Miracle-dz/UniFAR-FTUs huggingface dataset:
The Sustainability of Knowledge-Sharing Behavior Based on the Theory of Planned Behavior in Q&A Social Network Community

School of Economics and Management, Yanshan University, Qinhuangdao 066004, China Business School, University of International Business and Economics, Beijing 100029, China Huaxin College, Hebei GEO University, Shijiazhuang 050031, China School of Language and Culture, Hebei GEO University, Shijiazhuang 050031, China Miami University, Middletown 45042, OH, USA School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
A Reconfigurable Multiple-Precision Floating-Point Dot Product Unit for High-Performance Computing

There is an emerging need to optimize floating-point (FP) dot product units (DPU) for high-performance scientific computing as well as training deep learning models. Due to different precision requirements of applications, a reconfigurable multiple-precision DPU operation can largely reduce the cost of area and power. However, the existing methods could result in redundant bits for unit multipliers, but also leave idle hardware resources for the operations in different precisions. In this paper, a reconfigurable multiple-precision FP DPU design is proposed for high-performance computing (HPC) applications. The FP DPU can be reconfigured as follows. A bit-partitioning method is provided to minimize the redundant bits with a configurable mixed-precision multiplier for three-mode operations: 20 half-precision Dot Product (DP), 5 single-precision DP, and 1 double-precision DP operations. Any of the modes can be executed in two successive clock cycles without idle hardware resources. The proposed design is realized by using the UMC 55-nm process with simulation results. Compared with the existing multiple-precision FP methods, the proposed DPU achieves 88.9% and 35.8% area-saving performance for FP16 and FP32 operations, respectively. Moreover, when using benchmarked HPC applications where multiple precisions can be used, the proposed reconfigurable DPU can accelerate up to 4× and 20× maximum throughput rates when compared with fixed FP32 and FP64 operations, respectively.
Composition of Dynamic Control Objectives Based on Differential Games

We present a divide and conquer design approach for handling control problems with dynamically changing objectives. The approach is based on associating each objective with a virtual input and then considering a game between a set of (virtual) players, each trying to achieve an objective by controlling the associated virtual input. By guarantying a Nash equilibrium for this game, we synthesize a combined controller that establishes a stable balance between the objectives. To demonstrate the use of the approach, we describe how a controller for a non-linear system (quadrotor) can be designed to handle competing objectives that change in time. The example shows how our approach allows to model each of the objectives separately and to combine them using game theory.
Design and Implementation of a Re-Configurable Versatile Direct Digital Synthesis-Based Pulse Generator

Direct digital synthesis (DDS) technology has replaced many traditional analog circuit functions. The frequency, amplitude, and phase of a waveform generated using DDS can be precisely controlled. It is possible to achieve remarkably high-frequency resolution, fast frequency switching, and modulation can be easily introduced in a DDS generator. However, pulse waveform generated using DDS requires re-loading of the pulse waveform in the waveform memory to vary the pulse parameters, which causes phase discontinuity, requires prohibitively large memory to generate pulses with narrow widths and long periods, and suffers from trigger uncertainty of up to 1 clock period when the pulse waveform is triggered by an external event. Moreover, it is not possible to modulate the width and delay of the pulse stored in the waveform memory. This article describes a fully functional, low complexity, low cost, memory less, parallel, and DDS-based pulse waveform generator implemented in a low-cost field programmable gate arrays (FPGA) where pulse parameters such as amplitude, period, width, rise time, fall time, and delay can all be varied without any glitches or phase discontinuity, where the amplitude, frequency and phase, as well as period, width and delay of the pulse can be modulated by an internally generated or external modulating signal and where it is possible to trigger the generation of the pulse or burst of pulses by an external event where the time between the trigger event and the pulse output is fixed.
Research on Inconsistency Identification of Lithium-ion Battery Pack Based on Operational Data

