Miracle-dz/UniFAR-FTUs huggingface dataset:
IATK: An Immersive Analytics Toolkit

We introduce IATK, the Immersive Analytics Toolkit, a software package for Unity that allows interactive authoring and exploration of data visualisation in immersive environments. The design of IATK was informed by interdisciplinary expert-collaborations as well as visual analytics applications and iterative refinement over several years. IATK allows for easy assembly of visualisations through a grammar of graphics that a user can configure in a GUI-in addition to a dedicated visualisation API that supports the creation of novel immersive visualisation designs and interactions. IATK is designed with scalability in mind, allowing visualisation and fluid responsive interactions in the order of several million points at a usable frame rate. This paper outlines our design requirements, IATK's framework design and technical features, its user interface, as well as application examples.
Hierarchical Game Theoretic Design of Frequency Assignment and Channel Selection for General Authorized Accesses

General Authorized Access (GAA) is a registration-based spectrum access authorization and is the bottom tier in FCC's 3-tiered regulatory framework of spectrum sharing. A Spectrum Access System (SAS) manages shared accesses to raise spectrum utilization and meet heterogeneous demands. In managing GAA, a SAS announces spectrum availability and coordinate spectrum accesses by various wireless networks. Based on spectrum availability, the operator of each wireless network submits to SAS a channel selection (CS) plan that maximizes the total capacity for users. Knowing their channel selection plans, the SAS then makes frequency assignments (FA) to individual operators to maximize total capacity of shared accesses while complying with interference management requirements among tiers. This paper formulates FA and CS as a leader-follower game and presents a novel design for SAS to coordinate GAA among operators by exploiting the game theoretic solution concept.
Leadership and Communication in Diverse Environments for Global Top Executives: A Survey of Executive Education at International Business Schools

Since there is improved awareness of the importance of global human resources development at the upper level of organizations, there is a growing demand for developing the top talents across all sectors. To meet the global demand, business schools across the world are stressing on Executive Education, which, according to the Financial Times, has increased by 10% in the 2 years from 2016 to 2017. Business schools are trying to meet the demand mainly by recruiting diverse participants and quality faculty in educational methods. From 2015 to 2018, study data were gathered from 16 business schools across six countries. The survey results on global business schools that focus on Executive Education for senior business leaders are presented. The present study discovered how the business schools improve the senior leaders’ global communication skills to maximize their leadership capabilities in the complex and boundaryless world. This study suggests that Executive Education attracts and develops global top leaders by providing latest education on leadership, strategy, critical thinking, and technology effectively in a diversified environment with quality faculty and global participants.
On the Exploration of Design Tradeoffs in Analog IC Placement with Layout-dependent Effects

At advanced integration nodes, the impact of layout-dependent effects (LDEs) turn the performance of MOSFET devices strongly dependent on the layout implementation details, but also, of its surrounding neighborhood. However, in the traditional design flow, the real LDEs-impact is only known after complete extraction and post-layout simulation, causing re-design iterations with no valuable feedback information to fix the problem. This paper proposes an automatic placement methodology for analog integrated circuit (IC) layout design, that minimizes the mobility and threshold-voltage-related variations caused by the two major sources of LDEs above the 40 nm technology nodes, i.e., well-proximity effect and length of oxide diffusion. An absolute representation of the floorplan is adopted, and, a multi-objective optimization (MOO) algorithm with LDE-impact mitigation operators, is applied. Established LDE formulations used in BSIM models are used to guide placement optimization, shorting the gap between pre and post-layout performance.
SeCoNetBench: A modular framework for Secure Container Networking Benchmarks

Container security, especially in the quest for controlled access and secured communication between containers, has spawned a multitude of implementations, based on various concepts and design choices. They are characterized by very different performance properties, which so far have not comprehensively been benchmarked nor compared in a fair manner. The emerging paradigm of moving execution to edge clouds requires both: efficiency in the light of ephemeral containers and mobility, and security in the face of resource sharing between various tenants at hosts of various providers. In this paper we introduce SeCoNetBench, a modular benchmarking platform for container network security, and compare the most prominent frameworks for access control and network isolation in the container ecosystem. The results demonstrate that trade-offs have to be made during infrastructure deployment, and we provide guidelines for designing high-performance secure container networking platforms in adversarial settings.
Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep Learning

