Miracle-dz/UniFAR-FTUs huggingface dataset:
Dividend payout and dual-class companies based on big data analysis

With the advent of the era of large data, facing massive data, we can apply statistical methods to mining the value hidden in the data.My study shows that there is a positive and significant relation between dividend payout and dual-class shares, suggesting that dual-class firms are more likely to pay dividend than single class firms. The finding is consistent with agency theory since agency theory implies that dividend can be used to mitigate the agency conflicts between managers and minority shareholders. I argue that this is because analyst coverage can effectively discipline the behaviors of managers so that analyst coverage can be used as alternative agency control mechanism of dividend to alleviate agency conflicts between managers and minority shareholders.
Phase Transition Device for Phase Storing

Nano-oscillators based on phase transitions materials (PTM) are being explored for the implementation of different non-conventional computing paradigms. This article describes the capability of such autonomous non-linear oscillators to store phase-encoded information. A latch based in sub-harmonic injection locking using an oscillator composed of a PTM device and a transistor is described. Resistive coupling is used to inject both a required synchronization signal and the input to be stored. Operation of the proposed latch implementation, the embedding of functionality into the latch and its application to frequency division are illustrated and validated by simulation.
Transcoding-Enabled Edge Caching and Delivery for Tile-Based Adaptive 360-Degree Video Streaming

The high-definition and panorama characteristics of 360-degree video make its transmission in existing networks more challenging. To alleviate this problem, this paper leverages transcoding-enabled edge caching in tile-granularity and makes joint caching, transcoding and delivery decisions among multiple edge servers for adaptive 360-degree video streaming. We formulate the joint caching, transcoding and delivery decision-making problem as a multivariate nonlinear integer programming problem, aiming at minimizing the aggregate network operational cost and balancing the server load, subject to storage and computation capacity constraints of the edge servers. Through decomposition and relaxation approach, the original intractable optimization problem is solved by an effective iterative algorithm. Finally, simulation results show that the proposed iterative algorithm outperforms the existing schemes in terms of resource utilization, cost reduction and server coordination.
Design of Right Ventricle PPG Signal Generator

Photoplethsmograph(PPG) is a low-cost and noninvasive optical detecting technique. Because it provides rich physiological information, PPG can help some of the rate responsive pacemakers determine the optimal pacing rate, improving patient's cardiac chronotropic function. In order to facilitate the production of the pacemakers based on right ventricle PPG signal, this paper designed a kind of PPG signal generator. In this paper, based on the morphological characteristics of PPG wave and considering the influence of respiration and heart rate variability(HRV), the mathematical model of right ventricle PPG signal is established, and then the corresponding hardware circuit and software program are designed to realize a signal generator which can simulate the pulsating change of right ventricle PPG signal in real time. The signal generator can be used as the source of arbitrary PPG signal, and can replace some animal experiments and facilitate the design and research of the rate responsive pacemakers based on right ventricular PPG signal.
Learning-Based Index-of-Maximum Hashing for Touch-Stroke Template Protection

Touch-stroke dynamics is an emerging biometrics which captures a user's unique way of swiping a set of “strokes” on the mobile devices, and uses them as a matching template. Unexceptional to other biometrics, this template is also vulnerable to adversary attacks that may lead to privacy invasion and impersonation risk. One of the ideal ways to protect the biometric template is through the method of cancelable biometrics. On top of distorting the original template, it makes the template revocable as well. In this work, we propose an improved cancelable touch-stroke template method based on the Index-of-Maximum (IoM) hashing [10], and termed as learning based IoM (LIoM) hashing throughout this paper. Unlike vanilla data-agnostic IoM hashing, data-driven based LIoM hashing utilizes the supervising learning mechanism to generate a more discriminative and compact cancelable touch-stroke template. The proposed method achieved better performance than original template on Frank touch-stroke dynamic database with same feature length as well as satisfies three template protection design criteria, namely irreversibility, revocability and unlinkability.
Performance Comparison of Machine Learning Techniques on Diabetes Disease Detection

In this paper, the performance comparison of the machine learning techniques on diabetes disease detection is carried out. Diabetes disease attracts great attention in the machine learning community. Because diabetes is a chronic disease and needs to be detected at an early stage in order to deal with the correct medication. A series of machine learning techniques are used in the work such as Decision Trees (DT), Logistic Regressions (LR), Discriminant Analysis (DA), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) and ensemble learners. MATLAB software is considered. Especially, the MATLAB Classification Learner Tool (MCLT) is used. The MCLT covers the mentioned machine learning techniques and their various variants. Thus, a totally 24 classifiers are used in the presented work. The results are evaluated according to the 10-fold cross-validation criteria and average classification accuracy is used for performance measure. The obtained average accuracy scores are in the range of 65.5% and 77.9%. The best accuracy score 77.9% is produced by the LR method and the worst one 65.5% is produced by the Coarse Gaussian SVM technique.
A Bagging-based ensemble method for recommendations under uncertain rating data

