Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the FinTech Jobs-Skills Fit of Financial and Information Technology Professionals: Evidence from LinkedIn

Utilizing LinkedIn resumes of Singapore employees in 2016, this paper not only investigates what kind of work experiences and education training best fit FinTech job roles, but also examines the differences of FinTech job roles between FinTech startups and FinTech incumbent firms. The preliminary results suggest that professionals with financial job title or work experiences in finance industry are more likely to switch to a FinTech job. Meanwhile, the finance or computing major also has a positive effect on switching to a FinTech job. Further analysis shows that FinTech professionals in FinTech startups and FinTech incumbent firms exhibit significant different human capital characteristics, which indicates that startups put more attention to IT workers's leadership and prior startup experience while FinTech incumbent firms focus more on IT techniques and IT educational background when hiring employees. More interestingly, both of the FinTech startups and incumbent firms prefer hiring professionals with either specialized finance or computing background rather than those with both financial and computing backgrounds. Our findings are expected to have important implications for both researchers and practitioners.
Screen-rendered text images recognition using a deep residual network based segmentation-free method

Text images recognition has long been known as a research hotspot of computer vision. However, screen-rendered text image pose great challenges to current character or text recognition methods due to its low resolution and low signal-to-noise ratio properties. In this paper, a segmentation-free method utilizing Residual Network (ResNet) and Recurrent Neural Network (RNN)-Connectionist Temporal Classification (CTC) is proposed to recognize Chinese and English texts in screen-rendered images. Text lines are firstly extracted from screen-rendered images to obtain feature sequences. Then, a bidirectional RNN layer is applied to model the contextual information within feature sequences and predict identification results. Finally, a CTC method is employed to calculate loss and yield the results. The proposed method can achieve the best performance on ORAND-CAR-A dataset, ORAND-CAR-B dataset and a generated dataset with the recognition accuracy of 91.89%, 93.79% and 95.67%, respectively. Moreover, experiments on several real screen-rendered text images also demonstrate the effectiveness of the proposed method.
Transfer Learning for SSVEP-based BCI Using Riemannian Similarities Between Users

Brain-Computer Interfaces (BCI) face a great challenge: how to harness the wide variability of brain signals from a user to another. The most visible problem is the lack of a sound framework to capture the specificity of a user brain waves. A first attempt to leverage this issue is to design user-specific spatial filters, carefully adjusted with a lengthy calibration phase. A second, more recent, opening is the systematic study of brain signals through their covariance, in an appropriate space from a geometric point of view. Riemannian geometry allows to efficiently characterize the variability of inter-subject EEG, even with noisy or scarce data. This contribution is the first attempt for SSVEP-based BCI to make the most of the available data from a user, relying on Riemannian geometry to estimate the similarity with a multi-user dataset. The proposed method is built in the framework of transfer learning and borrows the notion of composite mean to partition the space. This method is evaluated on 12 subjects performing an SSVEP task for the control of an exoskeleton arm and the results show the contribution of Riemannian geometry and of the user-specific composite mean, whereas there is only a few data available for a subject.
Precision pharmacotherapy: psychiatry’s future direction in preventing, diagnosing, and treating mental disorders

Mental disorders account for around one-third of disability worldwide and cause enormous personal and societal burden. Current pharmacotherapies and nonpharmacotherapies do help many patients, but there are still high rates of partial or no response, delayed effect, and unfavorable adverse effects. The current diagnostic taxonomy of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases relies on presenting signs and symptoms, but does not reflect evidence from neurobiological and behavioral systems. However, in the last decades, the understanding of biological mechanisms underlying mental disorders has grown and can be used for the development of precision medicine, that is, to deliver a patient-tailored individual treatment. Precision medicine may incorporate genetic variants contributing to the mental disorder and the response to pharmacotherapies, but also consider gene ¥ environment interactions, blood-based markers, neuropsychological tests, data from electronic health records, early life adversity, stressful life events, and very proximal factors such as lifestyle, nutrition, and sport. Methods such as artificial intelligence and the underlying machine learning and deep learning approaches provide the framework to stratify patients, initiate specific tailored treatments and thus increase response rates, reduce adverse effects and medical errors. In conclusion, precision medicine uses measurable health parameters to identify individuals at risk of a mental disorder, to improve the diagnostic process and to deliver a patient-tailored treatment.
Spindle-Net: CNNs for Monocular Depth Inference with Dilation Kernel Method