Lithium-ion batteries have been widely used in the field of energy storage, due to the high energy density, wide temperature range and long service life. However, in application, the parameters such as the capacity and voltage of each cell in the battery pack are inconsistent due to unreasonable use, poor operating environment and other factors. In this paper, the qualitative relationship between the voltage variation of lithium-ion battery and the cell capacity and SOC is firstly analyzed, and then an inconsistent cell identification method is proposed based on three feature parameters of voltage outliers, voltage range and voltage difference. The static component is obtained from the voltage in the unstable discharge data by empirical mode decomposition and the inconsistency of capacity and SOC of static components is analyzed. The online identification of inconsistency of lithium-ion battery pack is realized based on the threshold and $K$-means algorithm.
Hierarchical Clustering Detection Based Secure Fusion Filtering for Multiple False Data Injection Attacks

This paper concerns the secure fusion filtering under multiple false data injection attack scenarios. A hierarchical clustering based secure fusion filtering approach is proposed for the remote-measuring system with a known secure node. The fusion filtering approach includes two parts. In the local filtering part, the remote measurement is utilized to estimate the system sate locally, after detected by Chi-square detector. In the distributed fusion part, a novel hierarchical clustering detection based distributed fusion method is provided to obtain the final fusion filtering results. The simulation results are presented to illustrate the effectiveness of the proposed approach.
Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities

In artificial intelligence, deep learning (DL) is a process that replicates the working mechanism of the human brain in data processing, and it also creates patterns for decision making. Deep learning or neural networks have been deployed in several fields, such as computer vision, natural language processing, and speech recognition. It has been used in many healthcare applications for the diagnosis and treatment of many chronic diseases. These algorithms have the power to avoid outbreaks of illness, recognize and diagnose illnesses, minimize running expenses for hospital management and patients. This paper discusses the deep learning methods used in different healthcare fields, i.e., identifying depression, heart diseases, physiological signals, lymph node metastases from breast cancer, etc. These diseases are categorized into the central nervous system, cardiovascular system, and respiratory system. For each category, after summarizing the studies, comparison tables are laid down using some important factors. Different applications, tools, methods, and data sets used for DL models are leveraged. Finally, research opportunities and challenges being faced for deep learning models are discussed.
Automated net-shape preforming of MAAXIMUS C73 frames

High-performance composite structures are increasingly used in today’s airframes due to the excellent weight-specific mechanical performance. The announced ramp-up for next- generation aircrafts demands the next step in composite manufacturing in order to produce parts with excellent geometrical accuracy in high-rate processes. Within the European FP7 project MAAXIMUS a side shell structure was chosen as demonstrator. As part of MAAXIMUS fuselage panel, two door-surrounding frames have been manufactured by an automated preforming cell which is part of a fully automated Resin-Transfer-Molding (RTM) production line
OnlineHD: Robust, Efficient, and Single-Pass Online Learning Using Hyperdimensional System

Hyper-Dimensional computing (HDC) is a brain-inspired learning approach for efficient and robust learning on today's embedded devices. HDC supports single-pass learning, where it generates a classification model by one-time looking at each training data point. However, the single-pass model provides weak classification accuracy due to model saturation caused by naively accumulating high-dimensional data. Although the retraining model for hundreds of iterations addresses the model saturation and boosts the accuracy, it comes with significant training costs. In this paper, we propose OnlineHD, an adaptive HDC training framework for accurate, efficient, and robust learning. During single-pass training, OnlineHD identifies common patterns and eliminates model saturation. For each data point, OnlineHD updates the model depending on how similar it is to the existing model, instead of naive data accumulation. We expand the OnlineHD framework to support highly-accurate iterative training. We also exploit the holographic distribution of patterns in high-dimensional space to make OnlineHD ultra-robust against possible noise and hardware failure. Our evaluations on a wide range of classification problems show that OnlineHD adaptive training provides comparable classification accuracy to the retrained model while getting all efficiency benefits that a single-pass training provides. OnlineHD achieves, on average, 3.5× and 6.9× (3.7× and 5.8×) faster and more efficient training as compared to state-of-the-art machine learning (HDC algorithms), while providing similar classification accuracy and 8.5× higher robustness to a hardware error.
The Signal Effect of New Energy Vehicles Promotion on Enterprise Innovation

