Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Intelligent Drunk Driving Detection System Based on Internet of Things

In recent years, with the rapid development of China’s economy and the continuous improvement of people’s living standards, the number of motor vehicles and the number of drivers in the country have grown rapidly. Due to the increase in the number of vehicles and the number of motorists, the traffic accident rate is increasing, causing serious economic losses to society. According to the traffic accident statistics of the Ministry of Communications of China in 2009, more than 300,000 car accidents occurred in the year, most of which were caused by drunk driving. Therefore, this paper proposes a design scheme based on the Internet of Things-based vehicle alcohol detection system. The system uses STM8S003F3 single-chip microcomputer as the main control chip of the system, combined with alcohol sensor MQ-3 circuit, LCD1602 liquid crystal display circuit, buzzer alarm circuit and button circuit to form a complete alcohol detection module hardware system. The main functions of the system are as follows: the alcohol sensor in the car detects the driver’s alcohol concentration value, and displays the value on the LCD screen. The buzzer alarm is exceeded and the information is sent to the traffic police department and the family’s mobile phone through the GPRS module. The system can effectively make up for the shortcomings of traffic police detection, which has certain research significance.
Homogenization of biased convolution type operators

This paper deals with homogenization of parabolic problems for integral convolution type operators with a non-symmetric jump kernel in a periodic elliptic medium. It is shown that the homogenization result holds in moving coordinates. We determine the corresponding effective velocity and prove that the limit operator is a second order parabolic operator with constant coefficients. We also consider the behaviour of the effective velocity in the case of small antisymmetric perturbations of a symmetric kernel.
Parallax-Tolerant Image Stitching with Optimal Homography

Artificial ghosting is a serious problem in image stitching with wide-baseline, which makes it difficult to achieve seamless stitching. A robust parallax-tolerant image stitching method combined with optimal homography matrix selection and seam-cutting processing is proposed. Optimal homography matrix is obtained by analysing feature point distribution and minimizing the registration error. And then, the aligned area is enhanced by the optimal homography matrix, which can facilitate the seam searching. To realize seamless stitching, the luminance difference and the geometrical structure are used in seam-cutting processing. At last, the experimental results show that the artificial ghosting is solved quickly and effectively in the images stitching with large parallax.
Control of Chaotic Maps by Optimized Periodic Inputs

It has been established that chaotic iterated maps can be transferred to a periodic motion by application of periodic signals. As a next step, we investigate the optimization of such signals with respect to a small norm. This method employs a cost function on the space of periodic inputs. By means of a direct optimization search in this space, the approach stabilizes periodicity with small forces. The results for increasing driving periods are compared.
Reducing DRAM Refresh Rate Using Retention Time Aware Universal Hashing Redundancy Repair

As the device capacity of Dynamic Random Access Memory (DRAM) increases, refresh operation becomes a significant contributory factor toward total power consumption and memory throughput of the device. To reduce the problems associated with the refresh operation, a multi-rate refresh technique that changes the refresh period based on the retention time of DRAM cells has been proposed. Unfortunately, the multi-rate refresh technique has a scalability issue, because the additional storage and logic overhead on a memory controller increases as the device capacity increases. In this article, we propose a novel redundancy repair technique to increase the refresh period of DRAM by using a universal hashing technique. Our redundancy repair technique efficiently repairs both hard faults, which occur during the manufacturing process, and weak cells that have short retention time using the remaining spare elements after the process. Also, our technique solves the Variable Retention Time problem by repairing weak cells at boot time by exploiting the Built-in self-repair (BISR) technique and Error Correction Code. Our technique outperforms a conventional BISR redundancy repair with very little hardware overhead, and ensure reliability with more extended refresh period in the entire system. In particular, our experimental results show that our BISR technique achieves 100% repair rate at a 384ms refresh period in 1.0e-6 hard fault BER configuration, and reduces the refresh energy consumption by 83.9% compared to the 64ms refresh and 12% compared to the conventional multi-rate refresh technique for the state-of-the-art 4Gb device.
Attention-Augmented Multilinear Networks For Time-series Classification