Learning depth from a single image is an important issue in computer vision. To solve this problem, encoder-decoder architect is usually employed as a powerful architecture to learn the dense corresponding function. In this work, we propose a symmetrical Spindle network of the encoder-decoder to learn the fine-grained depth. Unlike traditional convolution neural network, we first boost up the feature maps from low-dimension space to a high-dimension space, then extract the features for monocular depth learning. In order to overcome limitation of the computer memory, a single image super-resolution technique is proposed to replace the boosting process by fusing local cues in edge direction. Given the super-resolution images, the monocular depth learning needs more global information than most architectures for pixel-wise predictions. To address this issue, dilation kernel method is proposed to enlarge the receptive field in each layer. For the task of the super-resolution, the proposed method achieves better performance than the state-of-the-art methods. Extensive experiments on the monocular depth inference demonstrate that the Spindle network could achieve comparable performance on the NYU and Make3D datasets, compared with the state-of-the-art algorithms. The proposed method reveals a new perspective to learn the depth from a single image, which shows a promising generality to other pixel-wise prediction problems.
On the Relationship Between Short-Time Objective Intelligibility and Short-Time Spectral-Amplitude Mean-Square Error for Speech Enhancement

The majority of deep neural network (DNN) based speech enhancement algorithms rely on the mean-square error (MSE) criterion of short-time spectral amplitudes (STSA), which has no apparent link to human perception, e.g., speech intelligibility. Short-time objective intelligibility (STOI), a popular state-of-the-art speech intelligibility estimator, on the other hand, relies on linear correlation of speech temporal envelopes. This raises the question if a DNN training criterion based on envelope linear correlation (ELC) can lead to improved speech intelligibility performance of DNN-based speech enhancement algorithms compared to algorithms based on the STSA–MSE criterion. In this paper, we derive that, under certain general conditions, the STSA–MSE and ELC criteria are practically equivalent, and we provide empirical data to support our theoretical results. Furthermore, our experimental findings suggest that the standard STSA minimum-MSE estimator is near optimal, if the objective is to enhance noisy speech in a manner, which is optimal with respect to the STOI speech intelligibility estimator.
Adversarial Generation Of Defensive Trajectories In Basketball Games

In this paper, we present a method to generate realistic trajectories of defensive players in a basketball game based on the ball and the offensive team's movements. We train on the NBA dataset a conditional generative adversarial network that learns spatio-temporal interactions between players' movements. The network consists of two components: a generator that takes as input a latent noise vector and the offensive team's trajectories to generate defensive trajectories; and a discriminator that evaluates the realistic degree of the generated results. Our system allows players and coaches to simulate how the opposing team will react to a newly developed offensive strategy for evaluating its effectiveness. Experimental results demonstrate the feasibility of the proposed algorithm.
An Adaptive Rotor Flux Observer for Variable Flux Machine

Variable flux machine is a promising candidate in high-speed field. The rotor flux of variable flux machine is an important performance index, and it need to be detected accurately in real time. An adaptive rotor flux observer is investigated in this paper. The mathematical model of the observer is established, and the simulation result verifies the good accuracy and following characteristic of it. The influences of parameter fluctuation on the accuracy of adaptive rotor flux observer are analyzed, and the disturbance compensation is introduced to reduce the error rate of rotor flux. The experiment is carried out, and the adaptive rotor flux observer is proved having good precision.
Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models

Modeling disease progression in healthcare administrative databases is complicated by the fact that patients are observed only at irregular intervals when they seek healthcare services. In a longitudinal cohort of 76,888 patients with chronic obstructive pulmonary disease (COPD), we used a continuous-time hidden Markov model with a generalized linear model to model healthcare utilization events. We found that the fitted model provides interpretable results suitable for summarization and hypothesis generation.
Multi-Scale Deep Neural Network for Mitosis Detection in Histological Images

Mitotic figure detection in breast cancer images plays an important role to measure aggressiveness of the cancer tumor. Currently, in clinic environment the pathologist visualized the multiple high power fields (HPFs) on a glass slide under super microscope which is an extremely tedious and time consuming process. Development of the automatic mitotic detection methods is need of time, however it also bears, scale invariance, deficiency of data, improper image staining and sample class unbalanced dilemma. These limitations are however; prohibit the automatic histopathology image analysis to be applied in clinical practice. In this paper, an automatic domain agnostic deep multi-scale fused fully convolutional neural network (MFF-CNN) is presented to detect mitoses in Hematoxylin and eosin (H&E) images. The intended model fuses the multi-level and multi-scale features and context information for accurate mitotic count and in training phase multi-step fine-tuning strategy is used to reduce the over-fitting. Moreover, the training image samples efficiently built by stain normalized the poorly stained (H&E) images and by applying an automatic sample selection strategy. Preliminarily validation on the public MITOS-ATYPIA-14 challenge dataset, demonstrate the efficiency of proposed work. The proposed method achieves better performance in term of detection accuracy with an acceptable detection speed compared to other state-of-the-art designs.
Passenger in vehicle counting method of HOV/HOT system

