detection: a maximum likelihood estimation approach

A blind maximum likelihood multiuser detection scheme for DS-CDMA systems is proposed, based on signal subspace tracking. Different from conventional blind detection methods, we investigate the detection problem from the perspective of user's parameter estimation. The proposed scheme uses efficient subspace tracking algorithms and yields better performance and lower computational complexity in contrast to some of the existing detection approaches. A simulation example is given to compare the proposed method and some of the methods in literature.
Accreditation Of Open And Distance Learning: A Framework For Turkey

The purpose of this study is to develop a framework for the accreditation of higher open and distance learning (ODL) programs in Turkey. The study was designed as a sequential monomethod multistrand mixed model including two strands which were both qualitative (QUAL→QUAL). In the first strand, both quantitative and qualitative data were collected through a three-round Delphi study with an expert panel consisting of 28 experts. In the second strand, qualitative data were collected via focus group interview. Based on a comprehensive literature review and the findings from the study, a framework was proposed including an initial accreditation process for new ODL programs and a re-accreditation process for ongoing programs. In addition, 35 criteria for new programs and 42 criteria for ongoing programs were developed to be used in the accreditation process.
CHARACTERIZATION OF MEASUREMENT ERROR AND UNCERTAINTY IN WORKING STANDARDS

Measurement uncertainty and error in metrology of the basic proposition is frequently used measurement tester’s important concept. It has a direct bearing on the reliability of measurement results and the exact same value transfer. Quality statements for calibrated measuring instruments give the systematic error and the uncertainty of measurement at the moment of calibration. In contrast to this, the statement of conformity for verified measuring instruments indicates whether or not the error limits on verification specified by law are met. The conformity assessment is based on the result of a previous calibration. Calibration uncertainty thus becomes an uncertainty of conformity decision. In this study five years’ test and verification data of working standards such as non-automatic weighing instrument, weight measures, and volumetric measures were analyzed. Regression analysis has also been done to understand and predict time variance of error. This study also investigates how to introduce the concept of uncertainty in the criteria of conformity in type approval and verification. For that, calibration data of non-automatic weighing instruments were used. It was observed from the study that the uncertainty in type approval is different from the uncertainty in the performance of measuring instruments, the error grows with ageing of instruments and also there is instrument-to-instrument difference in error contribution. This study also addresses the differences, common bases and the relationship between calibration and verification. In particular, the relationships between legally prescribed error limits and uncertainty and the uncertainty contribution of verified measuring instruments are discussed.
Prognosis of remaining bearing life using neural networks

Abstract A new concept referred to as progression-based prediction of remaining life (PPRL) is proposed in the present paper in order to solve the problem of accurately predicting the remaining bearing life. The basic concept behind PPRL is to apply different prediction methods to different bearing running stages. A new prediction procedure which predicts precisely the remaining bearing life is developed on the basis of variables characterizing the state of a deterioration mechanism which are determined from on-line measurements and the application of PPRL via a compound model of neural computation. The procedure consists of on-line modelling of the bearing running state via neural networks and logic rules and not only can solve the boundary problem of remaining life but also can automatically adapt to changes in environmental factors. In addition, multi-step prediction is possible. The proposed technique enhances the traditional prediction methods of remaining bearing life.
Filtragem baseada em conteúdo auxiliada por métodos de indexação colaborativa

Recommender systems arose from the need to select and present relevant content to users according to their preferences. Among several existent methods, those based on content make exclusive use of information inherent to the items. This information can be created through automatic and manual indexing techniques. While automatic approaches require greater computing resources and are limited to the specific task they perform, manual methods are expensive and prone to errors. On the other hand, with the expansion of the Web and the possibility of common users to create new content and descriptions about different items and products, an alternative is to get these metadata created collaboratively by the users. However, this information, especially reviews and comments, may contain noise, besides being in a unstructured fashion. Thus, this study aims to develop methods for the construction of itemsâ?  representations based on collaborative descriptions for a recommender system. This study aims to analyze the impact that different feature extraction techniques, combined with sentiment analysis, caused in the accuracy of the generated suggestions, evaluating the results in both recommendation scenarios: rating prediction and ranking generation. Among the analyzed techniques, it is observed that the best is able to describe items in a more efficient manner, resulting in an improvement in the recommendation system. Keyword: Recommender systems, unstructured information, items’ representation, feature extraction, sentiment analysis.
Response of early soybean cultivars to nitrogen fertilization associated with Bradyrhizobium japonicum inoculation

ABSTRACT In early soybean cultivars of high productive potential, the use of chemical nitrogen (N) fertilizer may be a critical factor to meet the crop N requirements for obtaining high yields. In order to determine the response of early soybean cultivars to doses and times of nitrogen fertilizer application, associated with the inoculation of Bradyrhizobium japonicum, two field experiments were conducted in a Quartzipsamment soil from the Brazilian tropical Savannah, during the 2016/2017 and 2017/2018 growing seasons. The experimental treatments were arranged in a 2 × 3 × 4 factorial scheme, being two soybean cultivars (BRS 1074 IPRO and ST 797 IPRO), three application times of N fertilizer (sowing, 30 and 50 days after the emergence) and four N doses (0 kg ha-1, 20 kg ha-1, 40 kg ha-1 and 60 kg ha-1). The following variables were evaluated: plant height, shoot dry matter, number of nodules, nodule dry matter, first pod height, number of pods, number of grains per pod, 1,000-grain mass, grain yield and harvest index. The use of N fertilizer did not improve the production components and did not increase the soybean grain yield, regardless of the doses and times of application. Therefore, it was concluded that, if efficient strains of B. japonicum are used in areas of first soybean crop, in a Quartzipsamment soil with medium-high fertility, especially with a relatively high N availability due to the mineralization of the soil organic matter, there is no need to apply starter or late doses of nitrogen fertilizer.
The Indian inflation–growth relationship revisited: robust evidence from time–frequency analysis

