Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated and Perceptual Data Mining of Stock Market Data

Data mining large abstract data sets for useful patterns is an attractive proposition. It may, for example, reveal useful trading rules in stock market data. There are two basic types of data mining, Automated data mining tools use algorithms that allow the computer to search the data. Perceptual data mining tools present the data to the user’s senses (vision, hearing, touch) in a way that the user can search for useful patterns. The two methods are not disjoint, as rules discovered with the user’s perception can then be automated. This paper describes a case study, where a visual-auditory interface was used to uncover patterns in stock market data. Results from a formal evaluation are reported. The paper also includes a discussion on how to incorporate these results into an automated tool using an agent framework. INTRODUCTION A problem facing many areas of industry is the rapid increase in the amount of data and how to deal with it. However, these large amounts of data could also be considered a resource. The term data mining has been used to describe the diverse methods used to explore abstract data in the search for valuable and unexpected patterns [8]. The available tools for data mining can be considered in two broad categories, Automated Intelligent Tools and Human Perceptual Tools (see figure 1). Automated Intelligent Tools implement well-defined strategies for finding rules or patterns in data. These systems take advantage of a computer's capability to perform error-free, repetitive tasks and to process large amounts of data efficiently. Human Perceptual Tools, on the other hand, display the data to the user and allow the user to search for patterns. These systems take advantage of the human capability to perform subtle pattern matching tasks. In fact the two types of data mining are closely related. New rules discovered by users of Human Perceptual Tools can be automated. These new patterns thus then form the basis of a new Automated Intelligent Tool (see figure 1). The primary motivation of this paper is to illustrate this close relationship with a real world case study. The are a number of different approaches to developing Automated Intelligent Tools [17]. These include supervised methods, such as, Neural Networks and Linear Regression Models. They also include unsupervised methods, such as, K-means and self-organising maps. Because the idea of a Human Perceptual Tool is less familiar this paper reviews in more detail work in this area. In particular the review concentrates on the design of multi-sensory displays for virtual environments.
A New Search Model for Liquid-based Cervical Cells Based on Auto-focus Microscope System

Liquid-based cervical cytology test which one of the most important method of detecting cervical lesions has a good effect on diagnosis and prognosis of cervical carcinomas. Speed of manual scanning is slow, furthermore large quantities of works repeated due to the presence of visual fatigue that lead to misdiagnosis. Automatic screening method for pathological cells is proposed based on auto-focus microscope system combined advanced machine vision technology and image processing technology, convenient for doctors find suspicious cells. This way can improve the efficiency of medical workers and avoid misdiagnosis. The key technology of auto-focus microscope system is that scanning automatically of liquid-based cervical cell. An efficient and accurate focus method can find a clear image quickly, which is of great significance to the detection of abnormal cells. In this paper, we proposed a new research mode for auto-focus microscope system, based on the micrograph characteristics of liquid-based cervical cytology. Results show that the approach outperforms with high accuracy and comparable performance.
Differences in Buyer Journey between High- and Low-Value Customers of E-Commerce Business

The knowledge of high-value customers provides the possibility to make decisions ensuring profitability of the company. By analyzing and optimizing a buyer's journey, companies can better understand their customers and optimize marketing costs in the way that will generate a higher return on investment. The primary objective of this paper is to define the current state of multichannel attribution and, based on the literature, study and analyze the data regarding the buyer's journey of high- and low-value customers of selected e-commerce business. To accomplish the main objective of our study, we retrieved and analyzed top conversion paths from Google Merchandise Store, the e-commerce website selling goods branded by Google, with the use of Markov chains and heuristic models. A difference between high- and low-value customers regarding the acquisition by marketing channels before the purchase was found. Moreover, it was found that high-value customers' journeys consist of more interactions compared to those of low-value customers.
A Sentence Similarity Estimation Method Based on Improved Siamese Network

In this paper we employ an improved Siamese neural network to assess the semantic similarity between sentences. Our model implements the function of inputting two sentences to obtain the similarity score. We design our model based on the Siamese network using deep Long Short-Term Memory (LSTM) Network. And we add the special attention mechanism to let the model give different words different attention while modeling sentences. The fully-connected layer is proposed to measure the complex sentence representations. Our results show that the accuracy is better than the baseline in 2016. Furthermore, it is showed that the model has the ability to model the sequence order, distribute reasonable attention and extract meanings of a sentence in different dimensions.
Be Inaccurate but Don't Be Indecisive: How Error Distribution Can Affect User Experience

