Miracle-dz/UniFAR-FTUs huggingface dataset:
What's the Next Move? Learning Player Strategies in Zoom Poker Games

In this article, we address the problem of modeling the actions of a human player in order to learn his strategies from his past game logs in Zoom Texas Hold'em poker variant. Although Texas Hold'em is a very popular game, Zoom is yet a very recent format of game in which, instead of playing in a specific table against a specific set of opponents, a player is placed in a large pool of players in which their opponents change every hand. Pros and cons of Zoom include respectively bigger effective time playing (and possibly getting money) and scarcity of data to get reads from the opponents. To deal with this problem, our model consists of a simple and generic set of features designed to fulfill each one of four proposed categories (hand quality, position insights, aggressiveness and current situation) in order to be able to capture a wide range of player strategies in each stage of the game. As a consequence of our modeling, we generate five data sets which were further evaluated by machine learning techniques. The results show that much of the player strategies were effectively learned, especially by non-linear techniques. Moreover, our data sets are available online as a test-bed for machine learning research in poker games.
Design of Ion Pump Power Supply Based on LCC Resonant Converter

In this paper, the detailed design of an ion pump power supply is presented. The ion pump power supply was designed based on an LCC resonant topology with high switching frequency to take advantage of a smaller transformer for isolation. Because the proposed ion pump power supply has a 7-kV, 50-mA specification, high-voltage insulation is required. In order to secure insulation reliability, the power supply was designed with eight separate transformers that share primary turns. The spacing between the primary turns and secondary turns allows for increases in the leakage inductance and parasitic capacitance of the transformer. A compact design can be achieved using the parasitic component generated by the proposed design as the resonant parameters of the LCC resonant converter. In addition, based on a trapezoidal approximation of the LCC resonant converter, the resonant parameters are designed to have trapezoidal shapes of the resonant current in order to reduce conduction loss. Finally, the results of the experiment indicate that the developed ion pump power supply can be used effectively in high-voltage and low-current applications.
A Link Prediction-Based Method for Identifying Potential Cooperation Partners: A Case Study on Four Journals of Informetrics

Global academic exchange and cooperation have become an increasing trend in both academia and industry, but how to quickly and effectively identify potential partners is becoming an urgent problem. This paper proposes a link prediction-based model to help researchers identify partners from a large collection of academic articles in a given technological area. We initially construct a co-authorship network, and take a series of indices based on network and similarity of researchers into consideration. A fitting model of link prediction is then established, in which logistic regression analysis is involved. An empirical study on four journals of informetrics is conducted to demonstrate the reliability of the proposed method.
Modeling and Maximizing Angle Coverage in Visual Sensor Networks

Maintaining the quality of network coverage is a major concern in visual sensor networks. In this paper, we study the angle coverage problem in visual sensor networks, considering the target is very large, and each camera node can only monitor a portion of its perimeter. The goal of the proposed work is to schedule camera nodes to achieve maximum angle coverage in different period. Firstly, we establish a novel coverage model and formally prove that the problem is NP-hard in general. Next, we present a scheduling scheme based on greedy algorithm, to schedule camera nodes into disjoint cover sets working in turn. Then, we conclude that the approximate ratio of the proposed algorithm is k, theoretically, and the time complexity of the algorithm is 0(n). Finally, extensive simulations have been conducted to evaluate the performance of the proposed algorithm.
Skeleton Feature Fusion Based on Multi-Stream LSTM for Action Recognition

Human action recognition from skeleton sequences has attracted a lot of attention in the computer vision community. Long short term memory (LSTM) network has shown its promising performance for this problem, due to their strengths in modeling the dependencies and temporal dynamics of sequential data. However, original LSTM is difficult to grasp the dynamics of entire sequence data, if the input feature of each time step is just a simple combination of raw skeleton data. In this paper, we present a fusion model to make full use of the skeleton data through multi-stream LSTM for action recognition. In each stream of the model, skeleton feature fed to each step of LSTM are extracted from different time duration, which are called single frame feature, short term feature, and long term feature, respectively. Single frame feature represents static pose, which is converted from joints coordinates directly. Short term feature represents skeleton kinematics, which is extracted from a short time window. Long term feature represents joints mutuality during the action process, which is extracted from a longer time window. All these features are modeled by LSTM, and the final states of LSTM streams are fused to predict the underlying actions. The proposed model makes better use of the skeleton dynamics than standard LSTM model. Experimental results on two benchmark skeleton data sets NTU RGB+D data set and SBU interaction dataset show that our proposed approach achieved significant performance.
Modeling and Dispersion Analysis of Coaxial Lines With Higher Symmetries

