Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Dynamic Matching Under Impatient Demand and Patient Supply

We consider an infinite-horizon dynamic matching problem where the agents on one side, say supply side, are long-lived, and those on the other, say demand side, are impatient and will get lost if unmatched upon arrival. The agents on either side are vertically differentiated with a high- or low-quality level and arrive at the platform sequentially. The matching reward has a supermodular structure, more specifically, it is the multiplication of quality levels of agents in a match. In a centralized setting, we show that the optimal matching priority is different from assortative mating that is optimal for a system with agents on both sides long-lived (see Baccara et al. 2016). In the lost sales setting, it is optimal to still prioritize high-quality supply over low-quality one to satisfy high-quality demand; however, for the low-quality demand, low-quality supply has priority. This is because the centralized planner has an incentive to hold high-quality supply for future arrivals of high-quality demand. In a decentralized setting where the matching reward is split between agents in a match, in equilibrium, a similar protocol prevails, but the expected queues are inefficiently long or short. We show that in some cases the decentralized matching process can be perfectly coordinated as the centralized one by only adjusting the reward allocation to high-quality supply.
Investigating Variable Dependencies in Dialogue States

Dialogue State Tracking is arguably one of the most challenging tasks among dialogue processing problems due to the uncertainties of language and complexity of dialogue contexts. We argue that this problem is made more challenging by variable dependencies in the dialogue states that must be accounted for in processing. In this paper we give details on our motivation for this argument through statistical tests on a number of dialogue datasets. We also propose a machine learning-based approach called energy-based learning that tackles variable dependencies while performing prediction on the dialogue state tracking tasks.
A Context-Aware Platform For Personalized Content Delivery In Educational Environments

In most educational environments, the process of communication with students is performed on ad hoc basis, with lowstandardization and few interaction. However, a more dynamic communication is demanded by the young profile of most students. In this way, mobile devices are an efficient and flexible channel to reach this public. In this scenario, we present a context-aware platform to provide the distribution of educational content using a sensitive model context based on the users location. Thus, the platform can satisfy the specific demands informed by the user, and also respond to the current context, delivering personalized content according to the users location. Moreover, this is a multidisciplinary project, involving students in the role of users and also as collaborators for modeling, development and testing of the platform.
Emotion recognition method of Tibetan micro-blog text based on sentiment dictionary

With the development of network technology, social media represented by micro-blog has become an important platform for personal emotional expression. Automatic analysis of micro-blog texts and identification of their emotions is important for understanding people’s emotions. In this paper, the emotion of Tibetan micro-blog text is analyzed automatically for the first time, the emotion in text expression is recognized, the analysis method based on Tibetan sentiment dictionary and rules is put forward, and the language characteristics of emotion expression in Tibetan micro-blog are discussed. The experimental results show that the accuracy of emotion recognition based on Tibetan sentiment dictionary reaches 78.6%, which provides a basis for establishing a high-precision Tibetan textual sentiment classification system.
Identification Of Driver Types Using Drive Lab Data

IDENTIFICATION OF DRIVER TYPES USING DRIVE LAB DATA Xuezhao Li University of Guelph, 2019 Advisor: Professor Deborah A. Stacey With a computer-aided driving simulator, the data of the driver’s every move will be recorded. Because of the economic and risk-free feature, driving simulator is becoming popular in vehicle design, road construction and human factors researches. In this regard, we have three hypotheses: 1. Drivers’ performance in a driving simulator can reflect the real driving situations; 2. Different driving behaviour can reflect different personality; 3. Drivers can be classified based on the data collected by a driving simulator. To test these hypotheses, the data from two psychology experiments was applied. The driving bahaviours of different drivers when passing a same hazard were compared and analyzed. An unsupervised Kmeans was used to give preliminary labels and the artificial neural network was applied to verify the labels. Finally, a significant gender effect was found. Training the neuron network with only female data then save the model to test only male data showed a low predicting accuracy. Repeating the experiments with only female data gave the best accuracies.
Supervoxel based point cloud segmentation algorithm

In the field of 3D imaging and computer graphics, how to decompose 3D point cloud data into regions with semantic information and functional significance is an important research issue. In this paper, through the construction of the graph, the point cloud segmentation problem is transformed into a graph clustering optimization problem, and a supervoxel based point cloud segmentation algorithm is proposed. It starts with the supervoxel segmentation of the point cloud, and by calculating the similarity between each voxel seed and the other voxels, it clusters the voxels to complete the over-segmentation of the point cloud, and regards each supervoxel as a vertex in the graph. This processing simplifies the construction of the graph model. Then, the weight of the graph is constructed according to the color and geometric information, and then the graph is segmented and clustered. Finally, there may be some over-segmented supervoxel blocks, which are fused according to the structural characteristics. Verified by the open 3D segmentation dataset (OSD), the proposed algorithm has higher segmentation accuracy than the existing point cloud segmentation algorithms.
Uncertainty analysis of sensitivity of MEMS microphone based on artificial neural network

