Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Price-based Vehicle-to-Station Recommendations for EV Battery Swapping

We present a framework to integrate the choice of electric vehicle (EV) customers into the vehicle-to-station (V2S) routing problem for battery swapping. Instead of assigning stations to each EV customer directly, we provide a recommendation, including a list of station-price pairs that are available for EV battery swapping services, for customers to choose. Compared with assignment, recommendation is closer to reality for scenarios lacking incentives for the cooperation of customers such as the battery swapping services of private EVs. In this paper, we model customers’ behavior by their choice probability given a particular recommendation, which can be readily obtained based on analytics techniques once the real data are available. We propose an online V2S recommendation algorithm, which aims at maximizing the expected revenue of a group of battery swapping stations (BSSs) and ensuring the quality service of EV customers. Leveraging the primal-dual analysis, we show that the loss of revenue due to online EV arrivals is theoretically bounded by a provable competitive ratio. Moreover, numerical tests also validate that the proposed online algorithm can significantly outperform benchmarks in maximizing revenues in online settings.
Experiment and Analysis on Visual Field and Fixation when Going Upstairs and Downstairs

People’ visual information when walking on stairs is regarded an important variable that may affect their posture and balance in previous studies, but the detail of their eye movement remains unclear. In this study, we focus on people’s eye-movement when walking on stairs and design an experiment to examine subjects’ visual information (visual field and fixation) and possible influencing factors by using a mobile eye tracker. Based on data analysis, our results proved that the movement of going up/down stairs are significantly related to subjects’ visual field. The results also indicated that the factor of subjects’ gender affects both visual field and average number of fixation, but the factor of load-carrying situation only affects visual field. Some foundational insights of this study can be used to detect the fall risk for the elderly.
Cooperative spectrum sharing in 5G access and backhaul networks

Stringent demands for a continuous evolution of cellular networks push today academic and industrial researchers to re-think backhaul solutions for 5G. In one hand, wireless backhaul solutions are cost effective and easy to deploy but suffer from limited capacity. On the other hand, wired solutions have the potential to meet bandwidth requirements but usually involve higher costs. Thus, adoption of heterogeneous technologies will be necessary. Moreover, in 5G, access and backhaul networks will work closely, and therefore, total separation of their resources may not be possible anymore; rather, cooperation between the two portions of the cellular network is desirable. Subsequently, cooperative access-backhaul mechanisms become necessary to ensure the best use of the scarce resources, i.e. bandwidth. Hence, in this paper we present the idea of spectrum sharing among different links from a cooperative access-backhaul mechanism point of view. We present simulation results for different approaches of such sharing from a common spectrum pool. The results show that traffic-aware approaches show increased fairness thus reinforcing the idea of cooperative access-backhaul mechanisms as essential strategies in current and future networks.
Bio-Organic Glycine Based Flexible Piezoelectric Stress Sensor for Wound Monitoring

The application of controlled mechanical stress on a wound site can accelerate the wound healing by improving the cellular proliferation during tissue regeneration. To facilitate this there is need to monitor the applied stress around the wound site and hence there is need to develop a biocompatible stress sensor. Here we present the glycine-based biocompatible and flexible piezoelectric stress sensor. The sensor is based on novel piezoelectric composite comprising of piezoelectric y-glycine micro-crystals embedded in PDMS. On application of external force the sensor generates an average output voltage of 250 mV, with current density of 0.010 µA/cm2 and a power density of 2.5 nW/cm2. While the produced piezoelectric voltage helps accelerate the wound healing, the energy generation by the sensor could also find potential applications in self-powered touch sensors. The successful demonstration of presented stress sensor, we will pave the way for their integration on compression bandages and dressings used for chronic wound management.
Artificial Intelligence Based Adaptive GOP Size Selection for Effective Wyner-Ziv Video Coding

Wyner-Ziv video coding (WZVC) has been gaining many attentions in recent decades due to its low computational complexity and error resiliency benefits, notably when compared to traditional video coding standards such as H.264/AVC or High Efficiency Video Coding (HEVC) standards. In a Wyner-Ziv video coding scheme, the compression efficiency can be controlled by the length of the group of pictures (GOP) which typically consists of the two key and several WZ frames. However, the current Wyner-Ziv video coding solutions usually employ a fixed GOP size or simple adaptive GOP size mechanisms, which depend on some heuristic features extracted from video content. To address the limitation of the current GOP size adaptation solutions, we propose in this paper a novel Artificial Intelligence based GOP size adaptation mechanism and integrate it into the most advanced transform domain Wyner-Ziv video coding (TDWZ) architecture. In the proposed GOP size adaptation mechanism, the proper GOP size is learnt from the correlation between video features and the optimal compression performance. The power of machine learning techniques is used to select the most suitable video features and the model of GOP size and compression performance correlation. Experimental results shown that, using the obtained GOP size adaptation mechanism, the TDWZ codec achieved a better compression performance when compared to relevant benchmarks.
Forecasting of China Consumer Price Index Based on EEMD and SVR Method