Dat Thanh Tran: ATTENTION-AUGMENTED MULTILINEAR NETWORKS FOR TIME-SERIES CLASSIFICATION Master of Science Thesis Tampere University Master‘s Degree Programme in Information Technology Major: Machine Learning and Data Engineering January 2019 Time-series analysis has long been a challenging problem and has been studied extensively over the past decades. In fact, several phenomena possess the dynamic nature of time, with related data collected and expressed in the form of time-series data. While the current development of hardware and software infrastructures provides us a tremendous amount of data to build and validate our models, the noisy and stochastic nature observed in many data modalities still prevent us from having definite solutions. This is especially true for chaotic systems such as the stock market in which the involvement of actors with different goals in the feedback loop leads to complex behaviors. In this thesis, the author proposes a neural network layer design that incorporates the intuitive idea of bilinear mapping to multivariate time-series, as well as an attention module that enables the layer to automatically calculate and focus on important temporal instances. The contribution of the new design is two-fold. Firstly, the proposed layer is highly interpretable thanks to its ability to quantify the contribution of different instances encoding temporal information. In the post-training and inference phase, the attention quantities can be visualized to highlight the time instances of interest, opening up the opportunity for further analysis. Secondly, the new layer design requires both lower memory and fewer computations compared to the popular attention-based Long-ShortTerm-Memory design, which is the state-of-the-art solution. In order to validate the proposed architecture, the author has conducted experiments on the problem of stock mid-price movement prediction using information available in Limit Order Book. In the algorithmic trading regime, an automated forecasting system is required to be both accurate and efficient since the market operates on nanosecond resolution. Our experimental results demonstrate that the proposed architecture establishes new state-of-the-art forecasting performances in the problem of interest while running much faster than previously proposed solutions.
Portfolio Optimization by Means of Meta Heuristic Algorithms

Investment decision making is one of the key issues in financial management. Selecting the appropriate tools and techniques that can make optimal portfolio is one of the main objectives of the investment world. This study tries to optimize the decision making in stock selection or the optimization of the portfolio by means of the artificial colony of honey bee algorithm. To determine the effectiveness of the algorithm, its sharp criteria was calculated and compared with the portfolio made up of genes and ant colony algorithms. The sample consisted of active firms listed on the Tehran Stock Exchange from 2005 to 2015. The sample selected by the systematic removal method. The findings show that artificial bee colony algorithm functions better than the genetic and ant colony algorithms in terms of portfolio formation
Machine Learning Techniques for Detecting False Signatures

Deciding whether a handwritten signature is legit or it has been falsified is a very complex task. Several methods have been tried out by the graphology experts in order to detect such fraud. However, it is obvious that it is very hard to perform such a classification. In this paper we investigate the possibility to use some supervised learning techniques in order to build models capable to accurately perform such an analysis. The results reported during the testing phase of the obtained model are encouraging for further work.
Neuro Sync: Inventing Data-enhanced communication in Russia

ABSTRACT Nowadays, almost any experience can be digitalised, copied and reproduced; it is nothing but data. In this article, we discuss a new ethics of technology- and data-enhanced communication. We explore neurotechnology, one of the final frontiers of digitalisation, as a possible new way to share inner processes happening within the brain, to communicate with each other. Using real-time data, we create a shared neurofeedback environment that helps artists better understand each other in an extremely new model of data-driven communication.
Uplink cooperative diversity using power‐domain nonorthogonal multiple access

In this paper, an uplink scheme that exploits cooperative diversity using power domain nonorthogonal multiple access (NOMA), termed UCD‐NOMA, with successive interference cancelation receivers is proposed. A two‐user case is considered, where both the NOMA users can communicate with the base station directly and indirectly via a half‐duplex decode‐and‐forward relay. Under perfect and imperfect successive interference cancelation assumptions, the ergodic sum capacity, outage probability, and outage sum capacity of the considered protocol are analyzed over the Rayleigh fading channel. As a protocol with which to compare the proposed UCD‐NOMA protocol, an uplink cooperative network using a conventional multiple access technique (termed UCD‐OMA) is also considered and analyzed. The performance excellence of UCD‐NOMA as compared to UCD‐OMA is clarified through simulations and theoretical analyses.
Demolished building detection from aerial imagery using deep learning

Abstract. In this paper, we present a novel approach for demolished building detection using bi-temporal aerial images and building boundary polygon data. The building boundary polygon data can enable the proposed method to distinguish buildings from non-buildings. Moreover, it can enable the exclusion of non-building changes such as those caused by changes in tree cover, roads, and vegetation. The results of demolished building detection can be achieved by using the building-base. The proposed method classifies each building as demolished or undemolished. The architectures, which based on U-Net and VGG19, are implemented for realizing automatic demolished building detection. The result suggested that U-Net is a useful architecture for image classification problems as well as for semantic segmentation tasks. In order to verify the effectiveness of proposed method, the detection performance is evaluated using images of an entire city. The results suggest that the proposed method can accurately detect demolished buildings with a low mis-detection rate and low over-detection rate.
Computation Offloading Toward Edge Computing