Due to the uncertainties from manufacturing processes and material properties, MEMS (micro-electro-mechanical system) microphone may exhibit significant variations in their performance compared to the nominal design. The published analytical methods have obvious shortcoming, which cannot meet the needs of both accuracy and efficiency. In order to improve efficiency, the MC (Monte Carlo) simulation based on ANN (artificial neural network) is presented to analyze the uncertainty of sensitivity of polysilicon circular clamped diaphragm microphone. Using the PDS (probabilistic design system) of Ansys software and MC simulation to predict the qualified rate of microphone, the simulated results are 91.2% and 91.4% respectively. The qualified rate of manufactured microphones is 91.5%. In addition, the time-consuming of these two simulations are 10 minutes and about 3000 minutes. This paper also analyzes the change of sensitivity probability densities with the varieties of nominal parameters of diaphragm. The results show that the presented MC simulation with accuracy and high efficiency is an alternative to the traditional methods.
Analysis of Mathematical Communication Skills in Submission of Field Geometry Based on The Van Hiele Theory

The purpose of this study was to analyzed of mathematical communication skills in submission of field geometry based on the van hiele theory. The research method used is descriptive analysis method using qualitative approach. The data collection technique used in this study is the test method. The subjects in this study were the 2018 STKIP class of mathematics education students at STKIP Pamane Talino in the Field Geometry course. The result from students of STKIP Pamane Talino in the Geometry Field course is (1) high-ability students capable of proving images into mathematical language and making symbols, making and providing good information about understanding and parts of points, lines and fields, can provide information about two possibilities of right triangles by using special angles, both relations between special angles that can, provide information and about information about its parts, can represent circular tangents and information about their parts, able to express circle resolution with right, and perceive relationships between properties and between figures (2) Students with moderate abilities cannot provide information about points, lines, and fields properly. The parts of the point, line, and field are not yet clear symbols, the information given about right triangles is not yet complete and the mathematical relations described are still incomplete about special angles, do not describe the circle of information, information about resolutions and their parts are conveyed with details, mathematical representation is still wrong, can use their own language when expressed again about circle resolution, and see figures as collections of properties (3) students with low ability cannot provide information about points, lines and fields and students are not able to symbolize points, lines and fields, and recognize figures by appearance alone, often by comparing them to a known prototype.
Research on Extraction of Address from Criminal Case Records via Lattice LSTM

As an important carrier for recording the details of case in the field of criminal investigation, case records contain address of the crime scene which is crucial to the case analysis. This paper uses Lattice LSTM to extract address from case records which combines the advantages of the character-based and word-based models. It can exploit word and word sequence information without performing Chinese word segmentation. For the purpose of optimizing the model, this paper proposes a word vector template which is generated by address repository of Shanghai to pretrain more accurate and more controllable word embeddings. The experiment shows that the model achieves the performance with the precision rate of 96.49%, recall rate of 94.83% and F1 score of 95.65%.
Research on fault diagnosis method based on KPCA-SDAE

The complexity of modern chemical processes is increasing, and the degree of process automation is also improving. In order to extract further features and diagnose the faults of TE process accurately, steadily and quickly, this paper presents a method of using Kernel Principal Component Analysis (KPCA) and Stacked Denoising Auto-Encoder (SDAE) to extract the secondary features and using softmax to classify the faults. Firstly, the KPCA is used to reduce the dimensionality of the data, reduce the irrelevant feature information, and prepare for further feature extraction of SDAE. Then SDAE is used to extract features through unsupervised pre-training and supervised global fine-tuning, and effective feature information is obtained for classification. Finally, softmax classifier is used to classify the extracted features, and fault state recognition and classification of TE process are realized. Experiment results show that this method can extract feature information successfully, identify fault types accurately, and its accuracy and training speed are better than those using SDAE alone.
Cost efficient 5G heterogeneous base station deployment using meta-heuristics

