Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the relationship between IFA remuneration and advice quality

This article deals with the interaction between commission remuneration of independent financial advisers and selected sales factors, including the quality of advice. Utilizing data on investment transactions and a linear model with mixed effects, we have found that the link between commission and quality of the subsequent recommendation is not homogeneous, and advice-bias potential is present only in a limited range of organizational environments, connected mainly to the flat-structure business model. On the other hand, arbitrage between different product classes was found to create a biasing potential across almost all types of firms, creating potential for market systemic risk. Finally, the effect of information provided was proved to be significant only to a very limited extent. © 2017 Academy of Financial Services.
NEAR OPERATIVE VIRUSTOAST WITH SOFTWARE DATA DISCOUNT METHODS

To lessen time cost in manual work, text classification techniques they can fit on conduct automatic bug triage. Within this paper, we address the issue of understanding reduction for bug triage, i.e., the easiest method to reduce the scale and improve the standard of bug data. Software companies spend over 45 percent of cost when controlling software bugs. An unavoidable step of fixing bugs is bug triage, which aims to properly assign a developer to a different bug. To uncover an order of applying instance selection and possess selection, we extract attributes from historic bug data sets creating a predictive model for each new bug data set. We combine instance selection with feature selection to concurrently reduce data scale within the bug dimension along with word dimension. To conclude result shows our data reduction can effectively reduce the data scale and lift take a look at bug triage. We empirically investigate performance of understanding reduction on totally 600,000 bug reports of two large free projects, namely Eclipse and Mozilla. Our work supplies a types of leveraging techniques on human sources to create reduced and-quality bug data in software development and maintenance.
ANN-Based Prediction of Turning Rate of Traffic Flows at Intersection

Based on an improved back-propagation neural network,a predication model for the turning rate of traffic flows at intersections was proposed to predict traffic flows for the signal control of intersections.The corresponding method to determine necessary parameters in this model was given.To improve the learning rate and reliability of neural network algorithms,the self-adaptive learning rate approach and the gradient descent with momentum method were adopted.In addition,a simulation was carried out to prove the correctness of the proposed model.The research result shows that compared with the average value method,the proposed model can decrease the mean absolute relative error by 1%~3%.
Channel estimation and normalization by coherent spectral averaging for robust speaker recognition

Speech and speaker recognition systems degrade rapidly in the presence of mismatch between training and testing conditions. In a real-world scenario, speech is usually recorded through telephone handsets having diverse characteristics, and then transmitted over commercial telephone lines. Varying transmission channels and telephone handsets introduce convolutional distortion resulting in mismatch between training and testing conditions. The objective of this dissertation has been to develop techniques to estimate the channel with which a speech signal has been corrupted and use the estimated channel for normalization, thereby improving system performance. Conventional approaches to channel normalization such as Cepstral Mean Normalization and Periodogram Averaging are based on averaging the magnitude spectrum. However, averaging the magnitude spectrum results in significant residual speech in the average, leading to a poor channel estimate, particularly of sharp cut-offs and steep roll-offs. This dissertation takes an alternate approach to channel estimation by employing the coherent or complex spectral average instead of the magnitude average, thereby preserving phase information during the averaging process. This significantly reduces the amount of residual speech in the average and results in a much better channel estimate. The new channel estimation technique, Coherent Spectral Averaging , aims to estimate the channel accurately from the complex spectral average of the channel, corrupted utterance and convert it into an inverse filter and use it for normalization. In addition to the basic Coherent Spectral Averaging technique, an improved version that is based on using a reference utterance is also presented. A refinement process was also developed, that utilized the reference utterance to further enhance the estimated channel. The Coherent Spectral Averaging technique was evaluated on speaker recognition and channel/handset identification experiments. These experiments were performed on speech corrupted with simulated channels and also on real-world telephone data. Experiments were also performed on data recorded through multiple telephone handsets. The Coherent Spectral Averaging technique, particularly with the reference utterance and with the refinement process yielded excellent channel estimates that closely tracked sharp cut-offs and steep roll-offs in the channel. The Coherent Spectral Averaging techniques also resulted in significant improvement in speaker recognition performance on channel/handset mismatched speech, most often performing better than Cepstral Mean Normalization, and in many cases restoring performance to that under channel free conditions.
CURE Algorithm-based Inspection of Duplicated Records

