Miracle-dz/UniFAR-FTUs huggingface dataset:
Does an Introduction of a Person in a Group by a Robot Have a Positive Effect on People's Communication?

lack of social relationships has become a significant issue in modern society. A robot is expected to enhance communication between people by introducing each person using knowledge of the person which the robot has obtained through interactions on their daily life. Therefore, we need to clarify what kind of behavior is required when a robot interacts with a group of people in such a situation. The goal of this paper is to reveal the kind of communication that occurs between a group of people new to each other if a robot introduces each person. We conducted an experiment in which two robots communicated with 14 people (7 children and 7 adults) from 5 families in the following situation. First, the robots asked each person a simple question about their person or daily life. Then, all the people and the two robots gathered together. The two robots started chatting, including an introduction of each person. Based on the video coding and questionnaires, we found that the robots generated an opportunity for people new to each other to learn about each other. Furthermore, we discuss the design requirements of the robot behavior to enhance communication between people new to each other through the introduction of each person by a robot.
SSL: Smart Street Lamp Based on Fog Computing for Smarter Cities

Both safety and energy conservation are very important advantages of smart cities. Namely, the city street lamp is correlated with both safety and energy conservation. Therefore, a street lamp is an indispensable part of the smart cities. However, current street lamps have lack of smart characteristics, which increases both danger and energy consumption. In order to address these problems, a smart street lamp (SSL) based on the fog computing for smarter cities is proposed in this paper. The advantages of the proposed SSL are as follows: 1) fine management, because every street lamp can be operated independently; 2) dynamic brightness adjustment, all street lamps can be adjusted dynamically; and 3) autonomous alarm on abnormal states, each street lamp can report the abnormal status independently, such as broken, stolen, and so on. The experimental results showed that the proposed SSL can improve the energy efficiency and reduce danger.
Predicting Future Visitors Of Restaurants Using Big Data

For efficient and economical operation, restaurant owners need to accurately estimate the number of future customers. In this paper, we propose an approach to predict how many future visi-tors will go to a restaurant using big data and supervised learning. The included big data involves restaurant information, historical visits and historical reservations. With features constructed from the big data, our approach generates predictions by performing regression using a mix of K-Nearest-Neighbour, Random Forests and XGBoost. We evaluate our approach using large-scale realworld datasets from two restaurant booking websites. The eval- uation results show the effectiveness of our approach, as well as useful insights for future work.
Imbalanced Data Classification Based on Extreme Learning Machine Autoencoder

In practice, there are many imbalanced data classification problems, for example, spam filtering, credit card fraud detection and software defect prediction etc. it is important in theory as well as in application for investigating the problem of imbalanced data classification. In order to deal with this problem, based on extreme learning machine autoencoder, this paper proposed an approach for addressing the problem of binary imbalanced data classification. The proposed method includes 3 steps. (1) the positive instances are used as seeds, new samples are generated for increasing the number of positive instances by extreme learning machine autoencoder, the generated new samples are similar with the positive instances but not same. (2) step (1) is repeated several times, and a balanced data set is obtained. (3) a classifier is trained with the balanced data set and used to classify unseen samples. The experimental results demonstrate that the proposed approach is feasible and effective.
License Plate Recognition Data-Based Traffic Volume Estimation Using Collaborative Tensor Decomposition

The sparse problem of traffic volume data is unavoidable due to budget limits and device malfunctions in traffic systems. To address this problem, we propose a license plate recognition (LPR) data and collaborative tensor decomposition (CTD)-based method to estimate the sparse traffic volume data. The method works in two phases: first, a vehicle-time matrix is created based on LPR data, and non-negative matrix factorization is employed to analyze vehicle types; second, a road traffic volume tensor and the corresponding matrix of vehicle types are created, and people’s check-in data and point of interest information are introduced to complement the sparse tensor with CTD. Experimental results show that our method outperforms traditional estimation methods, and it can estimate traffic volume data even when the missing rate is high.
Learning to Maximize Return in a Stag Hunt Collaborative Scenario through Deep Reinforcement Learning

In this paper we present a deep reinforcement learning approach for learning to play a time extended social dilemma game in a simulated environment. Agents face different types of adversaries with different levels of commitment to a collaborative strategy. Our method builds on recent advances in policy gradient training using deep neural networks. We investigate multiple stochastic gradient algorithms such as Reinforce or Actor Critic with auxiliary tasks for faster convergence.
A Model is Worth a Thousand Words?

