Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation Runtime Prediction Approach based on Stacking Ensemble Learning

Many technologies in complex system simulation (CSM), such as resource scheduling and load balancing, largely rely on historical data with different characteristics to predict the future. The accuracy of runtime prediction has a significant impact on scheduling performance. However, with cloud computing becoming the main infrastructure for deploying CSM applications, the current prediction methods are difficult to adapt to the dynamic changes of cloud computing resources. Insufficient computing resource allocation will be difficult to support the efficient operation of simulation. In addition, excessive computing resource allocation will lead to higher computing and data communication costs. Therefore, a simulation runtime prediction approach based on stacking ensemble learning has been proposed, which uses the characteristic variables of simulation applications (such as the number of simulation entities, the number of simulation events, the simulation time, etc.) and the performance monitoring data of computing resources (such as CPU utilization, memory utilization, etc.) as the characteristic inputs. The machine learning algorithms such as XBG, SVG, MLP are integrated by stacking model, and the performance of the integrated learning algorithm is comprehensively evaluated by mean absolute error (MAE), accuracy (ACC), root mean square error (RMSE) and coefficient of determination (R2). Experimental results show that the proposed algorithm improve the prediction accuracy by 3% 24% when compared with existing machine learning prediction methods.
Compositionality of Complex Graphemes in the Undeciphered Proto-Elamite Script using Image and Text Embedding Models

We introduce a language modeling architecture which operates over sequences of images, or over multimodal sequences of images with associated labels. We use this architecture alongside other embedding models to investigate a category of signs called complex graphemes (CGs) in the undeciphered proto-Elamite script. We argue that CGs have meanings which are at least partly compositional, and we discover novel rules governing the construction of CGs. We ﬁnd that a language model over sign images produces more inter-pretable results than a model over text or over sign images and text, which suggests that the names given to signs may be obscuring signals in the corpus. Our results reveal previously unknown regularities in proto-Elamite sign use that can inform future decipherment efforts, and our image-aware language model provides a novel way to abstract away from biases introduced by human annotators.
Annotations Matter: Leveraging Multi-task Learning to Parse UD and SUD

Using multiple treebanks to improve parsing performance has shown positive results. However, to what extent similar, yet competing annotation decisions play in parser behavior is unclear. We investigate this within a multi-task learning (MTL) dependency parser setup on two parallel treebanks, UD and SUD, which, while possessing similar annotation schemes, differ in speciﬁc linguistic annotation preferences. We perform a set of experiments with different MTL architectural choices, comparing performance across various input embed-dings. We ﬁnd languages tend to pattern in loose typological associations, but generally the performance within an MTL setting is lower than single model baseline parsers for each annotation scheme. The main contributing factor seems to be the competing syntactic annotation information shared between tree-banks in an MTL setting, which is shown in experiments against differently annotated tree-banks. This suggests that the impact of how the signal is encoded for annotations and its in-ﬂuence on possible negative transfer is more important than that of the input embeddings in an MTL setting.
A Novel and Dedicated Machine Learning Model for Malware Classification

: Malicious executables are comprised of functions that can be represented in assembly code. In the assembly code mining literature, many software reverse engineering tools have been created to disassemble executables, search function clones, and ﬁnd vulnerabilities, among others. The development of a machine learning-based malware classiﬁcation model that can simultaneously achieve excellent classiﬁcation performance and provide insightful interpretation for the classiﬁcation results remains to be a hot research topic. In this paper, we propose a novel and dedicated machine learning model for the research problem of malware classiﬁcation. Our proposed model generates assembly code function clusters based on function representation learning and provides excellent interpretability for the classiﬁcation results. It does not require a large or balanced dataset to train which meets the situation of real-life scenarios. Experiments show that our proposed approach outperforms previous state-of-the-art malware classiﬁcation models and provides meaningful interpretation of classiﬁcation results.
Pharmacokinetic Study on Characteristic Ingredients from Shaoyao before and after Compatibility

OBJECTIVE To study compatibility rationality of Shaoyao-gancao decoction.METHODS Rats were orally administered with Shaoyao-gancao decoction and Shaoyao decoction.Blood samples at different times were collected,then were analysed by HPLC fingerprints technology.The pharmacokinetic parameters of characteristic peaks were analyzed by SPSS13.0 software.RESULTS The absorption of these compounds in Shaoyao decoction were increased by coadministration with Gancao.The pharmacokinetic parameters such as AUC,ρmax were increased.CONCLUSION The compatibility connotation of Shaoyao-gancao decoction was revealed by an the angle of pharmacokinetics.The absorptions of characteristic peaks from Shaoyao were increased distinguished after co-administration with Gancao.
Determining on-shelf availability based on RGB and ToF depth cameras