To inspect duplicated records,the Clustering Using Representatives(CURE) algorithm is ameliorated.The definition of pre-sampling is put forward,which can find the distribution of duplicated records so as to improve exactness of random sampling in record sets.A new method of choosing representative records for a cluster is proposed,which is based on distance infection weight.With this method,representative points are selected not only according to the density of the clusters,but also according to the importance of points including some isolated points.This method can make selecting representative points suitable.Both theory and experiment show that it is an effective approach to detect the similar duplicated records.
Forensic Readiness Capability for Cloud Computing

Cloud computing services represent the actual computation delivery to the most of customer communities. Such services are regulated by a contract called Service Level Agreement (SLA), cosigned between customers and providers. During its validity time several contractual constraints have to be respected by the involved parties. Due to their popularity, cloud services are enormously used and unfortunately also abused, especially by cyber-criminals. A manner for guaranteeing and enhancing cloud service security is the provisioning of a forensic readiness capability to them. Such a capability is responsible to perform some activities aimed to prepare the services for a possible forensic investigation. Sometimes, the crimes are related to some contractual constraint violations without the parties are aware of. Thus, a dedicated forensic readiness capability interacting with cloud services and detecting the SLA violations by analysing some cloud log files can guarantee more control on such contracts. In this dissertation, a formal model aimed to represent a forensic readiness capability for the cloud that detects contractual violations is presented, together with a prototype system running on a specific case study. Statement of Original Authorship I hereby certify that the submitted work is my own work, was completed while registered as a candidate for the degree stated on the Title Page, and I have not obtained a degree elsewhere on the basis of the research presented in this submitted work.
A Proposal of a Cross-Browser User Tracking Method with Browser Fingerprint

of Bachelor Thesis A Proposal of a Cross-Browser User Tracking Method with Browser Fingerprint Today, modern web browsers and browser plug-ins provide a rich set of interfaces for many Rich Internet Applications. But on the other hand, when web users access to the Internet, there are many browser’s information sending upon each request, for example, their installed plug-in set and even installed font set on their system. Especially this information is sending without users’ awareness. But the problem is when we combine all information together, they could become a personally identifiable information. This thesis introduces a method to track users online behavior using information that certainly sending upon each request connection which we call Browser Fingerprint. We will show that without Cookies and user awareness, we can track users browsing history each time they access to web server. Besides showing that users could be profiled like using third party cookie, we also show that browser fingerprint can be linkable even user using many browsers on the same computer or even browser from other devices. To make a clear picture of current privacy and security problem, by collecting only information transmitted upon each request connection to the Web server, we discuss how to defend against Browser Fingerprint. However we reach the conclusion where currently there is still a tradeoff because a method to defend against Browser Fingerprint could itself become fingerprinting information. We will also discuss about privacy aspect by building a scenario to show how users privacy are threatened each time information is collected enough.
Reclaiming the periphery: Kinetic perimetry in patients with glaucoma

Static automated perimetry of the central 30˚ is the most often used visual field test in glaucoma patients. Short test durations are achieved by focusing on a central region, which constitutes ~20% of the visual field. However, ignoring the periphery may sacrifice information on how patients are affected functionally. Peripheral vision is important for guiding attention, balance and mobility. An efficient standard automated examination for the peripheral visual field has not been established yet. This thesis aims to lay groundwork for the development of such a test. I introduce a kinetic automated test, which estimates an isopter with three repeated presentations per meridian. I ask whether measuring a peripheral isopter adds information to central visual field test results, investigate retest reliability and evaluate the efficiency of test procedures with repeated presentations through computer simulations. Moreover, I investigate how visual field thresholds obtained with static and kinetic stimuli relate to each other and examine the influence of stimulus sizes III and V on static threshold estimates. I also investigate the relationship between response variability and contrast sensitivity in the peripheral visual field. Based on the results, I suggest using repeated presentations in automated kinetic tests. I demonstrate that data driven computer simulations are useful for the development of efficient automated kinetic perimetry. The frequency-of-seeing results suggest that response variability to static stimuli in the far periphery is lower than suggested by previous data (Henson et al., 2000). This is relevant to future computer simulations of peripheral visual field tests with static automated perimetry. As a future avenue for examining the visual field periphery I propose a combined static kinetic automated visual field test, which combines a peripheral isopter as a region of interest with static stimuli inside this region. In a separate investigation, I examine the influence of visual field damage on reading performance and evaluate the relationship between reading performance and eye movements, using a within-patient between-eye study design in glaucoma patients with asymmetrical visual field loss. Between-eye reading performance was affected by visual field loss and co-occurred with specific eye movement patterns. The within-patient between-eye design appeared to be useful for investigating the relationship between visual field loss and functional disability.
An information hiding method in advanced audio coding(AAC)