This paper proposes a method of using ensemble empirical mode decomposition (EEMD) method for Chinese consumer price index. We analysis the periodicity and volatility of the Intrinsic Mode Function (IMF), they reveal the fluctuation information of Chinese consumer price index at different scales and prove the rationality and effectiveness of the ensemble empirical mode decomposition method. We use a hybrid EEMD and Support Vector Regression (SVR) model to forecast Chinese consumer price index. The hybrid method has higher precision. It can support certain reference value to the economic development strategy.
KOMPETENSI, KOMPENSASI, DAN KEPEMIMPINAN PENGARUHNYA TERHADAP KINERJA KARYAWAN PADA RUMAH SAKIT RATUMBUYSANG MANADO

Rumah sakit dibentuk untuk melayani konsumen di bidang kesehatan. Untuk mencapai sebuah keberhasilan, itu memerlukan landasan yang kuat yaitu organisasi yang mampu memperkuat dan memaksimumkan kompetensi.Kompensasi merupakan balas jasa yang diberikan perusahaan kepada karyawan sesuai dengan pekerjaan yang dilakukan. Kepemimpinan adalah keseluruhan aktivitas dalam rangka mempengaruhi orang-orang agar mau bekerja sama mencapai suatu tujuan yang memang diinginkan bersama. Kinerja merupakan tanggungjawab setiap individu terhadap pekerjaan, membantu mendefinisi harapan kinerja, mengusahakan kerangka kerja bagi supervisor dan pekerja saling berkomunikasi. Metode penelitian adalah asosiatif dengan teknik analisis yaitu regresi berganda. Populasi sebanyak 334 responden dan sampel 77 responden. Hasil penelitian menunjukkan bahwa kompetensi, kompensasi dan kepemimpinan berpengaruh secara simultan terhadap Kinerja karyawan. Namun pada pengujian parsial variabel kepemimpinan tidak berpengaruh signifikan terhadap kinerja karyawan. Hal ini berarti bahwa kepemimpinan yang ada masih kurang baik, itu dikarenakan masih kurangnya komunikasi antara pemimpin dan karyawan, kurangnya motivasi kerja yang diberikan oleh pemimpin kepada karyawan. Pemimpin sebaiknya memberikan semangat kerja bagi karyawan dan lebih memotivasi karyawan dengan menjalin komunikasi yang baik agar kinerja yang dihasilkan semakin baik. Kata kunci : kompetensi, kompensasi, kepemimpinan, kinerja karyawan
Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach

Over the last decade, music-streaming services have grown dramatically. Pandora, one company in the field, has pioneered and popularized streaming music by successfully deploying the Music Genome Project [1] (https://www.pandora.com/about/mgp) based on human-annotated content analysis. Another company, Spotify, has a catalog of over 40 million songs and over 180 million users as of mid-2018 (https://press.spotify.com/us/about/), making it a leading music service provider worldwide. Giant technology companies such as Apple, Google, and Amazon have also been strengthening their music service platforms. Furthermore, artificial intelligence speakers, such as Amazon Echo, are gaining popularity, providing listeners with a new and easily accessible way to listen to music.
Deep Fusion Feature Learning Network for MI-EEG Classification

Brain–computer interfaces (BCIs) are used to provide a direct communication between the human brain and the external devices, such as wheelchairs and intelligent apparatus, by interpreting the electroencephalograph (EEG) signals. Recently, motor imagery EEG (MI-EEG) has become an active research field where a subject’s active intent can be detected. The accurate decoding of MI-EEG signals is essential for effective BCI systems but also very challenging due to the lack of informative correlation between the signals and the brain activities. To improve the precision performance of a BCI system, accurate feature discrimination from input signals and proper classification are necessary. However, the traditional deep learning scheme is failed to generate spatio-temporal representation simultaneously and capture the dynamic correlation for an MI-EEG sequence. To address this problem, we propose a long short-term memory network combined with a spatial convolutional network that concurrently learns spatial information and temporal correlations from raw MI-EEG signals. In addition, spectral representations of EEG signals are obtained via a discrete wavelet transformation decomposition. In order to achieve even higher learning rates and less demanding initialization, we employ a batch normalization method before training and recognition. Various experiments have been performed to evaluate the performance of the proposed deep learning architectures. Results indicate a high level of accuracy over both the public data set and the local data set. Our method can also serve as a useful and robust model for multi-task classification and subject-independent movement class decoder across many different methods.
High Throughput and Low Cost Memory Architecture for Full Search Integer Motion Estimation in HEVC