Automated number of passenger in vehicle counting system has been desired to realize for HOV (High Occupancy Vehicle) and HOT (High Occupancy Toll) use. Under outdoor environment, many complicated factors such as sunlight and vehicle window with darkly tinted cause to deteriorate captured image quality and as a result number counting accuracy remains at low level. In this paper, simple but refined image acquisition scheme and novel number of passenger counting algorithm are proposed. Especially, by incorporating deep neural network for face detection, integrating face detection results over plural frames and calculating confidence value of estimation result, useful and high accuracy has been realized.
Centroid Based Human Annotation for Object Tracking

Visual object tracking is most important in surveillance systems. Trackers should be robust to occlusion while tracking a particular object. There are number of approaches to track objects. This paper introduces a new method that use centroid based human annotation to track moving objects in unseen videos with the help of motion patterns. It does not require object detectors and manual initialization. Centroid based method can handle up to 90% of occlusion. In this paper a library of annotated videos are created and a query video is tested against the library. Matching is done using Greedy algorithm.
Quantifying the Impact of Cyber-Attack Strategies for Control Systems Equipped With an Anomaly Detector

Risk assessment is an inevitable step in the implementation of cost-effective security strategies for control systems. One of the difficulties of risk assessment is to estimate the impact cyber-attacks may have. This paper proposes a framework to estimate the impact of several cyber-attack strategies against a dynamical control system equipped with an anomaly detector. In particular, we consider denial of service, sign alternation, rerouting, replay, false data injection, and bias injection attack strategies. The anomaly detectors we consider are stateless, cumulative sum, and multivariate exponentially weighted moving average detectors. As a measure of the attack impact, we adopt the infinity norm of critical states after a fixed number of time steps. For this measure and the aforementioned anomaly detectors, we prove that the attack impact for all of the attack strategies can be reduced to the problem of solving a set of convex minimization problems. Therefore, the exact value of the attack impact can be obtained easily. We demonstrate how our modeling framework can be used for risk assessment on a numerical example.
Empirical study on two-class image classification

Aim of this research is to study the two-class classifiers. A series of experiments were carried out using the three basic colour models - RGB (red, green, blue), HSV (hue, saturation, value), and the image temperature based model CCT (Correlated color Temperature); and feed forward artificial neural network model, CNN (Convolutional neural network). Combinations of these models were also tested to check which combination or the individuals prove to be relevant for indoor and outdoor scene classification, which has two distinct classes. Among colour models based experiments, the model with the combination of average RGB and average HSV achieved the highest accuracy of 80.95% and the model with the combination of average RGB and average CCT achieved the lowest accuracy of 52.38%. CNN with Adam and Stochastic gradient descent optimizers showed the best performance among the considered eight optimizers.
Characteristic Analysis of a V-Shape Interior Permanent Magnet Synchronous Motor According to Design Parameter

We performed the characteristic analysis of a V-shape interior permanent magnet synchronous motor (IPMSM) using a 2D-finite element method. The inductance of the IPMSM was obtained through a no-load analysis, on-load analysis, and vector diagram for the IPMSM. Additionally, the torque-speed and power-speed characteristics were analyzed. From the characteristic analysis results of the IPMSM, we selected the optimal design for the design specifications.
A Novel Approach for Detecting DGA-based Ransomwares

Nowadays, hybrid cryptosystem ransomware, as well as botnets, utilize domain-generation algorithms to communicate with the command and control (C&C) server to exchange public key and perform their malicious actions. We present an approach for detecting domain-generationalgorithm-based ransomware for the first time. By running instances of this type of ransomware in a test environment, we analyze their behavior, especially in the DNS traffic segment, which leads us to derive several behavioral characteristics. Among these features, we can point to ”random and gibberish characters” in the requested domains; But using this feature is not easy as it can yield a lot of false positives. Our new and innovative approach to solving this challenge is to measure “Frequency of Different Domains Generation” and “Repetition of Same Domains in a Time Interval”. With the help of these criteria, we show that our method is more effective. The proposed approach can be used to detect botnets and other DGA-based malwares. Moreover, our approach detects ransomwares in their early phase of activity (i.e., before encrypting user data). Ultimately, we propose these features as a framework for identifying these ransomwares with high detection accuracy and low false positives rate.
Change-Aware Scheduling for Effectively Updating Linked Open Data Caches