In this paper, 1-D periodic structures possessing higher symmetries are proposed and investigated in terms of their dispersion properties. The proposed structures are coaxial lines with coaxial rings periodically loaded on their inner or outer conductors. The higher symmetries, namely, glide and twist symmetries, are obtained by performing an additional geometrical operation within the unit cell of the periodic structure. We demonstrate that the propagating modes exhibit a lower frequency dispersion in higher symmetric coaxial lines. Moreover, the conventional stopbands of periodic structures at their Brillouin zone boundaries can be controlled by breaking the higher symmetry or changing the order of the twist symmetry. A circuit-based analytical method is proposed to calculate the dispersion diagram of the glide-symmetric coaxial lines. The results are validated with a full-wave simulation. Moreover, several prototypes of the twist-symmetric coaxial lines are manufactured and measured. A remarkable agreement is achieved between the measurements and simulations, validating the theoretical results. The proposed structures find potential applications in leaky-wave antennas and fully metallic reconfigurable filters and phase shifters.
The Impact of Physical Activities on the Physiological Response to Emotions

Despite the advantages of using physiological sensors to collect emotion data, emotion recognition systems using physiological signals such as Electrodermal Activity (EDA), Electrocardiogram (ECG) or Electromyography (EMG) are mainly tested in controlled environments or under laboratory conditions. The use of physiological data in real-world scenarios has not been widely investigated. One of the main issues of using physiological data from real-world scenarios is that the data may also be influenced by movement and in some cases, the physiological response to emotions can be even confused with the one due to physical activities, such as walking or running. In this paper, we investigate the impact of physical activities in the recognition of emotions and provide new insights on how emotion data from physiological sensors are affected by these activities. We use two scenarios (one with and one without the influence of physical movement) to investigate the effect of physical activities in the Blood Volume Pulse (BVP) and the Skin Temperature (TMP) signals. To overcome these issues we used a random forest algorithm to model both scenarios. Our results show that by combining emotion data from both scenarios, we can achieve a recognition accuracy of up to 96%.
Transcriptomic Analysis of Shiga Toxin-Producing Escherichia coli FORC_035 Reveals the Essential Role of Iron Acquisition for Survival in Canola Sprouts and Water Dropwort

Enterohemorrhagic Escherichia coli (EHEC) is a foodborne pathogen that poses a serious threat to humans. Although EHEC is problematic mainly in food products containing meat, recent studies have revealed that many EHEC-associated foodborne outbreaks were attributable to spoiled produce such as sprouts and green leafy vegetables. To understand how EHEC adapts to the environment in fresh produce, we exposed the EHEC isolate FORC_035 to canola spouts (Brassica napus) and water dropwort (Oenanthe javanica) and profiled the transcriptome of this pathogen at 1 and 3 h after incubation with the plant materials. Transcriptome analysis revealed that the expression of genes associated with iron uptake were down-regulated during adaptation to plant tissues. A mutant strain lacking entB, presumably defective in enterobactin biosynthesis, had growth defects in co-culture with water dropwort, and the defective phenotype was complemented by the addition of ferric ion. Furthermore, gallium treatment to block iron uptake inhibited bacterial growth on water dropwort and also hampered biofilm formation. Taken together, these results indicate that iron uptake is essential for the fitness of EHEC in plants and that gallium can be used to prevent the growth of this pathogen in fresh produce.
Design and Development of a Compact All-Solid-State High-Frequency Picosecond-Pulse Generator

The Marx circuit structure based on fast high-power semiconductor devices is the classical way of generating picosecond pulses. However, most reported studies suggest that as the number of stages in the Marx circuit increases, the output pulse amplitude tends to saturate. This has of late made high-voltage picosecond-pulse generation a challenge. In this paper, as the number of stages increases, the internal resistance of the avalanche transistor tends to decrease with increasing current flowing through the avalanche transistor. A step-by-step wiring and debugging of the avalanche transistor-based Marx circuit demonstrates this experimentally. That is, for a certain range in the number of stages, saturation of the output pulse amplitude for the Marx circuit of avalanche transistors is not apparent. Introducing a parallel structure for the transistors not only increases the current level of the entire circuit but also further reduces the equivalent resistance of the avalanche transistors, thereby improving the output efficiency. Drawing on microstrip transmission theory, and combining the Marx circuit and avalanche transistor parallel structure, component parameters and circuit topology of the picosecond-pulse generator were redesigned, and a 30-level Marx circuit with a parallel structure of two avalanche transistors was developed. The picosecond-pulse generator outputs a pulse with adjustable voltage amplitude of 0.9–3.1 kV, a 350-ps full-width at half-maximum, a 150-ps rise time, and an adjustable high-stability repetition rate of up to 10 kHz. The number of pulses is precisely controlled. The generator is an all-solid-state compact device with high frequency suitable for research needs.
Wave Compensator Design Based on Adaptive FFT Prediction Algorithm and H∞ filtering