System accuracy is a crucial factor influencing user experience in intelligent interactive systems. Although accuracy is known to be important, little is known about the role of the system’s error distribution in user experience. In this paper we study, in the context of background music selection for tabletop games, how the error distribution of an intelligent system affects the user’s perceived experience. In particular, we show that supervised learning algorithms that solely optimize for prediction accuracy can make the system “indecisive”. That is, it can make the system’s errors sparsely distributed throughout the game session. We hypothesize that sparsely distributed errors can harm the users’ perceived experience and it is preferable to use a model that is somewhat inaccurate but decisive, than a model that is accurate but often indecisive. In order to test our hypothesis we introduce an ensemble approach with a restrictive voting rule that instead of erring sparsely through time, it errs consistently for a period of time. A user study in which people watched videos of Dungeons and Dragons sessions supports our hypothesis.
Deep neural networks for music tagging

In this thesis, I present my hypothesis, experiment results, and discussion that are related to various aspects of deep neural networks for music tagging. Music tagging is a task to automatically predict the suitable semantic label when music is provided. Generally speaking, the input of music tagging systems can be any entity that constitutes music, e.g., audio content, lyrics, or metadata, but only the audio content is considered in this thesis. My hypothesis is that we can find effective deep learning practices for the task of music tagging task that improves the classification performance. As a computational model to realise a music tagging system, I use deep neural networks. Combined with the research problem, the scope of this thesis is the understanding, interpretation, optimisation, and application of deep neural networks in the context of music tagging systems. The ultimate goal of this thesis is to provide insight that can help to improve deep learning-based music tagging systems. There are many smaller goals in this regard. Since using deep neural networks is a data-driven approach, it is crucial to understand the dataset. Selecting and designing a better architecture is the next topic to discuss. Since the tagging is done with audio input, preprocessing the audio signal becomes one of the important research topics. After building (or training) a music tagging system, finding a suitable way to re-use it for other music information retrieval tasks is a compelling topic, in addition to interpreting the trained system. The evidence presented in the thesis supports that deep neural networks are powerful and credible methods for building a music tagging system.
Concatenated Identical DNN (CI-DNN) to Reduce Noise-Type Dependence in DNN-Based Speech Enhancement

Estimating time-frequency domain masks for speech enhancement using deep learning approaches has recently become a popular field of research. In this paper, we propose a mask-based speech enhancement framework by using concatenated identical deep neural networks (CI-DNNs). The idea is that a single DNN is trained under multiple input and output signal-to-noise power ratio (SNR) conditions, using targets that provide a moderate SNR gain with respect to the input and therefore achieve a balance between speech component quality and noise suppression. We concatenate this single DNN several times without any retraining to provide enough noise attenuation. Simulation results show that our proposed CI-DNN outperforms enhancement methods using classical spectral weighting rules w.r. t. total speech quality and speech intelligibility. Moreover, our approach shows similar or even a little bit better performance with much fewer trainable parameters compared with a noisy-target single DNN approach of the same size. A comparison to the conventional clean-target single DNN approach shows that our proposed CI-DNN is better in speech component quality and much better in residual noise component quality. Most importantly, our new CI-DNN generalized best to an unseen noise type, if compared to the other tested deep learning approaches.
Material classification with laser thermography and machine learning

ABSTRACT Robotic devices that can perform teleoperation work in complex and unstructured environments (e.g. healthcare, education, automotive, telepresence, defence, etc.) rely mainly on visual and/or auditory feedback to interact with a remote operator. There is a vital need for measuring the physical properties of ambient environments when performing precise manipulation tasks over a distance, to improve the quality of performance. In this paper, we propose an approach to combine infrared thermography with machine learning for a fast, accurate way to classify objects of different material. A laser source stimulates the surface of the object while an infrared camera captures its thermal signature, extracts features about these signatures and feeds them into a machine learning-based algorithm for classification. Results demonstrated that a classification accuracy of around 97% can be achieved with majority vote Decision Tree classifier, even in the presence of data from multiple data acquisition sessions.
Filtering Hoax Menggunakan Naive Bayes Classifier