The two-dimensional matrix array Integer Motion Estimation (IME) architecture using Full Search Motion Estimation (FSME) algorithm for High Efficiency Video Coding (HEVC) is presented in this paper. This architecture can operate for 4K resolution video at 30 fps with latency as low as 1219 clock cycles, allowing the design working in real-time. The memory required also kept as low as 12.5kB. The proposed architecture can reach maximum frequency of 148.6 MHz using 40 nm or 195.4 MHz in 28nm Virtex-6 FPGA using Xilinx ISE version 13.1.
Privacy Preserving on Trajectories Created by Wi-Fi Connections in a University Campus

In universities, it is common to find many access points widely distributed through the campus aiming to cover most of the area with Wi-Fi signal. Each time a connection between a device and an access point is performed, the data generated by that action (e.g., location, student identification and time) are stored in a log file. This file allow us to track back the trajectories of the students inside the universities, ordering this data by time. With the identification attribute of the student, it is possible to join it with quasi-identifiers present in the university systems. Publication of such data may put the privacy of university students in risk. Given that, this article proposes a method of anonymization called Mix $\beta$-k-anonymity. This technique provides a set of possible trajectories of a group of people with the same quasi-identifiers. After the application of this method, it is showed that with the right choice of the quasi-identifier is possible to anonymize the trajectories making possible the disclosure for operational mobility research on campus.
Enhancing Sketching and Sculpting for Shape Modeling

Sketch-based modeling uses freeform strokes as basic modeling metaphor and provides an intuitive way for shape modeling, for instance, for cyberworlds. This paper presents a new method to enhance sketch-based modeling. The core idea of the method is to enhance the sketching process by allowing the user to iteratively sketch to progressively create initial shapes that interpolate the sketched strokes. This process considers all the sketches and the up-to-date constructed 3D shape, which enables the user to be aware of the shape of the sketched model. The key underlying technique that supports this process is a novel surface construction algorithm, which generates 3D triangular mesh models with gradual shape changes during iterative sketching. Experiments demonstrate that the presented method can allow users to intuitively and flexibly create and edit 3D models even with complex topology, which is usually difficult in existing sketch-based modeling systems.
Practical Evaluation of UK Internet Network Characteristics For Demand-Side Response Applications

Internet based communications is a necessary solution for enabling smart grid services such as demand side management, automatic metering infrastructure, and virtual power plants. However, the speed and reliability of the Internet for providing smart grid services needs practical investigation. In this paper, we evaluate the robustness of the U.K. Internet network for demand response services, based on the latency, packet loss and jitter for smart grid communication. We use the ping tool to identify the minimum achievable latency within a national internet topology and the analysis has also been extended to consumer internet access. Further, the internet connectivity of consumer’s premises has been evaluated regarding suitability of these solutions for demand response applications.
An Adaptive Multi-objective Heuristic Search for Model-Based Testing

Search-based Testing (SBST) consists in the application of metaheuristic search techniques for test case generation. Most studies are focused on the generation of test data for white-box testing, but there are also proposals for model-based test case generation. In previous works, our group developed a method and a tool that supports multi-objective metaheuristic search techniques for test case generation from state models. A problem that practitioners and researchers face when employing search-based test case generation tools is that the algorithms must be properly parameterized for the different problems and even problem instances. However, proper parameter selection is a highly specialized and time-consuming task, which most often is not part of practitioner's skills. In this paper, we introduce an adaptive approach for test case generation in the context of model-based testing. After a literature review, to understand the current approaches for parameter adjustment, we selected some of them to implement. The different algorithms were compared with manual parameter tuning. Preliminary results indicate that the different parameter control algorithms used in this study can be useful and were integrated to the test case generation tool. Their performance may vary according to the model at hand, that is why we think it is better to offer distinct options to the users, besides the already existing manual parameter setting.
The Flagellation of Christ by Piero della Francesca: A Study of Its Perspective

This article discusses a method for taking measurements on artworks, a process that is useful in the perspectival analysis of a painting or a drawing. This method involves the use of a digital image of an original artwork that is viewed on a computer monitor and processed by computer software that reads the coordinates of points in the artwork. Once measurements have been expressed in terms of the coordinates of points on the digital image, perspectival analysis is obtained by algebraic calculations. The authors used this method to analyze the perspective of the painting The Flagellation of Christ by Piero della Francesca. Results include the determination of the vanishing point and of the projection point.
Genetic and chemical divergence among host races of a socially parasitic ant