As the performance of the unmanned aerial vehicle (UAV) has been greatly improved with the rapid development of science and technology, the UAVs have been widely used for a broad range of applications. In recent years, the shipborne UAV has received considerable attention due to its low cost, small size, and high cost-effectiveness ratio. A key difficulty for the shipborne UAV is that due to the effect of the waves, the vertical sway, horizontal sway, and heave motions of the ships brought great challenges to the safe take-off and landing of the shipborne UAVs. To circumvent this drawback, this study proposed a wave compensation system based on the adaptive fast Fourier transform (FFT) prediction algorithm and H∞ filtering. On the one hand, in order to reduce the time delay errors caused by sensors, controllers, and other devices, the FFT was adopted to construct an adaptive wave prediction algorithm. With the help of this algorithm, the heave motion model of the waves is forecasted to compensate the waves as synchronously as possible. On the other hand, in order to improve the robustness of the system and reduce external disturbances, the H∞ filter was used to eliminate the high-frequency wave interference and make the compensation platform move more smoothly. Using the control and computation simulation software Matlab and the visual scene simulation software Vortex, based on the adaptive FFT prediction algorithm and H∞ filtering, the co-simulation platform was constructed for the take-off and landing structural platform, the three-degree-of-freedom wave simulation, and the six-degrees-of-freedom wave compensation. The examples presented demonstrated that the proposed wave compensation system has feasibility in the take-off and landing of the shipborne UAVs. The examples also verified that the adaptive FFT prediction algorithm could forecast the waves effectively, and H∞ filter has a good filtering effect in the wave compensation.
Attachment as a Predictor of University Adjustment among Freshmen: Evidence from a Malaysian Public University.

Purpose – Building upon attachment theory and emerging theory, the current study was aimed at examining the effect of peer attachment in predicting adjustment to life in university among freshmen in a public unirvsity in East Malaysia. Furthermore, it sought to examine the influence of gender and perceived-adult status as moderators of the relationship between student attachment and student adjustment. Methodology – Data was collected from 557 freshmen in one of the public universities in East Malaysia. Two questionnaires, namely The Inventory of Parent and Peers Attachment (IPPA) and The Student Adaptation to College Questionnaire (SACQ) were used in this study. Partial Least Square (PLS) analysis was employed to examine the hypothesized relationships. Findings – The findings of the study showed that peer trust positively influenced academic and social adjustment. Meanwhile, peer communication positively influenced social adjustment, but negatively influenced personal-emotional adjustment. Lastly, peer alienation negatively influenced personal-emotional adjustment, but positively influenced institutional attachment. The Partial Least 112 Malaysian Journal of Learning and Instruction: Vol. 14 No. 1 (2017): 111-144 Square Multi Group Analysis (PLS-MGA) results indicated no significant differences in peer attachment and university adjustment across gender and perceived-adult status. Significance – The findings of this study has empirically proven the link between attachment theory and emerging adulthood theory, providing further corroborative evidence for the argument put forth by Howes and Spieker (2008) on the alternate attachment bond during emerging adulthood period. In terms of practical benefits,, several parties who work with emerging adult clients in the Malaysian university setting will be able to benefit from the research findings, particularly counsellors and academicians.
A Study of Elementary School Teachers' Intention to Use Multimedia Materials Based on the Technology Acceptance Model-A Case Study of Elementary Schools in Chiayi County

This study applied the Technology Acceptance Model (TAM) to explore the effects of the IT environment on the usefulness of cognition, usability of cognition, use attitude of teachers of multimedia teaching materials in teaching, and the relevance and influence of attitudes on behavioral intention. The stratified random sampling was used. The results showed that multimedia materials enhance teaching quality and convey class content effectively.
Towards Understanding and Refining the General Program Synthesis Benchmark Suite with Genetic Programming

Program synthesis is a complex problem domain tackled by many communities via different methods. In the last few years, a lot of progress has been made with Genetic Programming (GP) on solving a variety of general program synthesis problems for which a benchmark suite has been introduced. While Genetic Programming is capable of finding correct solutions for many problems contained in a general program synthesis problems benchmark suite, the actual success rate per problem is low in most cases. In this paper, we analyse certain aspects of the benchmark suite and the computational effort required to solve its problems. A subset of problems on which GP performs poorly is identified. This subset is analysed to find measures to increase success rates for similar problems. The paper concludes with suggestions to refine performance on program synthesis problems.
An Approach for Noninvasive Blood Glucose Monitoring Based on Bioimpedance Difference Considering Blood Volume Pulsation