An information system's (IS) design and implementation, beyond others, involve understanding of the social and organizational context of the system and making decisions according to the limitations of environment and technology. The open, dynamic and almost unbounded nature of contemporary environment of the ISs poses many new challenges. The need for reengineering of information systems has dramatically increased as legacy ISs are migrated to new platforms. Reengineering of an IS has the objective of extracting the contents, data structures, and flow of data and processes contained within existing legacy IS in order to reconstitute them into a new form for subsequent implementation. Inability of traditional information system (re)engineering approaches, methodologies and tools to cope with ever increasing complexity of contemporary information systems leads towards paradigm shift. Here we discuss an approach to IS (re)engineering that is based on Model-driven (MD) and Domain Specific Language paradigms. MD paradigm assumes orientation on models at all stages of system development and addresses complexity through abstraction. It promotes the idea of abstracting implementation details by focusing on: models as first class entities and automated generation of models or code from other models. MD software development approaches usually rely on Domain-Specific Languages (DSLs) that are specific to the certain domain of application. MD and DSL paradigms applied in information system (re)engineering projects increase: the development speed through automation and single point of system definition, the software quality, and component reuse. Their application reduces conceptual gap between problem domains and software implementation, improves manageability of complexity through abstraction, enables greater domain expert inclusion in the development process, and supports better communication between different stakeholders. The role of MD and DSL paradigms in information system (re)engineering is illustrated on the example of IIS*Studio development environment aimed at MD information system (re)engineering.
Comparing Sentiment Analysis and Document Representation Methods of Amazon Reviews

In the last few years sentiment analysis has made much progress. Sentiment analysis has been used in several applications to identify the opinions of people, products, brands, services, etc., which can, for example, improve a company's business. Some of these applications claim to have more effective document representation models than merely Information Retrieval approaches like the bag-of-words representation. Document representation models have increased interest to solve some of the limitations that bag-of-words representation has. In this paper, the several sentiment analysis and document representation methods of Amazon reviews are compared. In this paper, traditional models such as a bag-of-words, bag-of-ngrams and their TF-IDF variants combined with linear classifiers such as Logistic Regression and SVM, and deep learning models such as word-based convolutional neural networks (ConvNets) and the simple long short-term memory (LSTM) recurrent neural network were used. Various document representation techniques such as Paragraph Vector or using pre-trained Word2Vec and Glove word embeddings to compute the vector for each word in the document were tested, and word vectors are aggregated using the element-wise mean. It is shown that deep learning models perform better on our large dataset than traditional models. LSTM resulted with the best accuracy of 95.55%. Deep learning models generally work better than traditional models as training set size increases. Our best performing model can be used for automatic sentiment classification for future product reviews in retail stores.
Scale-Free Properties of Human Mobility and Applications to Intelligent Transportation Systems

Characterizing and modeling node mobility is of critical importance in building intelligent transportation systems and their applications. In this paper, we discuss the scale-free properties of some important human mobility characteristics, namely spatial node density and mobility degree, and show that they exhibit behavior that can be described by a power-law. Based on their power-law characteristics, we derive analytical models for the spatial node density and mobility degree and show that the data generated by the proposed analytical models closely approach the empirical data extracted from the real mobility traces. Another contribution of our work is to use the proposed analytical models to build a synthetic mobility regime that is suitable for simulations of intelligent transportation systems. Finally, through network simulations, we show that the ad-hoc network routing behavior under our mobility regime closely approximates routing behavior when the corresponding real trace is used.
Computation Offloading With Data Caching Enhancement for Mobile Edge Computing

Computation offloading is a proven successful paradigm for enabling resource-intensive applications on mobile devices. Moreover, in view of emerging mobile collaborative application, the offloaded tasks can be duplicated when multiple users are in the same proximity. This motivates us to design a collaborative offloading scheme and cache the popular computation results that are likely to be reused by other mobile users. In this paper, we consider the scenario where multiple mobile users offload duplicated computation tasks to the network edge, and share the computation results among them. Our goal is to develop the optimal fine-grained collaborative offloading strategies with caching enhancements to minimize the overall execution delay at the mobile terminal side. To this end, we propose an optimal offloading with caching-enhancement scheme (OOCS) for femto-cloud scenario and mobile edge computing scenario, respectively. Simulation results show that compared to six alternative solutions in literature, our single-user OOCS can reduce execution delay up to <inline-formula><tex-math notation="LaTeX">$42.83$</tex-math> </inline-formula>% and <inline-formula><tex-math notation="LaTeX">$33.28$</tex-math></inline-formula>% for single-user femto-cloud and single-user mobile edge computing, respectively. Our multi-user OOCS can further reduce <inline-formula><tex-math notation="LaTeX">$11.71$</tex-math></inline-formula>% delay compared to single-user OOCS through users’ cooperation.
A New Mutual Authentication Protocol in Mobile RFID for Smart Campus