We are living in a world where massive end devices perform computing everywhere and everyday. However, these devices are constrained by the battery and computational resources. With the increasing number of intelligent applications (e.g., augmented reality and face recognition) that require much more computational power, they shift to perform computation offloading to the cloud, known as mobile cloud computing (MCC). Unfortunately, the cloud is usually far away from end devices, leading to a high latency as well as the bad quality of experience (QoE) for latency-sensitive applications. In this context, the emergence of edge computing is no coincidence. Edge computing extends the cloud to the edge of the network, close to end users, bringing ultra-low latency and high bandwidth. Consequently, there is a trend of computation offloading toward edge computing. In this paper, we provide a comprehensive perspective on this trend. First, we give an insight into the architecture refactoring in edge computing. Based on that insight, this paper reviews the state-of-the-art research on computation offloading in terms of application partitioning, task allocation, resource management, and distributed execution, with highlighting features for edge computing. Then, we illustrate some disruptive application scenarios that we envision as critical drivers for the flourish of edge computing, such as real-time video analytics, smart “things” (e.g., smart city and smart home), vehicle applications, and cloud gaming. Finally, we discuss the opportunities and future research directions.
Strong Normalization by Type-Directed Partial Evaluation and Run-Time Code Generation (Preliminary Version)

We investigate the synergy between type-directed partial evaluation and run-time code generation for the Caml dialect of ML. Type-directed partial evaluation maps simply typed, closed Caml values to a representation of their long beta-eta-normal form. Caml uses a virtual machine and has the capability to load byte code at run time. Representing the long beta-eta-normal forms as byte code gives us the ability to strongly normalize higher-order values (i.e., weak head normal forms in ML), to compile the resulting strong normal forms into byte code, and to load this byte code all in one go, at run time. We conclude this note with a preview of our current work on scaling up strong normalization by run-time code generation to the Caml module language.
Cyclic Signals and Systems in Power Line Communications

This paper provides a comprehensive discussion about the characteristics of power line communication (PLC) channels related to its synchronization with the mains period. This fact constitutes an interesting and distinguishing feature of these kind of channels. Under the mains voltage presence, the behavior of some loads connected to the power network allows representing the transmission medium as a linear periodically time varying (LPTV) system. Some components in the received noise exhibit periodicity as well, in terms of impulsive waveforms synchronized with the mains period and other cyclostationary (CS) signals. This fact has been taken into account in the design of PLC systems, for instance by defining medium access control (MAC) frames synchronized with the mains period and by adapting the modulation to the cyclic signal-to-noise ratio (SNR) to improve the system performance. Hence, in PLC, it is of particular interest to describe the general relations of LPTV systems and CS signals when both have a common period, which is the purpose of this work. It is not aimed at providing new mathematical tools for the analysis of these cyclic signals and systems, which have been extensively developed over the last five decades, but to serve the readers of the PLC community more accessible mathematical relations that can be applied to the practical problems they have to face. A review of recent works dealing with the cyclic properties of PLC channels and transmission techniques aware of these strategies is included as well.
Convergence of the fractional Yamabe flow for a class of initial data

This work is a follow-up on the work of the second author with P. Daskalopoulos and J.L. V\'{a}zquez. In this latter work, we introduced the Yamabe flow associated to the so-called fractional curvature and prove some existence result of mild (semi-group) solutions. In the present work, we continue this study by proving that for some class of data one can prove actually convergence of the flow in a more general context. We build on the approach simplified in the book of M. Struwe.
Yellowfin Tuna in the Market for High Value Agricultural Products: Competitive Capability in Dominica