Matrix factorization (MF) is one of the most-used techniques to build recommender systems. However, in practical use, the existence of noise in the training set brings some uncertainty, degrading the performance of MF approaches. In this paper, we propose a Bagging-based MF framework, an ensemble method of using multiple MF-based models to improve the stability and accuracy. Specifically, our framework first rebuilds new training sets by resampling on the original ratings, then takes advantage of the sets to train MF models and finally combines the predictions from the models in a ensemble way. The experiment results on real data show that our framework can achieve some performance improvement when having noise samples.
A Low-Power Embedded System for Real-Time sEMG based Event-Driven Gesture Recognition

Latency and power consumption management are present-day hot topics for wearable devices and IoT applications. This paper presents the implementation of a low power system for hand movement recognition, based on surface ElectroMyo-Graphic (sEMG) signals, suitable for human-machine interface. Every time the sEMG signal crosses a predefined threshold an event is generated, implementing the Average Threshold Crossing (ATC) technique directly on-boards on the subject arm. Resulting quasi-digital signals, averaged over a fixed time window, are sent to an ARM Cortex-M4F processor which implements a fully-connected Neural Network (NN) able to recognize six different gestures from only three input channels. Dataset creation has involved 25 healthy people, each one performing five movements within five repeated sessions. The NN has been trained using the per-subject holdout validation method, obtaining an accuracy of 96.34%. With a maximum latency of 8.5 ms and an average power consumption of only 0.8 mW at full recognition rate, the proposed NN implementation shows promising results.
Harmonizing Heterogeneous Diagnostic Data of a Vehicle Fleet for Data-Driven Analytics

Data-driven technologies, such as predictive maintenance, become increasingly important to today's automotive industry due to advancements of connected cars and Over-the-Air technologies. A data source that has barely been used in the literature so far is diagnostic data, which is obtained by sending requests to the electronic control units of a vehicle. Diagnostic data can be collected cost-effectively and is already available on a large scale to car manufacturers today. However, the use of diagnostic data is associated with some difficulties. The set of measured variables differs greatly between different vehicles of the same type due to different configurations and therefore differences in the electronic control units. In this contribution, we show how diagnostic data can be harmonized for the use of data-driven modeling. An heuristic three-step procedure is introduced to identify similar measured variables. Finally, our approach is verified on a synthetic data set. Future data-driven technologies are able to use larger and more cost-efficient data sets this way.
A Conformal Band-Notched Ultrawideband Antenna With Monopole-Like Radiation Characteristics

A conformal, planar, and low-profile ultrawideband antenna with monopole-like radiation and band-notched characteristics is presented. A circular patch shorted to the ground is combined with two rectangular slots and two concentric rings, to achieve an ultrawide bandwidth from 3.8 to 8.3 GHz with a single rejection band from 5 to 6 GHz. The monopole-like radiation features, generated by combining TM<inline-formula><tex-math notation="LaTeX">$_{01}$</tex-math></inline-formula> and TM<inline-formula><tex-math notation="LaTeX">$_{02}$</tex-math></inline-formula> operating modes of a circular patch antenna, are maintained over the entire operating bandwidth. The antenna has only 0.046<inline-formula><tex-math notation="LaTeX">$\lambda _{o}$</tex-math></inline-formula> height at 3.8 GHz and is realized using polydimethylsiloxane conductive fabric composite technology making it highly flexible and physically robust. This was validated through severe bending tests with various curvatures.
A Palmprint ROI Segmentation Algorithm for Eliminating Wrist Interference

Palmprint region of interest (ROI) segmentation is an important step in palmprint recognition. Positioning successful ROI has a greater impact on subsequent feature extraction and matching. At present, the palmprint segmentation method mainly has problems in that the positioning points are not easy to determine, and the poor positioning stability for different images of the same person. Therefore, based on the existing algorithm that based on regulate angle, a palmprint ROI segmentation algorithm that eliminating wrist interference is proposed, which improving the method of locating finger valley points and selecting the little finger side edge line. A method of wrist segmentation is designed to effectively avoid the influence of the wrist on the positioning of the feature points. The experimental results finally show that the proposed algorithm is more accurate for palmprint ROI segmentation and has better positioning stability. The ROI extraction success rate can reach 99.80%.
A Deep Convolutional-Recurrent Neural Network for Freezing of Gait Detection in Patients with Parkinson's Disease