In this paper, a method of calculating the occupancy of a shelf will be presented. A vision pillar composed of two RGB cameras and two ToF depth cameras will be used to scan a shelf and determine the percentage of emptiness for the products. Since the cameras will scan a small section of the shelf at the time, a stitching method will be applied on both depth and RGB stream. In order to identify categories of products, along with their spatial delimitation within a shelf, a segmentation neural network is used. By combining the segmentation output with the depth information, the distance from the pillar to the product can be determined. Since this type of information does not help a human operator or a supervisor in understanding the state of the shelf or in making an appropriate decision, such as restocking, a method for computing the shelf occupancy is proposed. Optimization must be done to the processing algorithms to enable them to run on an embedded platform, which will allow for the implementation of the proposed architecture on a platform deployed in a production environment. All the computation is done on board of the mobile robot without transmitting the data to an external server.
38GHz Rectangular Microstrip Antenna with DGS for 5G Applications

the rectangular microstrip antenna with inset feed operating at 38GHz was designed and analyzed using Computer Simulation Technology CST software. The antenna has a relatively compact size, with Rogers Duroid 5880 as substrate material and dielectric constant 2.2 with a height of 0.208 mm. The dimensions of the Defected Ground Structure (DGS) and its position were implemented experimentally with the help of CST. The bandwidth of the microstrip antenna was limited so the purpose of (DSG) is to get a wider bandwidth with maintaining acceptable values of efficiency and return loss. The final values of antenna gain, bandwidth, efficiency, return loss, and VSWR, which have been obtained from the simulation are 6.85 dBi, 2.146 GHz, 95.4%, - 40.54 dB, and 1.02 respectively. This type of antennas can find applications in 5G within the mm-Wave band where it is necessary to maintain high rates of Gbps.
Joint Cache Size Scaling and Replacement Adaptation for Small Content Providers

Elastic Content Delivery Networks (Elastic CDNs) have been introduced to support explosive Internet traffic growth by providing small Content Providers (CPs) with just-in-time services. Due to the diverse requirements of small CPs, they need customized adaptive caching modules to help them adjust the cached contents to maximize their long-term utility. The traditional adaptive caching module is usually a built-in service in a cloud CDN. They adaptively change cache contents using size-scaling-only methods or strategy-adaptation-only methods. A natural question is: can we jointly optimize size and strategy to achieve tradeoff and better performance for small CPs when renting services from elastic CDNs? The problem is challenging because the two decision variables could involve both discrete and categorical variables, where discrete variables have an intrinsic order while categorical variables do not. In this paper, we propose a distribution-guided reinforcement learning framework JEANA to learn the joint cache size scaling and strategy adaptation policy. We design a distribution-guided regularizer to keep the intrinsic order of discrete variables. More importantly, we prove that our algorithm has a theoretical guarantee of performance improvement. Trace-driven experimental results demonstrate our method can improve the hit ratio while reducing the rental cost.
Security for a Multi-Agent Cyber-Physical Conveyor System using Machine Learning

One main foundation of Industry 4.0 is the connectivity of devices and systems using Internet of Things (IoT) technologies, where Cyber-physical systems (CPS) act as the backbone infrastructure based on distributed and decentralized structures. This approach provides significant benefits, namely improved performance, responsiveness and reconfigurability, but also brings some problems in terms of security, as the devices and systems become vulnerable to cyberattacks. This paper describes the implementation of several mechanisms to increase the security in a self-organized cyber-physical conveyor system, based on multi-agent systems (MAS) and build up with different individual modular and intelligent conveyor modules. For this purpose, the JADE-S add-on is used to enforce more security controls, also an Intrusion Detection System (IDS) is created supported by Machine Learning (ML) techniques that analyses the communication between agents, enabling to monitor and analyse the events that occur in the system, extracting signs of intrusions, together they contribute to mitigate cyberattacks.
Analyzing the Performance Limits of Articulated Soft Robots Based on the ESPi Framework: Applications to Damping and Impedance Control

In situations of harsh impacts, damping injection directly on the link of an articulated soft robot is challenging and usually requires high actuator torques at the moment of impact. In this work, we discuss the underlying reasons and analyze the performance limitations arising in the implementation of basic impedance elements, such as springs and dampers, through the elastic structure preserving impedance (ESPi) control framework. Using the insights obtained, we present a way to design impedance controllers with a damping design based on dynamic extensions. Inspired by the design of shock absorbers and the muscle-tendon model, the presented damping layout requires substantially smaller actuator torques in situations where the robot is subject to harsh impacts. The implementation is facilitated through the ESPi control framework resulting in a physically intuitive impedance design. The resulting closed-loop system can be interpreted as an interconnection of passive Euler Lagrange systems, which again, yields a passive system. The design's passive nature ensures stability in the free motion case and enables the robot to interact robustly and safely with its environment. The work focuses on robotic systems with no inertial coupling between the motor and link dynamics. Experimental results, obtained with the presented design on a dedicated series elastic actuator (SEA) test bed, are reported and discussed.
A Review of Deep Reinforcement Learning for Smart Building Energy Management