The problem of information hiding has been studied since 1990′s.Based on describing the coding-decoding process of AAC and the general ideas of hiding in AAC,a hiding idea of using the relationship between Huffman codebook selection and statistic properties of quantified values in frequency domain is proposed,and the process of information hiding and extracting is also described.The availability of the idea is proved by an experiment of hiding 526 bits in an AAC compressed audio signal of 3 seconds.Some present hiding detection methods are limited to adapt this idea.
Geometric compressed sensing and structured sparsity

Compressed sensing is a novel methodology in data processing, which takes an advantage of the fact that most signals are sparse (have a small number of nonzeros), or admit a sparse representation, i.e., can be represented as a linear combination of few elements of a given frame (dictionary). Sparsity then allows one to recover the signal from considerably fewer linear measurements than what is required by traditional methods. In the first part of the thesis, we exploit sparse geometric structure of the signal. We consider signals consisting of unions of a few discrete lines as the simplest case of geometric sparsity. We investigate the frame properties of the system of discrete lines and the application of compressed sensing to such signal models, for example, for separating discrete lines and points. The second type of structured sparsity that we consider is, signals which are sparse in a dictionary of timeand frequency-shifts, i.e., a Gabor system. We are interested in Gabor systems generated by difference sets, which can be seen as lines in (finite) projective geometry. We further view this system as a fusion frame, show that it is optimally sparse, and moreover an equidistant tight fusion frame, i.e. it is an optimal Grassmannian packing. In the second half of this thesis, we move from compressed sensing to phase retrieval: if compressed sensing studies the recovery of signals from a set of linear, non-adaptive measurements, phase retrieval tries to recover signals from only the absolute values of those measurements. In general, the signal in the phase retrieval problem is not necessarily sparse, but there has been an increased interest in the recent years in including the sparsity assumption for this problem, and by that lowering the number of measurements needed for recovery of the signal. We investigate the case when the signal itself is not sparse, but it has a sparse representation in an arbitrary dictionary. Finally, we consider the phase retrieval problem in the case when the measurements are timeand frequency-shifts of a suitably chosen generator. We prove an injectivity condition for recovery of any signal from all time-frequency shifts, and for recovery of sparse signals, when only some of those measurements are given. We discuss which generators are suitable for sparse phase retrieval from Gabor measurements, and provide an algorithm for solving this problem.
Digital Speech Processor For The Nucleus 22-channel Cochlear Implant

A cochlear implant digital speech processor (CI-DSP) for the Nucleus 22-channel auditory prosthesis has been implemented using a single chip digital signal processor (TMS320C25, TI). For evaluation experiments the CI-DSP was incorporated in a laboratory computer (PDP11/73), which provides interactive parameter control, graphic display of input/output signals and facilities for off-line speech file processing. In addition to the generated stimulus parameters for the cochlear implant an acoustic signal based on a perceptive model of auditory nerve stimulation is simultaneously output. For field studies and as a take home device for Cl-users a wearable battery-operated unit has been built. Two new digital speech processing strategies were evaluated with CI-users. Their speech test scores were significantly higher with the CI-DSP than with their own wearable speech processors.
Bandit learning for sequential decision making: a practical way to address the trade-off between exploration and exploitation

The sequential decision making is to actively acquire information and then make decisions in large uncertain options, such as recommendation systems and the Internet. The sequential decision becomes challenging since the feedback is often partially observed. In this thesis we propose new algorithms of “bandit learning”, whose basic idea is to address the fundamental trade-off between exploration and exploitation in sequence. The goal of bandit learning algorithms is to maximize some objective when making decision. We study several novel methodologies for different scenarios, such as social networks, multi-view, multi-task, repeated labeling and active learning. We formalize these adaptive problems as sequential decision making for different real applications. We present several new insights into these popular problems from the perspective of bandit. We address the trade-off between exploration and exploitation using a bandit framework. In particular, we introduce “networked bandits” to model the multi-armed bandits with correlations, which exist in social networks. The “networked bandits” is a new bandit model that considers a set of interrelated arms varying over time and selecting an arm invokes the other arms. The objective is still to obtain the best cumulative payoffs. We propose a method that considers both the arm and its relationships between arms. The proposed method selects an arm according to the integrated confidence sets constructed from historical data. We study the problem of view selection in stream-based multi-view learning, where each view is obtained from a feature generator or source and is embedded in a reproducing kernel Hilbert space (RKHS). We propose an algorithm that selects a near-optimal subset of m views of n views and then makes the prediction based on the subset. To address this problem, we define the multi-view simple regret and study an upper bound of the expected regret for our algorithm. The proposed algorithm relies on the Rademacher complexity of the co-regularized kernel classes. We address an active learning scenario in the multi-task learning problem. Considering that labeling effective instances across different tasks may improve the generalization error of all tasks, we propose a new active multi-task learning algorithm based on the multi-armed bandits for effectively selecting instances. The proposed algorithm can balance the trade-off
Chinese Comparative Sentences Identification and Comparative Relations Extraction