At present, with the increasingly growing application of Internet of Things, virtualization and intelligent technologies, and the maturity of smart campus systems, campus card technology based on radio frequency identification (RFID) has developed rapidly. Due to the convenience of use, it is widely used in dormitory access control system, book borrowing system, attendance system, and payment system. Because the data carried by the RFID card is more and more complex, the requirements for its security are getting higher and higher. In order to solve the problem of identity authentication and privacy protection of the RFID application existed in the digital campus in recent years, an improved mutual authentication protocol in mobile RFID for smart campus is put forward by analyzing and studying existing smart campus authentication protocols based on hash algorithm, which uses the technology of random number S and time stamp T. This protocol implements mutual authentication among the tag, reader, and backend database without the premise that the signal path between the database and reader communication is a safe signal path and can effectively resist counterfeit attack, replay attack, position tracking attack, de-synchronization attack, eavesdropping attack, denial of service attack, and man-in-the-middle attack, and it can also guarantee forward security. Due to the large data operations and storage have been transferred to readers and backend data, the protocol is formally proved to have a strong reliability and a good security. In addition, through the comparison with other existing authentication protocols on the resident variable storage overhead, the cost of computing complexity and the security performance, the authentication protocol proposed in this paper has a better storage and calculation performance, and it can better satisfy the security requirements in mobile RFID applications for smart campus in the future and has a good application prospect.
A comparative study of gloved versus ungloved merocel® as nasal pack after septoplasty

Introduction: Septoplasty or septal reconstruction is a corrective surgical procedure performed to straighten the nasal septum. It may be associated with numerous complications. To minimize these complications, both nasal cavities are frequently packed with different types of nasal packing. Materials and Methods: This prospective, observational, and comparative study was undertaken in the Department of ENT, Rajindra Hospital, Patiala, Punjab, India. A total of sixty patients fulfilling the inclusion criteria participated in the study. They were divided into two groups, Groups A and B. After septoplasty, the nasal cavity was packed with gloved Merocel® in Group A and ungloved Merocel® in control group (Group B). The efficacy and patient tolerance for both nasal packings were compared and assessed. The data collected were compiled and analyzed statistically. Results: In our study, it was demonstrated that gloved Merocel® produces less pain during pack insertion (P = 0.001) and produces less pain while insertion of pack in situ (P = 0.001) and during pack removal (P = 0.001). Saccharin transit time (STT) returned back to normal in gloved Merocel® group (P = 0.001) in most of patients (27) by the 2nd week, whereas STT in ungloved Merocel® group returned back to normal by the 4th week postoperatively. The differences in impairment in STT between the two groups were found to be statistically significant. There was no statistical significance between both groups for other parameters. Conclusion: Gloved Merocel® may be preferred over ungloved Merocel® as nasal packing following septoplasty since both types of packs had similar hemostatic, adhesion prevention properties and similar incidence in postoperative complications and gloved Merocel® produces less pain during its insertion, while it is in situ, during its removal with early recovery of nasal mucociliary clearance mechanism of nose.
ECC-Enabled Translation Mechanism for Intercommunication Between IPv4 and IPv6

The depletion of IPv4 addresses made an exogenous pressure in the rapid implementation of IPv6. But the backward incompatibility nature of IPv6 paved the way for various transition mechanisms namely dual stack, tunneling and translation. Although these mechanisms aid in the transition from IPv4 to IPv6, there exist many security issues that need to be resolved in order to provide a secure communication environment. This paper aims to provide such security, using elliptic curve cryptography (ECC) to achieve integrity, confidentiality, authentication and non-repudiation. The reason for using ECC for security, includes low processing power and high security in resource constrained mobile and IoT devices. Here we have proposed a new addressing scheme that provides router scalability, 1:N addressing and security. The proposed translation mechanism is implemented with PC emulator and the results are discussed.
Faster sublinear approximations of k-cliques for low arboricity graphs