Abstract Host–parasite associations facilitate the action of reciprocal selection and can drive rapid evolutionary change. When multiple host species are available to a single parasite, parallel specialization on different hosts may promote the action of diversifying natural selection and divergence via host race formation. Here, we examine a population of the kidnapper ant (Polyergus mexicanus) that is an obligate social parasite of three sympatric ant species: Formica accreta, F. argentea, and F. subaenescens (formerly F. fusca). Behavioral and ecological observations of P. mexicanus have shown that individual colonies parasitize only one species of host and that new Polyergus queens maintain host fidelity when establishing new colonies. To successfully adapt to a particular host, Polyergus ants may mimic or camouflage themselves with the species‐specific chemical cues (cuticular hydrocarbons) that their hosts use to ascertain colony membership. To investigate the extent of host specialization, we collected both genetic and chemical data from P. mexicanus that parasitize each of the three different Formica species in sympatry. We show that host‐associated genetic structure exists for both maternally inherited mitochondrial DNA data and biparentally inherited microsatellite markers. We also show that P. mexicanus can be distinguished by chemical profile according to host due to partial matching with their host. Our results support the hypothesis that host race formation is occurring among lineages of P. mexicanus that use different Formica hosts. Thus, this system may represent a promising model for illuminating the early steps of divergence, accumulation of reproductive isolation, and speciation.
Knowledge, attitude and perception toward evidence-based medicine among medical students in Saudi Arabia: Analytic cross-sectional study

Objective: To study the knowledge, attitudes, and perceptions toward evidence-based medicine among medical students from different colleges across the Kingdom of Saudi Arabia. Materials and Methods: An adapted evidence-based medicine (EBM) questionnaire was administered to second year to sixth year level of medical students and interns from different medical colleges across the Kingdom from November 2016 to May 2017. The questionnaire contains items that would describe the demographic characteristics of the respondents: 24 multiple-choice questions and one open-ended question. Questions were randomly arranged to refrain from respondents’ bias but are identified to determine the knowledge, attitudes, and perceptions of the respondents toward evidence-based medicine (EBP). Data were analyzed statistically using descriptive statistics. Results: This study surveyed 344 medical students from different universities in Saudi Arabia. The students’ knowledge and attitude to EBM were low: 80.8% answered incorrectly on the components of EBM; 40.4% knew that the strongest evidence to EBP is systematic review, and 95% of the respondents were not aware of the Cochrane Library (CL). Nearly 70% did not attend EBP workshops, 18% read journals, and 85.8% use the Internet to support clinical decisions. Only 50% are interested of knowing and using CL, 69.5% would evaluate the veracity of evidence when it contradicts clinical judgment, 24.4% will follow the evidence, and 6.1% will discard the evidence favoring their clinical judgment. No journal subscription, having no time, and difficulty in comprehension were the greatest reported barriers with a relative weight of 29.1%, 25%, and 15.7%, respectively; 68.8% claimed that EBP is not applicable to their culture, and 87.1% believed that their patients are willing to participate in clinical decision-making but perceived a low participation in clinical trials. Conclusion: The findings described the current status of the level of awareness and use of EBM among the medical students. This calls for a well-structured incorporation of the pedagogy into the undergraduate curriculum as a major competency standard considering culture and values that are well-preserved in the Kingdom. The outcomes of its integration will impact not only the medical and allied-medical professions but also the public health in general.
Requirement for CD40/CD40L Interactions for Development of Autoimmunity Differs Depending on Specific Checkpoint and Costimulatory Pathways

CD40/CD40L interactions play a critical role in immunity and autoimmunity. In this study, we sought to understand the requirement for CD40 signaling in the programmed cell death-1 (PD-1) checkpoint and CD28 costimulatory pathways important for maintenance of peripheral tolerance. Blocking either pathway can result in loss of self-tolerance and development of autoimmunity. We found that primary Sjögren’s syndrome (pSS) and autoimmune thyroid diseases (ATDs) that develop spontaneously in CD28-deficient IFN-γ−/− NOD.H-2h4 (CD28−/−) mice required CD40 signaling. Specifically, blockade of CD40L with the anti-CD40L mAb, MR1, inhibited autoantibody production and inflammation in thyroid and salivary gland target tissues. Unexpectedly, however, ATD and pSS in PD-1–deficient IFN-γ−/− NOD.H-2h4 (PD-1−/−) mice developed independently of CD40/CD40L interactions. Treatment with MR1 had no effect and even exacerbated disease development in pSS and ATD, respectively. Most interesting, anti-thyroglobulin and pSS-associated autoantibodies were increased following anti-CD40L treatment, even though MR1 effectively inhibited the spontaneous splenic germinal centers that form in PD-1–deficient mice. Importantly, blockade of the PD-1 pathway by administration of anti–PD-1 mAb in CD28−/− mice recapitulated the PD-1−/− phenotype, significantly impacting the ability of MR1 to suppress ATD and pSS in these mice. These results indicate that there can be different pathways and requirements to autoimmune pathogenesis depending on the availability of specific checkpoint and costimulatory receptors, and an intact PD-1 pathway is apparently required for inhibition of autoimmunity by anti-CD40L.