Abstrak - Forum merupakan salah satu tempat berinteraksi dan berpartisipasi dalam komunitas dunia maya yang memiliki minat yang sama. Forum mempunyai topik yang bermacam-macam, salah satunya adalah topik tentang kecantikan. Isi dari forum kecantikan adalah tentang tips dan trik tentang kecantikan. Namun, banyaknya komentar serta opini-opini dari pengguna lain membuat sulitnya mencari fakta dan hoax pada komentar forum. Laporan ini, menjelaskan tentang pendekatan algoritma naive bayes classifier untuk mendeteksi hoax pada komentar forum Female Daily . Tujuan dari penelitian ini adalah untuk mempermudah pengguna dalam mendeteksi hoax pada komentar thread forum. Penelitian ini memuat rancangan, membangun implementasi sistem dan melakukan tahapan data testing . Data training pada sistem ini didapat dari artikel pada website Female Daily . Dilakukan proses text preprocessing terlebih dahulu pada komentar sebelum data dihitung jumlah probabilitasnya dengan menggunakan algoritma naive bayes classifier . Pada tahap testing didapatkan hasil akurasi sebanyak 88% dengan data testing sebanyak 50 komentar.
Cognitive Modeling as a Tool for Improving Runway Safety

Runway incursions are low probability events resulting from complex combinations of cognitive and environmental factors, which can have deadly consequences. However, the development and evaluation of tools to reduce runway incursions are, ironically, hampered by the low incidence of such events. A possible path forward is the use of high-fidelity cognitive models to predict pilot performance under a wide variety of airport conditions and runway circumstances. We describe a fully embodied ACT-R 6.0 cognitive model, named SimPilot, of a pilot taxiing a simulated Boeing 737-800 aircraft. The goals of SimPilot are twofold. The first is automated testing of a new safety devices. The second goal is to show that modeling the multitasking inherent to taxiing in a cognitive plausible manner is an important step in predicting and preventing runway incursions. New tools are continually being developed to increase aviation safety by reducing human error. Since it has been shown that small changes in the design of a system can lead to large changes in human performance, imagine the potential changes that could occur as the result of introducing a new tool. To ensure that the changes introduced are beneficial, extensive testing with subject matter experts (SMEs) is required, but in the aviation domain this is expensive. A potential solution to this problem is simulating SMEs with cognitive models. The type of models that can provide useful simulations are process models that can actually do the task and are based on a cognitive architecture. In this paper we describe a cognitive model to test the Electronic Movable Map (EMM) tool that is intended to improve runway safety. Note the EMM used in this effort is not the real EMM developed for NASA but a functionally equivalent software version developed for testing this methodology. The cognitive model works in conjunction with the Aptima developed Performance Engine (PE), which collects and stores data from a data source and computes relevant performance data. To test the EMM, scenarios were developed to create runway incursions. The scenarios are to be followed by human pilots or model simulated pilots taxiing a Boeing 737 with and without the aid of an EMM.
Evaluating the User Experience and Usability of Virtual Reality Locomotion Techniques: An Empirical Comparison

Virtual reality technology has recently had a revival with new VR-hardware being introduced to the consumer market. This can be considered a turning point in the field as it has now become more accessible and affordable to the public. Since this revival, VR-technology has been subject to multiple HCI studies, where also VR locomotion techniques have been examined. However, a majority of these studies emphasize constructive research problems with a focus on understanding the technical aspects of the interaction. New and novel VR locomotion techniques are constructed without any empirical evaluation of the human aspects to support this work. Without the knowledge of already existing issues with the interaction, there is a research gap in the current field of VR locomotion studies. To fill this research gap, we examined and compared three of the currently prevalent VR locomotion techniques; joystick, teleportation and ”walk in place” with a focus on the user experience and usability. We examined these techniques through an empirical study followed by a Game Experience Questionnaire, questionnaire for the System Usability Scale and semi-structured interviews. This provided useful data to be analyzed and compared to determine which UX aspects were considered important, and how the usability was perceived for each technique. Based on the results, a few issues related to the interaction was identified. Taking these issues into consideration, we propose some design implications to address the current issues. This can be a contribution to improve the problem-solving capacity in the research field of VR to drive the field forward and support new and better constructive work in the future.
Design of State-Feedback Controller for Single-Phase Grid-Connected Siwakoti -H-Inverter with LCL Filter.

Grid-tied transformerless inverters for photovoltaic (PV) systems have attained a prominent share in the distributed power generation applications on both the domestic and utility scale. This arises the need of a transformerless inverter with less hardware components and complexity. This paper presents a state-feedback current controller (SFCC) for a flying-capacitor based transformerless inverter (Siwakoti-H topology). Compared to the conventional proportional-integral (PI) based control, the state-feedback current control provides a better dynamic performance for a single-phase grid-tied converter equipped with an LCL filter. The controller is designed using pole placement to set the dominant behavior of the converter, which actively damps the resonant frequency of the LCL filter. The controller is extended using dc voltage feedforward compensation (DVFC) to maintain the voltage ripple of the flying capacitor within the limits, and an integral state for improved disturbance rejection. Simulation results illustrate the steady-state and dynamic performance of the controller as well as the inherent damping of the LCL filter resonance. Experimental results are presented to verify the steady-state operation of the system.
Fast Radiometric Compensation for Nonlinear Projectors