Entering the promotion catalogue is the basis for China’s new energy vehicle (NEV) enterprises to obtain promotion subsidies. Using a multistage difference-in-differences (DID) model with a sample of listed NEV enterprises in China from 2008 to 2017, this paper empirically analyzes the impact of entering the promotion catalogue on the innovation of NEV enterprises. (e results indicate that entering the promotion catalogue can significantly promote innovation of NEV enterprises from the perspective of radical innovation, incremental innovation, and external technology introduction of NEV enterprises. In the mediation mechanism examination, this study finds that entering the promotion catalogue promotes innovation in NEV enterprises by increasing corporate profits and easing financing constraints. Based on the conclusions above, this paper recommends to strengthen core technology breakthrough of NEVs, cultivate market demand of NEVs industry, and improve subsidy supervision system of NEVs.
On the Design of Sidelink for Cellular V2X: A Literature Review and Outlook for Future

Connected and fully automated vehicles are expected to revolutionize our mobility in the near future on a global scale, by significantly improving road safety, traffic efficiency, and traveling experience. Enhanced vehicular applications, such as cooperative sensing and maneuvering or vehicle platooning, heavily rely on direct connectivity among vehicles, which is enabled by sidelink communications. In order to set the ground for the core contribution of this paper, we first analyze the main streams of the cellular-vehicle-to-everything (C-V2X) technology evolution within the Third Generation Partnership Project (3GPP), with focus on the sidelink air interface. Then, we provide a comprehensive survey of the related literature, which is classified and critically dissected, considering both the Long-Term Evolution-based solutions and the 5G New Radio-based latest advancements that promise substantial improvements in terms of latency and reliability. The wide literature review is used as a basis to finally identify further challenges and perspectives, which may shape the C-V2X sidelink developments in the next-generation vehicles beyond 5G.
How to Conduct Rigorous Supervised Machine Learning in Information Systems Research: The Supervised Machine Learning Report Card

Within the last decade, the application of supervised machine learning (SML) has become increasingly popular in the field of information systems (IS) research. Although the choices among different data preprocessing techniques, as well as different algorithms and their individual implementations, are fundamental building blocks of SML results, their documentation—and therefore reproducibility—is inconsistent across published IS research papers. This may be quite understandable, since the goals and motivations for SML applications vary and since the field has been rapidly evolving within IS. For the IS research community, however, this poses a big challenge, because even with full access to the data neither a complete evaluation of the SML approaches nor a replication of the research results is possible. Therefore, this article aims to provide the IS community with guidelines for comprehensively and rigorously conducting, as well as documenting, SML research: First, we review the literature concerning steps and SML process frameworks to extract relevant problem characteristics and relevant choices to be made in the application of SML. Second, we integrate these into a comprehensive “Supervised Machine Learning Reportcard (SMLR)” as an artifact to be used in future SML endeavors. Third, we apply this reportcard to a set of 121 relevant articles published in renowned IS outlets between 2010 and 2018 and demonstrate how and where the documentation of current IS research articles can be improved. Thus, this work should contribute to a more complete and rigorous application and documentation of SML approaches, thereby enabling a deeper evaluation and reproducibility / replication of results in IS research.
Future Computing Platform Design: A Cross-Layer Design Approach

Future computing platforms are facing a paradigm shift with the emerging resistive memory technologies. First, they offer fast memory accesses and data persistence in a single large-capacity device deployed on the memory bus, blurring the boundary between memory and storage. Second, they enable computing-in-memory for neuromorphic computing to mitigate costly data movements. Due to the non-ideality of these resistive memory devices at the moment, we envision that cross-layer design is essential to bring such a system into practice. In this paper, we showcase a few examples to demonstrate how cross-layer design can be developed to fully exploit the potential of resistive memories and accelerate its adoption for future computing platforms.
Microsegmented Flow-Through Synthesis of Silver Nanoprisms with Exact Tunable Optical Properties