Over the last two decades, the telecommunication industry has witnessed sustained growth in the number of mobile user devices driven by the introduction of data services, the take-off of the internet and smart user equipment. This growth, which is forecasted to continue, has continued to push the data transfer capacity requirement on mobile networks and has motivated research into the design of 5th generation (5G) mobile networks. A key concern in the design of 5G is the infrastructure and power consumption cost of the base station network which is expected to be significantly more advanced and dense than that of existing conventional mobile networks. This thesis presents an optimisation framework for the cost efficient design of 5G base station networks, based on the application of metaheuristic algorithms. The presented optimisation framework is centred on the ability to exploit three key technologies of 5G, a heterogonous base station network with small-cells, multi-antenna spatial multiplexing MIMO and cell range extension. The framework includes mathematical integer programming models for supporting the decisions about the optimal base station topology in a 5G mobile network and provides a clear core for the application of metaheuristics for optimising 5G base station deployment. The core optimisation framework includes the definition of solution encoding/decoding and fitness mechanisms. To increase power consumption awareness of base station network design, an independent base station deployment strategy has been presented and evaluated. Simulation results show that the strategy can improve base station network design power consumption by as much as 34%. The work in this thesis has been extensively evaluated using a simulated 5G mobile network system model. Evaluations of algorithms have been performed through empirical measurements. The main contribution of this thesis is the definition of a clear framework for application fitness based heuristic search in the design of 5G mobile networks.
Modeling and Optimization of Mobility-Aware Dynamic Caching With Time-Varying Content Popularity

A mobility-aware dynamic caching optimization problem (MDCP) is modelled for optimal caching when the contents’ popularity may vary over time. The <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula>-hardness of MDCP is proved. For the problem's solution, first, by providing a reformulation, the optimum of MDCP can be computed in up to medium-scale system scenarios. In addition, a time-efficient algorithm is developed for large-scale scenarios. The results of performance evaluation are provided.
A wide swing, high drive current output stage

ABSTRACT In this paper, a novel current output stage (COS) is introduced. Deliberately compose of class AB operation and a special voltage sense and current compensation scheme enables the structure to operate at extreme voltage beyond the triode limits to achieve wide voltage dynamic range and grants it such following ever demanded merits at power supplies of 1.8V: low power consumption of 40 µW, high drive to bias current ratio of 100. The results are verified by Spectre post-layout simulation using 0.18 µm CMOS standard technology of TSMC. The process, the stability and robustness of the proposed COS are verified by stepwise response and Monte Carlo and corner simulations, respectively.
Métodos baseados em Deep Learning para Análise de Vídeo

O livro Minicursos do XXV Simposio Brasileiro de Sistemas Multimidia e Web oferece a sua comunidade textos relacionados a temas que norteiam os ultimos avancos na area de multimidia e Web, trazendo informacoes sobre novas tecnologias e topicos atuais de pesquisa em areas correlatas ao tema. Assim, este volume oferece uma excelente oportunidade para familiarizacao dos interessados com novos temas de pesquisa que podem vir a ser uteis em suas vidas profissionais.
Solving assembly line balancing problem using heuristic: a case study of power transformer in electrical industry

Currently, problem in assembly line has created so much attention, particularly in manufacturing area. Similar to this case study as they faced with problems regarding workstation in production line of electrical industry. There exist some cases where workstations in assembly line are experienced with bottleneck and suffered from high idle time. Thus, four heuristic methods are used for minimizing number or workstations and improve the bottleneck problems at the same time. In this case study, LCR, RPW and LPT have successfully minimized the number of workstations from 19 to 16. This solution has affected the layout of assembly line. Different from SPT that manage to improve the bottleneck among workstations by reducing number of workstations from 19 to 17 without changing the line layout. Therefore, both solution has brought an option for an engineer to choose which decision to be used in this assembly line in order to increase the line efficiency.
On the periphery of the parts of speech system

The article is devoted to the consideration of the periphery of the parts of speech system. The theory of parts of speech, constituting the core of the theory of grammatical structure and serving as the basis for describing the linguistic system as a whole, is an area of controversial debates in the context of various linguistic paradigms. The internal contradictions inherent in the language system do not allow developing integrated, logically consistent criteria for the classification of lexis, covering all categories of words. The functional-semantic and formal heterogeneity of lexical categories does not make it possible to include them into “the Procrustean bed,” a scheme recommended by various linguistic schools. The most contradictory classifications are the ones of the so-called synsemantic, functional words which are characterized by a high degree of categorical interpenetration. The determination of the categorical status of this kind of words is possible only on the basis of semantic and pragmatic features of discourse, as well as when taking into account presupposition factors.
Design of power factor meter using internet of things for power factor improvement, remote monitoring and data logging