Given query access to an undirected graph $G$, we consider the problem of computing a $(1\pm\epsilon)$-approximation of the number of $k$-cliques in $G$. The standard query model for general graphs allows for degree queries, neighbor queries, and pair queries. Let $n$ be the number of vertices, $m$ be the number of edges, and $n_k$ be the number of $k$-cliques. Previous work by Eden, Ron and Seshadhri (STOC 2018) gives an $O^*(\frac{n}{n^{1/k}_k} + \frac{m^{k/2}}{n_k})$-time algorithm for this problem (we use $O^*(\cdot)$ to suppress $\poly(\log n, 1/\epsilon, k^k)$ dependencies). Moreover, this bound is nearly optimal when the expression is sublinear in the size of the graph. Our motivation is to circumvent this lower bound, by parameterizing the complexity in terms of \emph{graph arboricity}. The arboricity of $G$ is a measure for the graph density "everywhere". We design an algorithm for the class of graphs with arboricity at most $\alpha$, whose running time is $O^*(\min\{\frac{n\alpha^{k-1}}{n_k},\, \frac{n}{n_k^{1/k}}+\frac{m \alpha^{k-2}}{n_k} \})$. We also prove a nearly matching lower bound. For all graphs, the arboricity is $O(\sqrt m)$, so this bound subsumes all previous results on sublinear clique approximation. As a special case of interest, consider minor-closed families of graphs, which have constant arboricity. Our result implies that for any minor-closed family of graphs, there is a $(1\pm\epsilon)$-approximation algorithm for $n_k$ that has running time $O^*(\frac{n}{n_k})$. Such a bound was not known even for the special (classic) case of triangle counting in planar graphs.
A Low-Power Wideband dB-Linear Variable Gain Amplifier With DC-Offset Cancellation for 60-GHz Receiver

This paper presents a two-channel (I+Q) variable gain amplifier (VGA) for low power 60-GHz wireless receiver. The VGA consists of four-stage gain cells, an efficient DC-offset cancelling circuit and an output buffer for test purposes. To achieve wide bandwidth and low power consumption, a modified Cherry—Hooper amplifier gain cell is used. With a tunable transistor and its parasitic capacitance, a dB-linear characteristic and a flat gain response are achieved without any additional exponential circuit. The DC-offset is cancelled effectively with two proposed feedback loops. The measurement results show that the VGA achieves a gain range of 6 to 44 dB, an OP1dB of 1 dBm, and a 3-dB bandwidth of over 1 GHz with an excellent flat gain response. Fabricated in a 65-nm CMOS technology, the two-channel VGA core occupies a silicon area of only 0.055 mm2 and consumes only 10 mA from a 1.2 V power supply.
Personalized Lane-Change Assistance System With Driver Behavior Identification

An in-depth study on driving habits and personalized driving-assistance systems is conducive to the realization of vehicle safety and intelligent driving. In this paper, we present a personalized vehicle lane-change assistance system integrated with a driver-behavior identification strategy. First, the driver-behavior data-acquisition system is designed and established. Based on this, the input data of different kinds of drivers along with vehicle signals are collected under typical working conditions. The drivers are classified utilizing factor analysis and a fuzzy c-means clustering algorithm, and the identification of driver behavior is realized using a backpropagation neural network optimized by a particle swarm optimization algorithm. Then, personalized warning, planning, and control systems are designed for lane changing. The proposed personalized lane-change assistance system can provide more personalized recommendations to the drivers, increasing the potential for more widespread acceptance and use of advanced driver-assistance system. Finally, the correctness of the proposed personalized lane-change system is evaluated by conducting computer simulations and a driver-in-the-loop- simulation under various conditions. And the results show that the lane-change assistance system based on driver behavior can meet the driving needs of different drivers without sacrificing safety.
Finite-Time Sliding Mode Control of the Valve-Controlled Hydraulic System

Rapid tracking is the most important quality for the valve-controlled servo system. Therefore, a method is presented of terminal sliding mode control for the valve-controlled systems which can make the tracking error converge to zero within finite time. Actually, the main feature of this method is the terminal function involved in the sliding surface. The nonlinear items are introduced through the terminal function to build a moving terminal sliding surface. Based on the Lyapunov stability theory, the terminal sliding mode controller is constructed, which can realize finite-time convergence of position error. And the proposed control strategy is simulated on basis of a valve-controlled system model whose command functions is sine and cosine functions. The results of simulation show that the terminal sliding mode control method is feasible and effective for valve-controlled systems.
Vertically-Parallel Method and VLSI-Structure for Sorting of One-Dimensional Arrays

The vertically-parallel method for sorting one-dimensional arrays of numbers has been developed. The graph of the algorithm for vertically-parallel sorting of arrays has been built. The structure of the hardware for vertically-parallel sorting of one-dimensional arrays of large numbers has been designed. Components of the device for vertically-parallel sorting of arrays of numbers using FPGA have been implemented.