In this work, a multistep microcontinuous flow-through synthesis procedure for the generation of homogeneous, high-quality silver nanoprisms is presented. The particle synthesis is based on the wet chemical reduction of silver nitrate in the presence of the polyanionic polymer poly(sodium styrenesulphonate). To obtain a high yield of homogeneous prism-shaped Ag nanoparticles with a triangular base, two main experimental steps are necessary. The first step is the synthesis of seed particles. To match the quality criteria for small, homogeneous seed particles, the synthesis was carried out in a microcontinuous flow-through system. Constant residence times and an effective mixing of the reactants were realized by the application of the microsegmented flow technique. The advantage of good reactant mixing was also adapted in the second experimental step. The growth of silver nanoprisms by reduction of silver nitrate on the noncapped surfaces of the seed particles was again carried out within microfluid segment.
Influence of Al Doping on the Properties of ZnO Thin Films Grown by Atomic Layer Deposition

Aluminum-doped zinc oxide (AZO) films were grown by atomic layer deposition at 200 °C. The influence of Al content on the structure, optical, and electrical properties of AZO films was investigated. The X-ray diffraction spectra revealed that the grown ZnAlO films have a hexagonal structure with a preferential c-axis orientation perpendicular to the substrate surface. Furthermore, a 0.1° peak shift at a diffraction angle of 34.5° from the wurtzite structure to higher values was observed after Al doping. The X-ray photoelectron spectra of AZO film showed that Zn exists only in the oxide states with an oxygen-deficient ZnO1-x matrix. In photoluminescence studies, the intensity of the dominant peak at ∼380 nm was found to decrease with increasing Al doping concentration. In addition, a minimum resistivity of 9.36 × 10–4 Ωcm was obtained for the AZO film with 2.7 at. % Al.
A Dynamic System Builder and Simulator User ’ s Guide-Version 1 . 0

Scicos (Scilab Connected Object Simulator) is a Scilab package for modeling and simulation of hybrid dynamical systems. More specifically, Scicos is intended to be a simulation environment in which both continuous systems and discrete systems co-exist. Unlike many other existing hybrid system simulation softwares, Scicos has not been constructed by extension of a continuous simulator or of a discrete simulator; Scicos has been developed based on a formalism that considers both aspects from the beginning. Scicos includes a graphical editor which can be used to build complex models by interconnecting blocks which represent either predefined basic functions defined in Scicos libraries (palettes), or user defined functions. A large class of hybrid systems can be modeled and simulated this way. Key-words: Nonlinear simulation, hybrid systems, block-diagram modeling, dynamic systems.
Fuzzy Explicit Window Adaptation : a Method to Further Enhance TCP Performance

In the current Internet, standard TCP congestion control is based on segment loss events possibly assisted by simple explicit router feedback mechanisms like Explicit Congestion Notification (ECN). The performance of TCP congestion control can be improved if a more sophisticated Router Congestion Feedback (RCF) mechanism than ECN is used. One of such approaches is Explicit Window Adaptation (EWA). EWA uses TCP’s built-in flow control mechanism to inform TCP senders about the current load in a network path. To reach this, an EWA-capable router is able to decrease the advertised receiver window in TCP acknowledgments if necessary in order to avoid congestion and packet losses. Since the EWA algorithm performed in a router has some shortcomings under certain conditions, a new EWA-related approach is developed. This approach, called fuzzy explicit window adaptation (FEWA), replaces parts of the EWA algorithm in a router with a more accurate fuzzy-based calculation. In this technical report, FEWA is described in detail. Using two simulation scenarios with different load in the network, the performance of standard TCP is compared with the performances of standard TCP assisted by EWA and standard TCP assisted by FEWA. The simulation results show that standard TCP assisted by FEWA outperform both other approaches in throughput over loaded routers and considerably decreases the number of packet losses in congested routers.
Recent Titles from the Tr-inf-unipmn Technical Report Series Supporting Human Interaction and Human Resources Coordination in Distributed Knowledge-free Scheduling Algorithms for Multiple Bag-of-task Applications on Case-based Reasoning Workshop Proceedings Volume Editor Welcoming You, from Left to 

