Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized Commodity Recommendations of Retail Business Using User Feature Based Collaborative Filtering

Collaborative filtering is an extensively adopted approach for commodity recommendation. This paper proposes a user feature based collaborative filtering algorithm named UFCF for personalized commodity recommendations of retail business. It adopts matrix factorization and user features that are extracted from users' behaviors to improve the accuracy of recommendation result and alleviate the impact of sparse data. Experiments with real datasets from a supermarket marketing group demonstrate the effectiveness of the algorithm.
A Robot Barista Comments on its Clients: Social Attitudes Toward Robot Data Use

This paper explores peoples attitudes about a service robot using customer data in conversation. In particular, how can robots understand privacy expectations in social grey-areas like cafes, which are both open to the public and used for private meetings? To answer this question, we introduce the Theater Method, which allows a participant to experience a “violation” of their privacy rather than have their actual privacy be violated. Using Python to generate 288 scripts that fully explored our research variables, we ran a large-scale online study ($\mathbf{N}=4608$). To validate our results and ask more in-depth questions, we also ran an in-person follow-up ($\mathbf{N}=20$). The experiments explored social & data-inspired variables such as data source, the positive or negative use of that data, and whom the robot verbally addressed, all of which significantly predicted participants' social attitudes towards the robot's politeness, consideration, appropriateness, and respect of privacy. Body language analysis and cafe-related conversation were the lowest risk, but even more extreme data channels are potentially okay when used for positive purposes.
Security Requirements for Cloud-based C4I Security Architecture

With the development of cloud computing technology, developed countries including the U.S. are performing the efficiency of national defense and public sector, national innovation, and construction of the infrastructure for cloud computing environment through the policies that apply cloud computing. Korea Military is also considering that apply the cloud computing technology into its national defense command control system. However, only existing security requirements for national defense information system cannot solve the problem related security vulnerabilities of cloud computing. In order to solve this problem, it is necessary to design the secure security architecture of national defense command control system considering security requirements related to cloud computing. This study analyze the security requirements needed when the U.S. military apply the cloud computing system. It also analyze existing security requirements for Korea national defense information system and security requirements for cloud computing system and draw the security requirements needed to Korea national defense information system based on cloud computing.
A Bayesian Theory of Mind Approach to Nonverbal Communication

This paper defines a dual computational framework to nonverbal communication for human-robot interactions. We use a Bayesian Theory of Mind approach to model dyadic storytelling interactions where the storyteller and the listener have distinct roles. The role of storytellers is to influence and infer the attentive state of listeners using speaker cues, and we computationally model this as a POMDP planning problem. The role of listeners is to convey attentiveness by influencing perceptions through listener responses, which we computational model as a DBN with a myopic policy. Through a comparison of state estimators trained on human-human interaction data, we validate our storyteller model by demonstrating how it outperforms current approaches to attention recognition. Then through a human-subjects experiment where children told stories to robots, we demonstrate that a social robot using our listener model more effectively communicates attention compared to alternative approaches based on signaling.
Towards Ad hoc Testing Technique Effectiveness in Software Testing Life Cycle

Software testing is the most important aspect of the system development. This process helps to identify the bugs, errors, faults and failures in a system which ultimately ensures the quality of system. There are several testing techniques and strategies which determines the quality of a system. Ad hoc testing technique is the free form testing technique which does not require the pre-planned documentation and test cases. This is an informal and creative software testing technique which requires the prior knowledge about the system functionality. This article focuses on the Ad hoc testing, its types, advantages, disadvantages, challenges, how to use it more effectively and when and where to use it in the testing process.
An Online Sockpuppet Detection Method Based on Subgraph Similarity Matching

Multiple identity deception (sockpuppetry) has become an increasingly important issue in the social media environment, because they are engaged in undesired behaviors such as spreading rumors, posting hate speeches. The case of blocked users initiating new accounts, called sockpuppets, is widely known and past efforts, which have attempted to detect such users, have been primarily based on verbal features and non-verbal behaviors like posting habits. Although these methods have achieved a certain degree of success, it is easy for smart sockpuppets to forge verbal and behavioral features to circumvent management, making it difficult to guarantee the performance of these detection methods. In this paper, we observe that the social network plays a critical role in sockpuppets' influence, because recovering similar social structures can a sockpuppet guarantee the similar propagation impact. With this observation, a structure-based online method is proposed, which turns the sockpuppet detection problem into a subgraph similarity matching problem. The experiment on two real-world datasets of Sina Weibo demonstrates that our method obtains excellent detection performance, significantly outperforming previous methods.
Predicting Execution Time of CUDA Kernel Using Static Analysis