Nowadays, many residential and commercial buildings that used electricity needs to take care the power factor to avoid penalty from the utility companies. A power factor that is close to one provides a good indicator for the overall power quality. Therefore, power factor improvement plays a significant role to reduce electricity consumption and more efficient system operation. In this paper, the design of power factor meter using Internet of Things will be discussed. Voltage and current sensors outputs were interfaced to Arduino, in which the real power and apparent power were calculated to determine the power factor. Results showed the effectiveness of our proposed device in measuring power factor. Moreover, the measured data points were logged in an SD card and can be accessed by computer with Matlab graphical user interface (GUI). In addition, IoT framework analysis for smart meter which can provide power factor improvement, remote monitoring, and data logging was further discussed in this paper.
Challenges of patient handover process in healthcare services: A systematic review

BACKGROUND: The patient handover process is in fact a valuable and essential part of the care processes in the hospitals. This can be a factor in increasing the quality and effectiveness of medical care. Incorrect and incomplete handover can increase the percentage of errors and cause serious problems for patients. The aim of this study was to identify the handover challenges concerning safety and quality of health services. MATERIALS AND METHODS: A systematic review was conducted according to the Preferred Reporting Item for Systematic Reviews and Meta-analyses guideline. The key words “challenges of patient handover” or “challenges of patient handoff” were used in combination with the Boolean operators OR and AND. The ProQuest, Ovid, Doaj, Magiran, SID, Scopus, Science Direct, PubMed, and ISI were searched. RESULTS: A total of 263 articles were extracted, and 20 articles were selected for final review. The results of selected articles indicated that there are various challenges such as communication, noncoordination, nonuse of checklist, poor management, time management, and other things. These studies reported that communication was the main challenge of handover process. CONCLUSIONS: Hospitals try to provide a lot of services to the patients and other customers in a safe and healthy environment. Lack of communication among the incoming and outgoing nurses in handover process is one of the main causes of reduced safety and quality of services and patient dissatisfaction.
Efficient and Flexible Role-Based Access Control (EFRBAC) Mechanism for Cloud

The cloud reduces the user’s burden to many folds. But cloud providers and cloud users with dynamic relationship, are in distinct security domains. Amongst various challenges with cloud, the crucial one is to detect and protect the user’s data from unauthorized accesses. In cloud, users are not legendary by their predefined identities. Instead, they are providing accesses based on their characteristics and attributes. This work is focusing on available access control mechanisms and one that applicable for cloud environment. The paper also proposes an Efficient and Flexible Role-Based Access Control (EF-RBAC) mechanism for the cloud computing environment to achieve confidentiality and security. RBAC limits the accesses for resources within an organization to authorized users only and also guarantees that a user can solely access specific information they are authorized for by the organization policy. The proposed scheme adds flexibility to the RBAC for better cloud user’s experience.
Avaliação automática da utilidade de reviews usando Redes Neurais Artificiais no corpus do Steam

Through online reviews, consumers can communicate with product suppliers and influence the buying decision of other consumers over the Internet. However, thanks to the high number of published reviews daily, it is difficult to identify which texts to read. As a solution for that problem, some websites use a review evaluation system based on the users votes, which, while useful, is not always ideal. This paper proposes an automatic method to analyze the helpfulness of online user reviews from Steam using Multilayer Perceptron Artificial Neural Network. We found out that certain features of reviews affect the perception of helpfulness and we discuss applications and future researches.
Development of a Low-Cost System for Laparoscopic Skills Training

Technological advancements in video equipment and biocompatible materials have enabled improvements in complex surgery through small incisions. The mastery of these laparoscopic surgical techniques is now a requirement for surgeons, however, the necessary skills are not intuitive and require hundreds of practice hours. The current state of surgical education includes animate models, inanimate physical models, and computer-based simulations, the latter of which are limited by cost, accessibility, and a lack of engagement. We propose a novel low-cost training interface that mimics the laparoscopic surgical environment using customized instruments whose movement and control are used as inputs for video games. The system is significantly less expensive than commercial systems and allows users freedom to select and play any game, enabling a take-home system with potential for higher levels of engagement, as well as familiarity and expertise with ambidextrous laparoscopic hand motion. A preliminary study compared performance on FLS (Fundamentals of Laparoscopic Surgery) testing before and after training. For a precision cutting task, groups that trained on a standard simulator or on the new system with either a noninverted or inverted hand-instrument mapping showed statistically significant improvements, warranting further investigation of training with this new system.