ABSTRACT This article re-visits the inflation–growth nexus in India using the tools of wavelet, i.e. wavelet correlation, wavelet cross-correlation and scale by scale Granger causality test. Wavelet cross-correlation analysis shows that at the shortest scales inflation and economic growth were independent; at medium scales, there exists feedback effect; and at higher scales, only economic growth is leading to inflation. Furthermore, we find: (a) high and increasing dependence between inflation and economic growth, particularly after mid-2002; (b) high-frequency components of economic growth Granger-cause low-frequency component of CPI-based inflation and vice-versa, and at all scales economic growth Granger-cause inflation at scales of 4–6 and no evidence of causality was detected from WPI-based inflation to economic growth; (c) results indicate that there is no long-run causal link between inflation and economic growth. This study presents new insights for policymakers to sustain economic development by using inflation as an economic tool in India.
HQ-Learning: Discovering Markovian Subgoals for Non-Markovian Reinforcement Learning

To solve partially observable Markov decision problems, we introduce HQ-learning, a hierarchical extension of Q-learning. HQ-learning is based on an ordered sequence of subagents, each learning to identify and solve a Markovian subtask of the total task. Each agent learns (1) an appropriate subgoal (though there is no intermediate, external reinforcement for "good" subgoals), and (2) a Markovian policy, given a particular subgoal. Our experiments demonstrate: (a) The system can easily solve tasks standard Q-learning cannot solve at all. (b) It can solve partially observable mazes with more states than those used in most previous POMDP work. (c) It can quickly solve complex tasks that require manipulation of the environment to free a blocked path to the goal.
An Implementation of Optical Image Fingerprint Recognition System

Although there have been abundant research examining fingerprint recognition systems in the biometric recognition domain, recognition rate and speed remain significant problems. In order to achieve high recognition rates and real-time processing, a high-resolution image capturing device coupled with high speed processing is needed. As a potential solution, we propose a fuzzy identification method to get a higher recognition rate and increase recognition speed while avoiding deletion of necessary fingerprint features and removing unnecessary fingerprint features that do not affect recognition results. The images are cut into many small blocks, statistics pixels, and thresholds are set to judge the angles as eigenvalues. The FAR (False Acceptance Rate) and FRR (False Rejected Rate) are then tested by using multiple pieces of fingerprints on different people interactively. The results not only show a certain degree of recognition rate on low-quality fingerprint images, but also reduce the computational complexity on recognition rate. Additionally, there is a high requirement to request the accuracy of finger location, which indicates that there is still much space for system improvement.
Developing linear error models for analog devices

Techniques are presented for developing linear error models for analog and mixed-signal devices. A simulation program developed to understand the modeling process is described, and results of simulations are presented. Methods for optimizing the size of empirical error models based on simulated error analyses are included. Once established, the models can be used in a comprehensive approach for optimizing the testing of the devices. Models are developed using data from a group of 13-b analog-to-digital converters and are compared with the simulation results. <<ETX>>
An invariant traffic sign recognition system based on sequential color processing and geometrical transformation

One of the most noteworthy problems associated with conventional pattern recognition methods is that it is not easy to extract feature vectors from images which are not translation, rotation, and scale change invariant in outdoor noisy environments. This paper describes the development of an invariant traffic sign recognition system capable of tolerating the above variations. The signs are restricted to three types of warning signs and are all of red color. The developed method is insensitive to brightness changes as well as invariant to translation, rotation, scale change, and noise. The architecture of this system is based upon neural network supervised learning after geometrical transformations have been applied. The performance of this system is compared with other invariant approaches in terms of the percentage of correct decisions in outdoor noisy environments. <<ETX>>
Measuring Subgroup Preferences in Conjoint Experiments

Conjoint analysis is a common tool for studying political preferences. The method disentangles patterns in respondents’ favorability toward complex, multidimensional objects, such as candidates or policies. Most conjoints rely upon a fully randomized design to generate average marginal component effects (AMCEs). They measure the degree to which a given value of a conjoint profile feature increases, or decreases, respondents’ support for the overall profile relative to a baseline, averaging across all respondents and other features. While the AMCE has a clear causal interpretation (about the effect of features), most published conjoint analyses also use AMCEs to describe levels of favorability. This often means comparing AMCEs among respondent subgroups. We show that using conditional AMCEs to describe the degree of subgroup agreement can be misleading as regression interactions are sensitive to the reference category used in the analysis. This leads to inferences about subgroup differences in preferences that have arbitrary sign, size, and significance. We demonstrate the problem using examples drawn from published articles and provide suggestions for improved reporting and interpretation using marginal means and an omnibus F-test. Given the accelerating use of these designs in political science, we offer advice for best practice in analysis and presentation of results.
Optimized and Smooth Handover between Wi-Fi and WiMAX using Link Layer Triggers

ABSTRACT Mobile devices are beginning to have multiple wireless interfaces. There is a need for these devices to move freely across different networks and achieve seamless handoff across heterogeneous wireless network. In our simulation we performed handover between Wi-Fi (WLAN) and WiMAX network using link trigger [1] mechanism at Layer2. The trigger generated at Layer2 helps in reducing disconnection during handover latency. Trigger mechanism uses certain signal strength level to generate trigger event. Anticipation of the signal strength level improves packet loss performance for various speed of mobile station. The simulation is done for video traffic with NS- 2 [2] simulator. General Terms Wireless heterogeneous handover scenario, simulation & graphical results. Keywords to Multi interfaces , Heterogeneous handover, Link layer trigger, Power Level Coefficient, Layer 2 and Layer3 handover latency, Disconnection Time, WiFi (WLAN), WiMAX.