With the growing demand for performance-oriented problems, programmers routinely execute the embarrassing parallel part of the application (GPU kernels) in a GPU in order to achieve signi? cant speedup. These applications are becoming complex and long-running which makes it energy inef? cient. Anticipating its execution time can help the developers to ? x the inef? cient code before running it. In this paper, we propose an approach to predict the execution time of a GPU kernel without the need of executing it. We build an analytical model to predict the execution time of a GPU kernel by analyzing the intermediate PTX code of a CUDA kernel. Our experimental analysis of a set of benchmarks shows that for 45 applications the estimated execution time has the mean absolute error of 26.86% when compared to the actual execution time. Mean absolute error for benchmarks belonging to Dynamic programming dwarf is minimum, followed by Dense Linear Algebra benchmarks.
Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles

The VANET (Vehicle Ad Hoc Network) is gathering attention for autonomous vehicles and the MANET (Mobile Ad Hoc Network) is attracting interest as well. Therefore, efforts have been made to overcome the challenges of the VANET in which the topology changes in real time and instability exists due to the difference in speed and physical phase. Particularly in the IoT era, the total amount of network nodes in addition to vehicle nodes is expected to increase dramatically. Therefore, a clustering algorithm for a mesh network capable of autonomous configuration is suitable for reducing the load of the central control device and data redundancy on the network, which is expected to increase as the IoT era progresses. However, since clustering algorithms based on the existing research have been developed for the current traffic situation, inefficiency is inevitable in the future autonomous navigation period in which traveling path prediction can be accurately performed. Therefore, this paper discusses a clustering algorithm and a data propagation algorithm between clusters using path information. The main content of this paper is as follows. First, we propose a clustering algorithm using path information and considering the existing research results. In the autonomous navigation period, if the path is predictable, the probability that the nodes in the same cluster are in the same block for a longer time than the conventional one can converge to 100%. Therefore, the survival time of the cluster can be dramatically improved. Second, we developed a data propagation algorithm that can increase the information propagation rate of the entire network using path information. The cluster temporarily stores the data to be disseminated and then disseminates it when it encounters another cluster of neighbors. Therefore, data can be disseminated even for noncontiguous clusters. To summarize, this paper proposes clustering-based data dissemination algorithms and protocols using vehicle pathways for autonomous navigation and compares them with clustering-based data dissemination algorithms using existing directions.
Reducing SARS-CoV-2 infectious spreading patterns by removing S and R compartments from SIR model equation

This research points to the asymptotic instability of SIR model and its variants to predict the behavior of SARS-CoV-2 infection spreading patterns over the population and time aspects. Mainly for the S and R terms of the equation, the predictive results fail due to confounding environment of variables that sustain the virus contagion within population complex network basis of analysis. While S and R are not homologous data of analysis, thus with improper topological metrics used in many researches, these terms leads to the asymptotic feature of I term as the most stable point of analysis to achieve proper predictive methods. Having in its basis of formulation the policies adopted by countries, I therefore presents a stable fixed point orientation in order to be used as a predictive analysis of nearby future patterns of SARS-CoV-2 infection. New metrics using a Weinbull approach for I are presented and fixed point orientation (sensitivity of the method) are demonstrated empirically by worldwide statistical data.
The GEWEX Water Vapor Assessment: Overview and Introduction to Results and Recommendations

To date, a large variety of water vapour data records from satellite and reanalysis are available. It is key to understand the quality and uncertainty of these data records in order to fully exploit these records and to avoid data being employed incorrectly or misinterpreted. Therefore, it is important to inform users on accuracy and limitations of these data records based on consistent inter-comparisons carried out in the framework of international assessments. Addressing this challenge is the major objective of the Global Water and Energy Exchanges (GEWEX) water vapor assessment (G-VAP) which was initiated by the GEWEX Data and Assessments Panel (GDAP). Here, an overview of G-VAP objectives and an introduction to the results from G-VAP’s first phase are given. After this overview, a summary of available data records on water vapour and closely related variables and a short introduction to the utilized methods are presented. The results from inter-comparisons, homogeneity testing and inter-comparison of trend estimates, achieved within G-VAP’s first phase are summarized. The conclusions on future research directions for the wider community and for G-VAP’s next phase are outlined and recommendations have been formulated. For instance, a key recommendation is the need for recalibration and improved inter-calibration of radiance data records and subsequent reprocessing in order to increase stability and to provide uncertainty estimates. This need became evident from a general disagreement in trend estimates (e.g., trends in TCWV ranging from −1.51 ± 0.17 kg/m2/decade to 1.22 ± 0.16 kg/m2/decade) and the presence of break points on global and regional scale. It will be a future activity of G-VAP to reassess the stability of updated or new data records and to assess consistency, i.e., the closeness of data records given their uncertainty estimates.
Efficient energy, cost reduction, and QoS based routing protocol for wireless sensor networks