The linked open data (LOD) cloud is a global information space with a wealth of structured facts, which are useful for a wide range of usage scenarios. The LOD cloud handles a large number of requests from applications consuming the data. However, the performance of retrieving data from LOD repositories is one of the major challenge. Overcome with this challenge, we argue that it is advantageous to maintain a local cache for efficient querying and processing. Due to the continuous evolution of the LOD cloud, local copies become outdated. In order to utilize the best resources, improvised scheduling is required to maintain the freshness of the local data cache. In this paper, we have proposed an approach to efficiently capture the changes and update the cache. Our proposed approach, called application-aware change prioritization (AACP), consists of a change metric that quantifies the changes in LOD, and a weight function that assigns importance to recent changes. We have also proposed a mechanism to update policies, called preference-aware source update (PASU), which incorporates the previous estimation of changes and establishes when the local data cache needs to be updated. In the experimental evaluation, several state-of-the-art strategies are compared against the proposed approach. The performance of each policy is measured by computing the precision and recall between the local data cache update using the policy under consideration and the data source, which is the ground truth. Both cases of a single update and iterative update are evaluated in this study. The proposed approach is reported to outperform all the other policies by achieving an F1-score of 88% and effectivity of 93.5%.
TELIL: A Trilateration and Edge Learning based Indoor Localization Technique for Emergency Scenarios

When deploying localization techniques using wireless devices with only less amount of system resources, indoor WiFi localization is a difficult task. GPS (Global Positioning System) can provide a fairly accurate position of the user, but it is usually not used indoors because of the degradation of signals due to the structure of the building. Due to the unsteady nature of Received Signal Strength Indicator (RSSI), accurate results are hard to generate, in spite of the usage of various alternative WiFi localization techniques. We have attempted to improve the accuracy of localizing the user by implementing wall detection and pixel calculation to trilateration technique in smartphones. The users having WiFi enabled devices like a smartphone can locate their position and request for help in case of emergency with the implementation of WiFi localization on mobile devices. This study proves that the selection of access points used for generating the position of the user substantially affects the accuracy.
Stability and $\boldsymbol{L_{1}}$ -Gain Analysis for Positive Takagi–Sugeno Fuzzy Systems With Impulse

This paper is concerned with the stability analysis and <inline-formula><tex-math notation="LaTeX">$L_{1}$</tex-math> </inline-formula>-gain characterization for positive Takagi–Sugeno fuzzy systems subject to impulse effects. The main tool utilized is the construction of an impulse-time-dependent discretized copositive Lyapunov function where the impulse interval partitioning idea is exploited to derive less conservative results. For the disturbance-free case, stability criteria are developed to guarantee global exponential stability of the concerned systems under ranged and constant dwell-time constraints. The analysis of the influence of the discretizing parameter on the reduction of conservativeness is performed. We further employ the proposed impulse-time-dependent discretized copositive Lyapunov function to analyze the <inline-formula><tex-math notation="LaTeX">$L_{1}$</tex-math></inline-formula>-gain performance for the case where external disturbances are considered. By performing a grid search for a scalar in an interval, all the proposed criteria can be checked via linear programming. Finally, numerical simulations are discussed to illustrate the effectiveness of the theoretical results.
Exploring Computing at the Edge: A Multi-Interface System Architecture Enabled Mobile Device Cloud

Today, mobile applications advancements have overcome limited device capabilities by offloading to costly public cloud. As the edge computing paradigm began to take precedence, a mobile device cloud (MDC) formed at the edge based on idle intra-device resources emerged. This is a result of a customized user-centric composition service request for a time-bound application. Herein, devices volunteer their intra-device resources for producing a compute environment in turn satisfying the needs of the consumer. Now, with the growth of device technology and the available interfaces for accessing multiple radio technologies, a new transport layer protocol called Multipath TCP was introduced in literature. This protocol enables multiple sub-flows to join for transmitting data simultaneously. However, in scenarios like formation of device clouds, there are issues pertaining to sub-flows that are involved in a device cloud composition. One such issue is the management of sub-flow buffer. As each of these sub-flows have their own respective buffering and characteristic delays, it leads to sub-optimal performance in term of buffer occupancy. Thereby, degrading the quality of the device cloud composition. To this end, we propose an OS side architecture that plays a crucial role in managing the traffic coming from different flows. We model an agent that works conservatively satisfying Kleinrock's law and show a proof of concept experiment