In the last decade, there have been significant changes in the economic focus in Dominica, particularly with respect to changes in trade access for key agricultural commodities. This study examined opportunities for exporting yellowfin tuna (Thunnus albacares) from Dominica. The fisheries sector is identified as an under-exploited resource of Dominica, having significant capacity for foreign exchange earnings, evidenced by an increasing trend in yellowfin tuna harvest, and attractive prices on both domestic and international markets for this product. The study assessed the historical and current trade in yellowfin tuna and reviewed the prospects of the market for high-valued agricultural products. Finally, this study reviewed Dominica’s ability to meet existing and proposed marketing and trade requirements, as well as the institutional and other factors that are in place, and those needed to provide continued support to increasing trade of this commodity.
Heart Sounds Classification for a Medical Diagnostic Assistance

In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen in order to compare and validate our results with those already published. We subsequently extracted 20 features from each provided record. For classification, we used the Generalized Linear Model (GLM), and the Support Vector Machines (SVMs) with its different types of kernels (i.e.; Linear, polynomial and MLP). The best classification accuracy obtained was 88.25%, using the SVM classifier with an MLP kernel.
A NOVEL CLASSIFICATION APPROACH OF TRAVEL REVIEW DATASET BASED ON ENTERTAINMENT

Recommender systems are evolving as an essential part of every industry with no exception to travel and tourism segment. Considering the exponential increase in social media usage and huge volume of data being generated through this channel, it can be considered as a vital source of input data for modern recommender systems. This in turn resulted in the need of efficient and effective mechanisms for contextualized information retrieval. Traditional recommender systems adopt collaborative filtering techniques to deal with social context. However they turn out to be computational intensive and thereby less scalable with internet and social media as input channel. A possible solution is to adopt clustering techniques to limit the data to be considered for recommendation process. In tourism context, based on social media interactions like reviews, forums, blogs, feedbacks, etc. travelers can be clustered to form different interest groups. This experimental analysis aims at comparing key clustering algorithms with the aim of finding an optimal option that can be adopted in tourism domain by applying social media datasets from travel and tourism context
Pulse-Compression Signal Propagation and Parameter Estimation in the Troposphere With Parabolic Equation

Wireless channel analysis is essential in the design, performance evaluation, and error correction of radar system. In this paper, an efficient parabolic equation (PE) method, which employs the split-step Fourier transform (SSFT) solution and Fourier synthesis technique, is developed for the propagation and parameter estimation of pulse-compression signals in the troposphere considering anomalous propagation conditions. A sliding window method is applied to reduce computational loads for long-distance propagation in time-domain PE. The signal delay is obtained via searching the peak of the correlation function of the received signal and a known reference signal according to the autocorrelation of the signals. The numerical examples indicate that the presented method is well suited for pulse-compression signals. Beyond that, a multiple signal classification (MUSIC) algorithm with spatial smoothing technique is introduced to obtain the signal direction of arrival (DOA) in PE model, where the covariance matrix is constructed via the array fields obtained from PE and the curvature of wavefronts due to the atmospheric refraction is considered in the array steering vector. The numerical examples verify the accuracy of the presented method. The simulation experiments in a typical sea-to-land scenario are presented to analyze the sensitivity of pulse-compression signals to evaporation ducts, including pulse waveform, time delay, and DOA, utilizing the presented methods.
A Reinforcement Learning Approach to Health Aware Control Strategy

Health-aware control (HAC) has emerged as one of the domains where control synthesis is sought based upon the failure prognostics of system/component or the Remaining Useful Life (RUL) predictions of critical components. The fact that mathematical dynamic (transition) models of RUL are rarely available, makes it difficult for RUL information to be incorporated into the control paradigm. A novel framework for health aware control is presented in this paper where reinforcement learning based approach is used to learn an optimal control policy in face of component degradation by integrating global system transition data (generated by an analytical model that mimics the real system) and RUL predictions. The RUL predictions generated at each step, is tracked to a desired value of RUL. The latter is integrated within a cost function which is maximized to learn the optimal control. The proposed method is studied using simulation of a DC motor and shaft wear.
Response of Florida citrus growers to the freezes of the 1980s

The decade of the 1980s brought a set of severe freezes to Central Florida, killing many of the state's citrus trees. The reacbons of Florida's citrus growers to these freezes suggest that the experience of extreme climate events lnduces adjustments in risk-hedging strategies. Replanting patterns and changes in the vanetal mix are found to be consistent with a portfolio model of investment behavior if it is assumed that growers' climate expectations have been updated in response to the recent freezes. Orange growers now appear to perceive an increased risk of freeze damage in those areas that were heavily affected by these events. This suggests that climate expectations and investment patterns will tend to evolve in response to changes in the frequency of extreme climate events brought about by global climate change.