Recent developments and widespread in wireless sensor network have led to many routing protocols, many of these protocols consider the efficiency of energy as the ultimate factor to maximize the WSN lifetime. The quality of Service (QoS) requirements for different applications of wireless sensor networks has posed additional challenges. Imaging and data transmission needs both QoS aware routing and energy to ensure the efficient use of sensors. In this paper, we propose an Efficient, Energy-Aware, Least Cost, (ECQSR) quality of service routing protocol for sensor networks which can run efficiently with best-effort traffic processing. The protocol aims to maximize the lifetime of the network out of balancing energy consumption across multiple nodes, by using the concept of service differentiation, finding lower cost by finding the shortest path using nearest neighbor algorithm (NN), also put certain constraints on the delay of the path for real-time data from where link cost that captures energy nodes reserve, energy of the transmission, error rate and other parameters. The results show that the proposed protocol improves the network lifetime and low power consumption.
Demand Response-Based Operation Model in Electricity Markets With High Wind Power Penetration

The issue of climate change has received considerable attention in recent decades. Therefore, renewable energies and especially wind units have become a central point of attention. The stochastic nature of wind power production is modeled by means of a scenario-based method to show the possible events in the real time. Based on the Monte-Carlo simulation method and employing constructed Rayleigh probability distribution function (PDF), several scenarios that demonstrate the behavior of wind farms in real time are generated. To this end, a uniform random variable is generated and assigned to the mentioned PDF. Afterwards, a wind speed with a probability is achieved followed by the amount of wind power generation. Also, with a scenario reduction method (forward method), the desired number of scenarios can be obtained. To cope with the uncertainties of wind power generation, resulting from the intermittent nature of this kind of energy, this paper proposes a demand response (DR)-based operation approach. In other words, unlike the previous models in the literature that considered a supplementary role for the DR, this paper introduces the main role for the DR in the operation of future electricity markets. This approach focuses on a comprehensive modeling of the DR programs (DRPs) for the operational scheduling of electricity markets, considering the uncertainties of the generation of wind turbines, aiming at increasing the network security and decreasing the operation cost. The incorporation of market-based DRPs, such as demand bidding and ancillary service DR, is also considered. Two novel quantitative indices are introduced to analyze the success of DRPs regarding efficiency and wind integration. Numerical results obtained on two IEEE test systems indicate the effectiveness of the proposed model.
Metasurface holography: from fundamentals to applications

Abstract Holography has emerged as a vital approach to fully engineer the wavefronts of light since its invention dating back to the last century. However, the typically large pixel size, small field of view and limited space-bandwidth impose limitations in the on-demand high-performance applications, especially for three-dimensional displays and large-capacity data storage. Meanwhile, metasurfaces have shown great potential in controlling the propagation of light through the well-tailored scattering behavior of the constituent ultrathin planar elements with a high spatial resolution, making them suitable for holographic beam-shaping elements. Here, we review recent developments in the field of metasurface holography, from the classification of metasurfaces to the design strategies for both free-space and surface waves. By employing the concepts of holographic multiplexing, multiple information channels, such as wavelength, polarization state, spatial position and nonlinear frequency conversion, can be employed using metasurfaces. Meanwhile, the switchable metasurface holography by the integration of functional materials stimulates a gradual transition from passive to active elements. Importantly, the holography principle has become a universal and simple approach to solving inverse engineering problems for electromagnetic waves, thus allowing various related techniques to be achieved.
Optical Fiber Humidity Sensor Based on Polyvinylidene Fluoride Fabry–Perot

An optical fiber Fabry–Perot (FP) for relative humidity (RH) sensing is proposed. The FP cavity is fabricated by splicing a short length of hollow silica tube in a single mode fiber. The fiber is then coated with a polyvinylidene fluoride (PVDF) thin film to work as a mirror. The fabrication process of the FP interferometer with a dip coating process in a PVDF/dimethyl formamide solution is presented. The pattern fringes of the FP suffer a wavelength shift due to the change in the PVDF’s refractive index with the ambient RH variation. A short overview of the cavity’s formation and stability is presented. The RH response of the FPI cavity is tested. The sensor presented a sensitivity of 32.54 pm/%RH at constant temperature and −15.2 pm/°C for temperature variation.
The design and experimental verification of broadband absorbing structure combined by resin and lossy periodic structures