Automatic comparative sentences identification and comparative relations extraction contribute to opinion mining and information recommendation.This paper constructs a Chinese Comparative Pattern Database to identify comparative sentences.Moreover,several types of features are chosen to establish a Condition Random Field(CRF) model for the comparative relations extraction.Experiment shows the Chinese Comparative Pattern Database contributes to the identification of comparative sentences,and the proposed types of feature set are valid to improve the result of comparative relation extraction by CRF model.
Measuring complexity in mixed-model assembly workstations

In an effort to maintain or increase their market share and at the same time prevent costs from escalating, manufacturing organisations are increasingly using their current manufacturing system to produce custom output. As a consequence, the large number of product variants increases significantly the complexity of manufacturing systems, both for the operators as for the support services. This is especially true in automotive industry, where customisation is increasing at a rapid pace. To counter the ensuing loss of productivity, a more fundamental approach to dealing with this complexity in manufacturing processes is required. In order to investigate the impact of complexity on production performance, one must first delineate the concept and then identify as unambiguously as possible highly complex workstations. This article defines complexity at the workstation level and proposes a complexity measure for mixed-model assembly workstations. Based on data from several leading automotive companies from Belgium and Sweden, some statistical models are proposed to characterise workstations complexity. The models are described and their validity and accuracy are discussed.
Wideband VCO with Simultaneous Switching of Frequency Band, Active Core and Varactor Size

As the tuning range of integrated LC VCOs increases, it becomes difficult to co-design the active negative resistance core and the varactor optimally for the complete frequency range. The VCO presented here solves this by adjusting the negative resistance with a switched active core. Also the VCO gain variations are counteracted by employing an analog varactor that can change in size. The implementation in 0.13mum CMOS shows a tuning range from 3.1 to 5.2 GHz, with a power consumption varying accordingly from 2.1 to 7.7 mA from a 1.2V supply. The measured phase noise is -118 dBc at 1 MHz from a 4-GHz carrier
Are Consumers Willing to Pay More for Traceability? Evidence from an Auction Experiment of Vietnamese Pork

This study aims to conduct the consumer’s willingness to pay (WTP) for traceable pork products by using the auction experiments methods, estimate the factors affecting to the WTP and identify the demand for specific information in traceability systems from information of consumers in Vietnam. The results indicated that, the WTP for traceable pork products of consumers is higher than 24%, comparing to the non-traceable pork products. The frequency of pork consumption, gender, age, education, health self-report and consumer’s attitude from risk of unsafe pork products were the influencing factors to the consumer’s WTP for traceable pork. The necessary information of traceability, suggested by consumers, included: information of additives included in the purchased pork, place of origin, expiration date and quality certificate.
Improved feature selection method and TF-IDF formula based on word frequency differentia

The vectorization of documents affects the speed and accuracy of text categorization greatly.The most common used formulas: TF-IDF,MI,and IG were analyzed.The method of feature selection based on word frequency differentia was proposed and TF-IDF formula was modified to improve the quality of feature selection,the speed and accuracy of categorization.
Calibration and Uncertainty Analysis of Predictions from Computational Models

Abstract: Computer experiments are simulations of physical experiments performed by exercising a mathematical model for a physical or chemical process, to produce model outputs corresponding to sets of values of inputs to the model. They are especially useful when the corresponding physical experiments are difficult or expensive. In these cases one will perform a relatively small number of physical experiments that are used to calibrate the simulation model. This is then employed to explore the space of values of the inputs extensively, in a way that “interpolates” the results of the physical experiments. Quantification of uncertainty for the outputs of such computer experiments is of great interest. The sources of uncertainty are in part due to the experimental measurement uncertainty in the inputs, and in part due to inadequacies of the underlying mathematical model. This paper presents methods for calibration and assessment of both types of uncertainty and demonstrates their use on two simple fire models.