AI agents designed for real-time settings need to adapt themselves to changing circumstances to improve their performance and remedy their faults. Agents typically designed for computer games, however, lack this ability. The lack of adaptivity causes a break in player experience when they repeatedly fail to behave properly in circumstances unforeseen by the game designers. In this paper, we focus on an AI technique for game playing agents that helps them adapt to changing game circumstances. The agents carry out runtime adaptation of their behavior sets by monitoring and reasoning about their behavior execution and use this reasoning to dynamically carry out revisions on the behaviors. The evaluation of the behavior adaptation approach in a complex realtime strategy game shows that the agents adapt themselves and improve their performance by revising their behavior sets appropriately.
Antidiabetic activity of silver nanoparticles from green synthesis using Lonicera japonica leaf extract

In this work we report the green synthesis of silver nanoparticles (AgNPs) using the aqueous leaf extract of Lonicera japonica. A color change from pale yellow to brown was observed during the synthesis process. Ultraviolet-visible (UV-vis) studies show an absorption band at 435 nm due to surface plasmon resonance of the AgNPs, further characterized using the average size and stability of the AgNPs, which were 53 nm and −35.6 mV as determined by a zeta sizer. The spherical, hexagonal shape and the face centered cubic crystalline structure of metallic silver were confirmed by high resolution transmission electron microscopy (HR-TEM) and X-ray diffraction (XRD). The functional group of biomolecules present in the AgNPs was characterized by Fourier transform infrared spectra (FTIR). The synthesized AgNPs exhibited strong antioxidant activity. The antidiabetic ability of AgNPs was shown by the effective inhibition against carbohydrate digestive enzymes such as α-amylase and α-glucosidase with IC50 values of 54.56 and 37.86 μg mL−1, respectively. The inhibition of the kinetic mechanism was analyzed with LB and Dixon plots. AgNPs were identified to be reversible noncompetitive inhibitors and Ki values of 25.9 and 24.6 μg were found for key enzymes of diabetes (α-amylase and α-glucosidase). We conclude that the results suggest that the AgNPs were found to show remarkable potential antidiabetic activity against the key enzymes of diabetes and were found to be an appropriate nanomedicine for nanobiomedical applications.
Highly Fluorescent [7]Carbohelicene Fused by Asymmetric 1,2-Dialkyl-Substituted Quinoxaline for Circularly Polarized Luminescence and Electroluminescence

A new 1,2-dialkylquinoxaline-fused [7]carbohelicene ([7]Hl-NAIQx) was designed and synthesized by asymmetrically introducing two alkyl chains onto the quinoxaline unit. Direct alkylation of the quinoxaline ring of quinoxaline-fused helicene leads to discontinuity in the conjugated structure. In the single-crystal analysis, the parent quinoxaline-fused [7]carbohelicene ([7]Hl-Qx) was found to have a helical structure formed by two phenanthrene units and a nonplanar twisted angle between the phenanthrene and quinoxaline units. In contrast, [7]Hl-NAIQx possesses an almost planar aromatic structure between the alkyl-quinoxaline and phenanthrene units (torsion angle: 179°), in addition to the similar helical structure between the two phenanthrene units. The steady-state absorption, fluorescence, and circular dichroism (CD) spectra of [7]Hl-NAIQx were significantly red-shifted compared to those of [7]Hl-Qx and [7]carbohelicene ([7]Hl). These spectral changes were mainly explained by electrochemical measurements.