Noninvasive blood glucose monitoring (NBGM) provides a promising solution for patients with diabetes with the advantages of painless and continuous monitoring. To better characterize the response of glucose to radio-frequency (RF) signals at low frequencies, the conductivity and relative permittivity of aqueous solutions with different glucose concentrations were obtained by the use of an impedance analyzer in the frequency range of 1 kHz to 1 MHz. Furthermore, considering the blood volume pulsation in cardiac cycle, a new approach based on measuring bioimpedance was presented for NBGM in this paper. For this purpose, an inhomogeneous arm model, which consists of three tissue layers (i.e., blood, blood vessel, and other relevant tissues), was proposed to validate the aforementioned approach. Furthermore, the measurements were carried out by means of in vitro experiment and in vivo studies, respectively. The results showed that as the glucose concentration increased, the conductivity of aqueous solutions decreased when the frequency of RF signal was below 1 MHz. However, the relative permittivity was almost insensitive to glucose concentration. The simulation result of the arm model showed that as the glucose concentration increased, the bioimpedance difference of blood volume decreased. This was supported by both in vitro and in vivo experiments. We therefore suggest that the proposed approach for NBGM has potentials in practical applications.
Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis

Uveitis is the inflammation of the uvea and is a serious eye disease that can cause blindness for middle-aged and young people. However, the pathogenesis of this disease has not been fully uncovered and thus renders difficulties in designing effective treatments. Completely identifying the genes related to this disease can help improve and accelerate the comprehension of uveitis. In this study, a new computational method was developed to infer potential related genes based on validated ones. We employed a large protein–protein interaction network reported in STRING, in which Laplacian heat diffusion algorithm was applied using validated genes as seed nodes. Except for the validated ones, all genes in the network were filtered by three tests, namely, permutation, association, and function tests, which evaluated the genes based on their specialties and associations to uveitis. Results indicated that 59 inferred genes were accessed, several of which were confirmed to be highly related to uveitis by literature review. In addition, the inferred genes were compared with those reported in a previous study, indicating that our reported genes are necessary supplements.
Towards an In-Memory Reconfiguration of Arithmetic Logical Unit using Memristor Crossbar Array

Current von-Neumann computing technology demands high capacity storage and high communication bandwidth to access data between storage and processing units. Memristor is a promising device that has the desirable properties using which in-memory computing can be performed in the storage unit. These properties of memristors can overcome the bottleneck faced by current von-Neumann architecture. In this paper, we provide a reconfigurable implementation of a 1-bit Arithmetic Logical Unit (ALU) using the memristor crossbar array. Enabling the configuration in system hardware will provide the functionality to use the same module of the system for multiple purposes and change the functionality of the module as per the requirement. The proposed design of the ALU provides functionality to perform the operations in the single module (memristor crossbar) and it is capable to add new operations as per the requirements. For the design of such ALUs, we use the MAGIC NOT and NOR gates. To validate the design we perform SPICE simulation of a half adder using Cadence Virtuoso.
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs

The extension of image generation to video generation turns out to be a very difficult task, since the temporal dimension of videos introduces an extra challenge during the generation process. Besides, due to the limitation of memory and training stability, the generation becomes increasingly challenging with the increase of the resolution/duration of videos. In this work, we exploit the idea of progressive growing of Generative Adversarial Networks (GANs) for higher resolution video generation. In particular, we begin to produce video samples of low-resolution and short-duration, and then progressively increase both resolution and duration alone (or jointly) by adding new spatiotemporal convolutional layers to the current networks. Starting from the learning on a very raw-level spatial appearance and temporal movement of the video distribution, the proposed progressive method learns spatiotemporal information incrementally to generate higher resolution videos. Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to improve the distribution learning on the video data of high-dimension and mixed-spatiotemporal distribution. SWGAN loss replaces the distance between joint distributions by that of one-dimensional marginal distributions, making the loss easier to compute. We evaluate the proposed model on our collected face video dataset of 10,900 videos to generate photorealistic face videos of 256x256x32 resolution. In addition, our model also reaches a record inception score of 14.57 in unsupervised action recognition dataset UCF-101.
Foot-Ground Contact Force During Quadruped Locomotion Using CPG Control

Central Pattern Generator (CPG) is able to produce rhythmic signals without sensory feedback or any rhythmic inputs, and is becoming popular for locomotion of quadruped robots. However, due to the lack of foot-ground contact force information, the swing phase and the stance phase of control signals generated by CPG are not consistent with the actual phase of the quadruped limb, so that the dynamic performance of quadruped robots controlled by CPG is not satisfying. Therefore, in this paper, force sensor is attached on the quadruped limb to detect the real-time contact force of foot with the substrate, and the actual contact-lift state of the limb. In addition, we analyze the characteristic of ground reaction force (GRF), and the relationship between GRF and the detected foot-ground contact force. Moreover, we discuss the effect of different CPG parameters on the foot-ground contact force, so as to lay basis for improving the dynamic performance of quadruped robots.