Global buildings account for about 30% of the total energy consumption and carbon emission, raising severe energy and environmental concerns. Therefore, it is significant and urgent to develop novel smart building energy management (SBEM) technologies for the advance of energy efficient and green buildings. However, it is a nontrivial task due to the following challenges. First, it is generally difficult to develop an explicit building thermal dynamics model that is both accurate and efficient enough for building control. Second, there are many uncertain system parameters (e.g., renewable generation output, outdoor temperature, and the number of occupants). Third, there are many spatially and temporally coupled operational constraints. Fourth, building energy optimization problems can not be solved in real time by traditional methods when they have extremely large solution spaces. Fifthly, traditional building energy management methods have respective applicable premises, which means that they have low versatility when confronted with varying building environments. With the rapid development of Internet of Things technology and computation capability, artificial intelligence technology find its significant competence in control and optimization. As a general artificial intelligence technology, deep reinforcement learning (DRL) is promising to address the above challenges. Notably, the recent years have seen the surge of DRL for SBEM. However, there lacks a systematic overview of different DRL methods for SBEM. To fill the gap, this article provides a comprehensive review of DRL for SBEM from the perspective of system scale. In particular, we identify the existing unresolved issues and point out possible future research directions.
Split Duty Super Boost Converter for High Voltage Applications in a DC Microgrid

In order to establish an appropriate interaction of a renewable DC generating source such as Photovoltaic or Fuel cells with a DC microgrid, there is need of some DC-DC converter with suitable attributes and features. Imparting high voltage at the low duty ratio is the most demanding quality of such converters. This paper introduces and explains the entire features and behavioral assessment of a novel split duty super boost converter (SDSB) involving switched structure of passive components for a partial contribution in the voltage boosting. The proposed converter is capable of attaining the highest voltage gain without any magnetic coupling, multi-staging/leveling, interleaving, or any complex networking. Additional features of the proposed converter includes its suppleness in duty ratio selection from an extended range, lower voltage stress across switches, low conduction losses, simpler and flexible control as well as enhanced reverse recovery of diodes with a continuous input current. The converter involves three switches where a splitting in duty ratio is realized for many possible positive outcomes including very low duty ratio operation of the switches. Theoretical analysis of the converter is elaborated in CCM, DCM along with the boundary conditions. Later, the performance is justified through developing a hardware prototype of 400 W for the input supply of 24 V.
A Family of Dual-Boost Bridgeless Five-Level Rectifiers With Common-Core Inductors

In this article, a family of dual-boost bridgeless five-level rectifiers with common-core inductors is proposed, which is composed of two coupled inductors, one bidirectional switch unit, and the dual-boost bridgeless power factor correction (PFC) rectifier. A bidirectional switch unit is embedded in the midpoint between the two capacitors and the bridge arm of the dual-boost bridgeless PFC (DBBL-PFC) to directly generate the five-level waveforms in each line cycle. The proposed topologies have the characteristics of lower voltage/current stresses and low total harmonic distortion. Additionally, the proposed five-level rectifiers employ a pair of common-core coupled inductors at the input side to replace the inherent independent inductors to improve the core utilization and the power density. First, the characteristics of the proposed topologies are analyzed and compared, and one of the topologies is taken as an example to illustrate its operating principle. Second, the modulation strategy with strong topology applicability and control system is designed for the proposed topologies. The advantage of the proposed pulsewidth modulation method is that it only needs to change the pulse distribution of the five-level topology to realize the five-level rectification, and the program debugging is simple. Then, the coupled inductors are designed, compared, and analyzed by the equivalent model in detail. Finally, a rated output of 1 kW/400 V experimental prototype is built, and the experimental results are presented to demonstrate the performance and effectiveness of the proposed topologies.
Multi-Stage Liver Segmentation in CT Scans Using Gaussian Pseudo Variance Level Set

No single technology can be rich enough to segment accurately due to the challenges of liver segmentation, which include low contrast with neighboring organs and the presence of pathology as well as highly varied shapes between subjects. This paper presents a Multi-stage framework for location and segmentation. First, Faster RCNN is employed to locate the liver region. Then, the Gaussian mixture model-based signed distance function is proposed to increase the flexibility of shape prior models. To reach the long and narrow ravine liver regions, the Gaussian pseudo variance level set is applied. Experimental results demonstrate the efficiency of the proposed method. More specifically, the proposed method is evaluated on 40 CT scan images, which are publicly available on three databases: SLIVER07, 3Dircadb, and LiTS. Our method has a slightly superior performance compared with other newly published methods.
Autoencoder-Based Characterisation of Passive IEEE 802.11 Link Level Measurements