Radiometric compensation can be accomplished on nonlinearprojector-camera systems through the use of pixelwise lookup ta-bles. Existing methods are both computationally and memory inten-sive. Such methods are impractical to be implemented for currenthigh-end projector technology. In this paper, a novel computation-ally efficient method for nonlinear radiometric compensation of pro-jectors is proposed. The compensation accuracy of the proposedmethod is assessed with the use of a spectroradiometer. Experi-mental results show both the effectiveness of the method and thereduction in compensation time compared to a recent state-of-the-art method.
Brain–Computer Interfaces Handbook Technological and Theoretical Advances

This chapter presents the basic concepts of a brain–computer-interface (BCI) designed for neuromodulation that is based on known theories of memory storage and learning. Initially, an overview is provided of the control signal, the movement-related cortical potential (MRCP), its advantages over other signal modalities, and its neural generators. This is followed by a detailed account of factors that affect the MRCP morphology such as task parameters, shifts in user attention and plasticity, and insights into algorithm design for both detection and classification in an online self-paced BCI. Finally, the applications of this type of associative BCI to more complex tasks such as human gait and in the clinical environment with patients are presented.
An automated ECG signal quality assessment method with supervised learning algorithm

Wearable health has become a striking area in our daily life. Electrocardiogram (ECG) is one of the biomedical signals collected by the wearable or portable devices, which is widely used in heart rate monitoring and cardiac diagnosis. However, automatic ECG signal analysis is difficult in real application because the signals are easy to be contaminated by the noise and artifacts. Thus, the quality of ECG signals is essential for the accurate analysis. The objective of this project is to design a reliable automated ECG signal quality indicator based on the supervised learning algorithm, which intends to estimate the quality of the signals and distinguish them. The methodology of this project is creating a classification model to indicate the quality of ECG signals based on the machine learning algorithm. The model is trained by the extracted features based on the Fourier transform, Wavelet transform, Autocorrelation function and Principal component analysis of ECG signals. Subsequently, the feature selection techniques are proposed to remove the irrelevant and redundant features and then the selected features are fed to classification algorithms. The classifier was then trained and tested on the expert-labeled data from the collected ECG signals. Particularly, we focus on the performance of classifier and use the best training model to predict the quality of new ECG signals.
Optimal Dexterity for a Snake-like Surgical Manipulator using Patient-specific Task-space Constraints in a Computational Design Algorithm

Tendon-driven snake-like arms have been used to create highly dexterous continuum robots so that they can bend around anatomical obstacles to access clinical targets. In this paper, we propose a design algorithm for developing patient-specific surgical continuum manipulators optimized for oriental dexterity constrained by task-space obstacles. The algorithm uses a sampling-based approach to finding the dexterity distribution in the workspace discretized by voxels. The oriental dexterity measured in the region of interest in the task-space formed a fitness function to be optimized through differential evolution. This was implemented in the design of a tendon-driven manipulator for knee arthroscopy. The results showed a feasible design that achieves significantly better dexterity than a rigid tool. This highlights the potential of the proposed method to be used in the process of designing dexterous surgical manipulators in the field.
Positively Scale-Invariant Flatness of ReLU Neural Networks

It was empirically confirmed by Keskar et al.\cite{SharpMinima} that flatter minima generalize better. However, for the popular ReLU network, sharp minimum can also generalize well \cite{SharpMinimacan}. The conclusion demonstrates that the existing definitions of flatness fail to account for the complex geometry of ReLU neural networks because they can't cover the Positively Scale-Invariant (PSI) property of ReLU network. In this paper, we formalize the PSI causes problem of existing definitions of flatness and propose a new description of flatness - \emph{PSI-flatness}. PSI-flatness is defined on the values of basis paths \cite{GSGD} instead of weights. Values of basis paths have been shown to be the PSI-variables and can sufficiently represent the ReLU neural networks which ensure the PSI property of PSI-flatness. Then we study the relation between PSI-flatness and generalization theoretically and empirically. First, we formulate a generalization bound based on PSI-flatness which shows generalization error decreasing with the ratio between the largest basis path value and the smallest basis path value. That is to say, the minimum with balanced values of basis paths will more likely to be flatter and generalize better. Finally. we visualize the PSI-flatness of loss surface around two learned models which indicates the minimum with smaller PSI-flatness can indeed generalize better.