With the aim to design a particular broadband absorbing structure, a multi-layer structure was proposed by combining resin and square patch periodic structures. The absorbing structure was fabricated by resin curing process and screen printing process. The computational and experimental results showed that the reflectivity could be lower than -10dB at5.4–18GHz with a total thickness of 5.7mm. This effective enhancement on EM wave absorption performance was believed to be a consequence of resonance effect and destructive interference induced by lossy periodic structures.
ECG Baseline Wander Correction and Denoising Based on Sparsity

To reduce the influence of both the baseline wander (BW) and noise in the electrocardiogram (ECG) is much important for further analysis and diagnosis of heart disease. This paper presents a convex optimization method, which combines linear time-invariant filtering with sparsity for the BW correction and denoising of ECG signals. The BW signals are modeled as low-pass signals, while the ECG signals are modeled as a sequence of sparse signals and have sparse derivatives. To illustrate the positive of the ECG peaks, an asymmetric function and a symmetric function are used to punish the original ECG signals and their difference signals, respectively. The banded matrix is used to represent the optimization problem, in order to make the iterative optimization method more computationally efficient, take up the less memory, and apply to the longer data sequence. Moreover, an iterative majorization-minization algorithm is employed to guarantee the convergence of the proposed method regardless of its initialization. The proposed method is evaluated based on the ECG signals from the database of MIT-BIH Arrhythmia. The simulation results show the advantages of the proposed method compared with wavelet and median filter.
Acteve++: An Improved Android Application Automatic Tester Based on Acteve

As the Android application increases in number, it is a challenge for researchers to test Android applications automatically. <italic>Acteve</italic> based on dynamic symbolic execution proposes a new method and achieves higher coverage. But it cannot achieve the ideal test coverage due to incomplete events input and some missing activities. In this paper, we propose an improvement scheme, making <italic>Acteve</italic> can produce broadcast events as test inputs, and has the capability to find more activities. Based on these improved schemes, we design Android applications automatic tester, <italic>Acteve</italic>++. We experimented and analyzed existing representative Android automated testing tools: <italic>Monkey</italic>, <italic>Dynodroid</italic>, <italic>GUIRipper</italic>, <italic>Acteve</italic>, and <italic>Acteve</italic>++ on an open test set. The experiments show that <italic>Acteve</italic>++ achieves high average test coverage in an acceptable time.
Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network

With the advent of the Internet of Things (IoT), the security of the network layer in the IoT is getting more and more attention. The traditional intrusion detection technologies cannot be well adapted in the complex Internet environment of IoT. For the deep learning algorithm of intrusion detection, a neural network structure may have fine detection accuracy for one kind of attack, but it may not have a good detection effect when facing other attacks. Therefore, it is urgent to design a self-adaptive model to change the network structure for different attack types. This paper presents an intrusion detection model based on improved genetic algorithm (GA) and deep belief network (DBN). Facing different types of attacks, through multiple iterations of the GA, the optimal number of hidden layers and number of neurons in each layer are generated adaptively, so that the intrusion detection model based on the DBN achieves a high detection rate with a compact structure. Finally, the NSL-KDD dataset was used to simulate and evaluate the model and algorithms. The experimental results show that the improved intrusion detection model combined with DBN can effectively improve the recognition rate of intrusion attacks and reduce the complexity of the neural network structure.
Effective Persuasion Strategies for Socially Assistive Robots

In this paper we present the results of an experimental study investigating the application of human persuasive strategies to a social robot. We demonstrate that robot displays of goodwill and similarity to the participant significantly increased robot persuasiveness, as measured objectively by participant behaviour. However, such strategies had no impact on subjective measures concerning perception of the robot, and perception of the robot did not correlate with participant behaviour. We hypothesise that this is due to difficulty in accurately measuring perception of a robot using subjective measures. We suggest our results are particularly relevant for the design and development of socially assistive robots.
Multi-band Antenna for 5G Applications

This paper presents a multi band triangular shaped micro-strip patch antenna for 5G applications. The proposed antenna is designed and simulated on FR-4 substrate and is capable of operating at different set of frequency regions in 5G. The antenna provides bandwidth of 3.255 GHz, 7.132 GHz,7.721 GHz and 8.434 GHz at resonate frequency of 43.9 GHz, 61.8 GHz, 75.8 GHz and 94 GHz respectively and have a return loss of -19.233 dB, -28.209 dB,-41.89 dB and -19.035 dB at the resonate frequencies. The proposed antenna also provides the VSWR<2 in each band achieved by the antenna.