Wireless networks are indispensable in today's industrial manufacturing and automation. Due to harsh signal propagation conditions as well as co-existing wireless networks, transmission failures resulting in severe application malfunctions are often difficult to diagnose. Remote wireless monitoring systems are extremely useful tools for troubleshooting such failures. However, the completeness of data captured by a remote wireless monitor is highly dependent on the temporal, e.g., short-term interference, and spatial characteristics of its environment. It is necessary to first ensure that the data was completely captured at the remote monitor in order to maintain the integrity of the failure analysis, i.e., to avoid false positives. In this paper, we propose an autoencoder-based framework to evaluate the quality of wireless data captured at a remote wireless monitor. The algorithm is trained using data generated under controlled laboratory conditions and validated on testbed as well as realworld measurement data.
A 4Rx, 4Tx Ka-band transceiver in 40nm bulk CMOS technology for satellite terminal applications

This paper presents a K/Ka-band transceiver for phased array satellite mobile ground terminal applications. The transceiver integrates four receiver (Rx) RF paths operating at 17∼21GHz and the four transmitter (Tx) RF paths operating at 27∼30GHz, each with independent 0.5dB gain and 5-bit phase control to support analog RF beamforming. Each Rx RF path is summed in an integrated passive combiner before an IQ frequency down conversion stage driven with a LO frequency doubler. Each Tx RF path is driven from an integrated IQ modulator clocked with an LO frequency tripler with an active power splitter. The Rx and Tx baseband (BB)/IF paths consist of 325MHz wide IQ VGAs and filtering to support up to 650MHz instantaneous bandwidth. The transceiver achieves 3.3dB NF and +10dBm output power per RF path in Rx and Tx modes respectively, whilst consuming 200mW in 4RF+BB Rx mode and 500mW in BB+4RF Tx mode. It is fabricated in 40nm bulk CMOS and occupies 10.7mm2.
Design of a Visual Stimulation Module for a P300-based Brain Computer Interface that uses Pictures of Hand Movements

Brain Computer Interface (BCI) combined with functional electrical stimulation (FES) have shown great potential to enhance motor rehabilitation interventions. P300-based BCIs are generally used for generating commands for various communication and control applications. However, there are few reports of P300-based BCIs targeted for motor assistance or rehabilitation. They focus on classification performance, but do not assess the effects of the visual stimulation interface in EEG signals and evoked potentials. Here, we present the design of a visual stimulation module (VSM) for a P300-based BCI, including pictures of hand movements, arranged in matrix pattern based on the classical speller paradigm. EEG signals were recorded from a healthy subject while attending the movement pictures. Evoked potentials were found in the 350-600 ms window, and in occipital and frontal scalp locations. This was confirmed by topographic r2 maps, and statistical tests comparing amplitude of target and non-target epochs. Although, the VMS requires adjustments and testing with more subjects the results are encouraging. Future work includes a pilot study to assess the effect of pictures of hand movement and the variation in visual stimulation parameters in the VSM.
Multilayered PdTe₂/GaN Heterostructures for Visible-Blind Deep-Ultraviolet Photodetection

Deep-ultraviolet (DUV) photodetection has garnered extensive research interest for its vital applications in many military and civil fields. In this work, we present the synthesis of a large-area two-dimensional (2D) PdTe<sub>2</sub> multilayer, which can be directly transferred onto a GaN substrate to construct a vertical heterostructure for visible-blind DUV photodetection. Upon 265 nm light irradiation, the heterostructure displays a distinct photovoltaic behavior, enabling it to serve as a self-driven photodetector. The important photoresponse parameters, such as <inline-formula> <tex-math notation="LaTeX">${I}_{light}/{I}_{dark}$ </tex-math></inline-formula> ratio, responsivity, specific detectivity and DUV/visible (265 nm/450 nm) rejection ratio reach as high as 10<sup>6</sup>, 168.5 mA/W, <inline-formula> <tex-math notation="LaTeX">$5.3 \times 10^{12}$ </tex-math></inline-formula> Jones, and 10<sup>4</sup>, respectively, at zero bias. The responsivity can be further enhanced to 254.6 mA/W by applying a small reverse bias of −1.0 V. In addition, the photodetector can function as a DUV light image sensor to reliably record an “H” pattern with a decent resolution. The present study paves a way for designing high-performance cost-effective DUV photodetectors towards practical optoelectronic applications.