Freezing of gait (FOG) is a common symptom in Parkinson's disease (PD) patients that may result in falls and injuries. The accurate detection of FOG events is important for the clinical on-demand cueing and the treatment of patients. To realize FOG detection with high performance, we proposed methods to build FOG detection models using deep convolutional and recurrent networks. The acceleration signals included 237 FOG events captured from 10 PD patients who performed designed walking tasks with an accelerometer placed on their lower back. Raw acceleration patterns and spectrograms were extracted from successive gait cycles and used as input for FOG detection model training. The model performances were compared to evaluate different domain contributions in FOG detection model training. Deep convolutional layers combined with recurrent layers (DeepCNN-LSTM) were offline trained and then employed to detect FOG or non-FOG events. Using the acceleration spectrogram as input, we achieved an average FOG detection accuracy, sensitivity, specificity of 94.3%, 96.1%, and 95.5%, respectively, which was 2.1% higher in accuracy compared to using raw acceleration patterns as input. The proposed methods based on acceleration signals using the DeepCNN-LSTM classifier are able to detect FOG events with high accuracy, which could be transplanted to the wearable device for non-pharmacologic therapy for PD patients.
Classification of Tumor Gene Expression Data Based on Manifold Learning and Gaussian Process

Tumor gene expression data usually presents high-dimensional and imbalance characteristics. Effective feature dimension reduction has far-reaching significance for its classification. However, the real distribution of data in high-dimensional space is rarely considered in traditional feature extraction methods, which would lead to the distortion of the original data and the influence on classification accuracy of the minority class. A new method combining manifold learning and Gaussian process classification is proposed for gene expression data classification in this paper. Isometric Feature Mapping is used to reduce the dimension of target data and extract manifold features, then Gaussian process is trained to classify the tumor gene expression data. The experimental results on three real datasets show that the proposed method provides higher recognition accuracy, meanwhile, it is efficient and feasible for imbalanced data.
Digital-Signal-Processor Realization of Izhikevich Neural Network for Real-Time Interaction with Electrophysiology Experiments

The paper presents a realization on a digital signal processor (DSP) of an Izhikevich Neural Network operating with biologically plausible real-time constants. The paper demonstrates the real-time realization of different neuron behavioral modes, i.e., regular-spiking, chattering, bursting, and fast-spiking under proper parametrization. Real-time spike-timing-dependent-plasticity has also been embedded in the neural network realization. The paper studies the maximum array size that can be implemented on a TMS320C6455 microprocessor to be able to reproduce correctly the real-time dynamics of the different behaviors. The TMS320C6454, from the same DSP family as TMS320C6455, is embedded in a commercial microelectrode array system for real-time interaction with biological neural cell cultures. As demonstrator, a simple classification of two binary patterns has been implemented. Upon learning activation, the system robustly unsupervisely learns to differentiate the two patterns.
Energy-Quality Scalable Analog-to-Digital Conversion and Machine Learning Engine in a 51.9 nJ/frame Voice Activity Detector

In this work, voice activity detection (VAD) system with dynamic energy-quality (EQ) scalability is presented. EQ scalability is enabled through the insertion of multiple knobs at different levels of the signal chain, starting from analog-digital conversion and ending at the classification stage. Such knobs are co-optimized at runtime to achieve a given quality target with minimal energy. Such co-optimization is also shown to improve the fit of the machine learning algorithm, allowing for more graceful quality degradation. The proposed system, fabricated on a 28nm test chip, classifies at 81.2% accuracy while consuming 51.9 nJ/frame in a 10dB noise context. Scaling up energy consumption by 3.5x improves accuracy by 5.2%.
Path following controller for autonomous vehicles

In recent years vehicles with high level of automation are becoming more popular which is expected to continue in the future. Advanced driving assistance systems are increasingly taking control of the vehicle, starting with the support of the driving task. Automated or highly automated vehicles are expected to follow a planned road safely. In this paper, the authors aim to improve the accuracy of path following by developing a new control strategy. The controller includes both pure pursuit and Stanley methods, the operation of the controller is based on the geometry of the vehicle and the path; the key is the proper weighting between two controllers. The performance of the combined path following controller was measured on a demonstration vehicle.
Video Captioning Based on Joint Image–Audio Deep Learning Techniques

With the advancement in technology, deep learning has been widely used for various multimedia applications. Herein, we utilized this technology to video captioning. The proposed system uses different neural networks to extract features from image, audio, and semantic signals. Image and audio features are concatenated before being fed into a long short-term memory (LSTM) for initialization. The joint audio-image features help the entire semantics to form a network with better performance.A bilingual evaluation understudy algorithm (BLEU)—an automatic speech scoring mechanism—was used to score sentences. We considered the length of the word group (one word to four words); with the increase of all BLEU scores by more than 1%, the CIDEr-D score increased by 2.27%, and the METEOR and ROUGE-L scores increased by 0.2% and 0.7%, respectively. The improvement is highly significant.
Experimental Analysis of Auditory Distance Perception in Vertical Axis

In order to explore the law of vertical axial distance perception of human body, the listening experiment was carried out in the anechoic room. Because simulating sound source in the vertical axis was so difficult, the experiment was carried out in the lateral and supine conditions, and the experiment data were compared and analyzed. The results show that regardless of pure tone, narrow band noise and white noise signal, the changing trend of vertical axial distance perception of human body is consistent. Meanwhile, a verifying experiment in erect state has been carried out, and the results are similar, added with a certain degree of compression compared with those of lateral state.
Machine-Learning Algorithms for Mapping Debris-Covered Glaciers: The Hunza Basin Case Study

Global warming is one of the main challenges of recent times. The glaciers are melting faster than expected which has resulted in global mean sea level rise and increased the risk of floods. The development of modern remote sensing technology has made it possible to obtain images more frequently than ever before. On the other side, the availability of high-performance computing hardware and processing techniques have made it possible to provide a cost-effective solution to monitor the temporal changes of glaciers at a large scale. In this study, supervised machine learning methods are investigated for automatic classification of glacier covers from multi-temporal Sentinel-2 imagery using texture, topographic, and spectral data. Three most commonly used supervised machine learning techniques were investigated: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The proposed method was employed on the data obtained from Passu watershed in Hunza Basin located along the Hunza river in Pakistan. Three main classes were considered: glaciers, debris-covered glaciers and non-glaciated areas. The data was split into training (70%) and testing datasets (30%). Finally, an area-based accuracy assessment was performed by comparing the results obtained for each classifier with the reference data. Experiments showed that the results produced for all classifiers were highly accurate and visually more consistent with the depiction of glacier cover types. For all experiments, random forest performed the best (Kappa = 0.95, f-measure = 95.06%) on all three classes compared to ANN (Kappa = 0.92, f-measure = 92.05) and SVM (Kappa = 0.89, f-measure = 91.86% on average). The high classification accuracy obtained to distinguished debris-covered glaciers using our approach will be useful to determine the actual available water resources which can be further helpful for hazard and water resource management.
Single-Scale Workpiece Defect Detection Based on Deep Learning

As Mask R-CNN network can effectively detect objects, it can be used to detect product defects on the images from the production line in more intelligent manner. However, in order to speed up and enhance the recall rate for specific defects, there are many parts in the architecture can be optimized and improved. In this paper, the anchor box with specific scales are designed to adapt to specific defects to avoid redundant multiple anchors, and the shared feature extraction layers of Mask R-CNN is improved for more efficient process. The improved network is applied to detection of gears with tiny defects. Experimental results demonstrate that the improved networks aimed at specific defects of gears achieve a notable improvement in terms of both recall index and detection speed. This method can be applied to other specific defects such as button scratches, printed circuit defect,etc.
Extracellular Electrical Stimulation-based in Vitro Neuroscience: A Minireview of Methods and a Paradigm Shift Proposal

Biological neuronal cells communicate using neurochemistry and electrical signals. Electrical stimulation (ES) is utilized to study neuronal cells and networks. Currently, ES is applied and responses observed in an open-loop fashion, which does not resemble natural network I/O. We hypothesize that real-time closed-loop full-duplex (simultaneous two-way) paradigms could provide deeper insight in natural neuronal networks, helping to understand our brains and to control neuronal network states to cure diseases. We present a minireview of ES-based extracellular in vitro neuroscience, our first long-term closed-loop ES experiment results as the proof-of-feasibility of the method, and our paradigm-shifting proposal of dialogical bio-ICT paradigms.
Implementation of Enterprise Resource Planning (ERP) using Integrated Model of Extended Technology Acceptance Model (TAM) 2: Case Study of PT. Toyota Astra Motor

ERP (Enterprise Resource Planning) is an integrated information system that can accommodate information system needs specifically for different departments in a company. The use of ERP makes all systems within a company into a system that is integrated with one database so that some departments become easier in sharing data and communication. PT. Toyota Astra Motor is a company that has implemented an ERP system since 2000, but the success rate is still around 75% and 25% failure. Therefore, this study was designed to analyze what are the determinants of success that can be used as reference materials to make PT. Toyota Astra Motor is a better company and can improve the sales and customer service system. To analyze the determinants of the success of ERP implementation at PT. Toyota Astra Motor researchers used the Extended Technology Acceptance Model (TAM) 2 model to integrate the combined Technology Acceptance Model and the IS success model. This study uses quantitative design with hypotheses and analyzes using IBM AMOS software. Based on the results of the study using the Extended Technology Acceptance Model (TAM) 2 to find out success factors it is known that the influential variables are job relevance, compatibility, perceived ease of use, perceived usefulness, function, internal support and intention to use, which means if the variable can be used as a determining factor for the success of ERP implementation by PT. Toyota Astra Motor.