Individual pig tracking is key to stepping away from group-level treatment and towards individual pig care. By doing so we can monitor individual pig behaviour changes over time and use these as indicators of health and well-being, which, in turn, will assist in the early detection of disease allowing for earlier and more effective intervention. However, it is a much more computationally challenging than performing this task at group level; mistakes in identification and tracking accumulate and, over time, provide noise measures. We combine a deep CNN object localisation method, Faster Region-based convolutional neural network (R-CNN), with two potential real-time multi-object tracking methods in order to create a complete system that can autonomously localise and track individual pigs allowing for the extraction of metrics pertaining to individual pig behaviours from RGB cameras. We evaluate two different transfer learning strategies to adapt Faster R-CNN to our pig detection dataset that is more challenging than conventional tracking benchmark datasets. We are able to localise pigs in individual frames with 0.901 mean average precision (mAP), which then allows us to track individual pigs across video footage with 92% Multi-Object Tracking Accuracy (MOTA) and 73.4% Identity F1-Score (IDF1), and re-identify them after occlusions and dropped frames with 0.862 mAP (0.788 Rank 1 cumulative matching characteristic (CMC)). From these tracks we extract individual behavioural metrics for total distance travelled, time spent idle, and average speed with less than 0.015 mean squared error (MSE) for each. Changes in all these behavioural metrics have value in the detection of pig health and wellbeing.
A numerical procedure to obtain the pseudo-polynomial characteristic equation of a commensurate time-delay system

In this paper, a numerical procedure to obtain the pseudo-polynomial characteristic equation of a commensurate time-delay system is proposed. The method is formulated in terms of an interpolation problem, and it is based on the generation of a suitable set of random numbers. Some results, showing how the random number generation method affects the procedure convergence are also reported. This aspect is investigated in the paper and a suitable strategy to guarantee the problem solution is proposed. A numerical example is reported to outline the method behavior.
Fusing Geographic Information Into Latent Factor Model for Pick-Up Region Recommendation

Taxis' trajectories increasingly become an important data source for pick-up regions recommendation. However, long-term and growing taxi trajectory data would inevitably reduce the efficiency of recommendation and consume huge amounts of storage resources. Therefore, a Latent Factor Model integrated with the geographic information, the GeoLFM, is put forward for solving the data sparseness problem caused by the short-term taxis' trajectories. This model makes up the faultiness of data sparseness by integrating the geographic information being related to drivers into the decomposition of matrix. With the comparison between our experimental method and others, the Mean Absolute Error (MAE) between the recommended results and actual values is reduced by 23.4%, while the Root Mean Square Error (RMSE) is lowered by 19.8%.
Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method

This paper focuses on the force ripple estimation and compensation with the Kalman filter for the permanent magnet linear synchronous machine (PMLSM). The force ripple dynamics is firstly modeled as a higher-order integrator subsystem with fully considering its inherent nonlinear and time-varying characteristic. The motion system dynamics is then extended with modeling the force ripple as an extra state. The main idea for the accurate force ripple estimation is to construct an incremental extended state modeling-based Kalman filter (IESM-KF) for reducing the calculation cost as the higher-order dynamics of the force ripple is considered. And also, a simple and practical parameter tuning method for the IESM-KF is proposed with injecting a square-wave current disturbance to the position controller’s output under the cascade position-current closed loop. The inevitable time delay of the mechanical system is estimated with the sine-sweep-based model identification and is further considered in the IESM-KF design. Detailed experimental results are given to validate the effectiveness of the force ripple compensation with the IESM-KF and the corresponding parameter tuning method.
Fast DC-Type Electric Vehicle Charger Based On A Quasi-Direct Boost - Buck Rectifier

This work presents a DC-type fast electric vehicle battery charger featuring low switching losses. Herein, a conventional three-phase two-level voltage source rectifier with low capacitive energy storage operates with a discontinuous PWM modulation where each phase-legs stops switching for 240° of the grid fundamental period, i.e. only a single phase-leg switch every 60°. In this case, this AC-DC converter loses voltage controllability of the DC-link and thus a buck-type DC-DC converter is cascaded in order to provide the necessary voltage regulation and current limitation for the charging process of the electric vehicle. The presented circuit is benchmarked against other solutions for a designed 50 kW power capability battery charger when considering the charging of a 30 kWh Nissan Leaf. The results show a superior power efficiency of the studied system.
Practical consensus tracking of multi-agent systems with linear controllers

Ahstract- The second-order practical consensus tracking of multi-agent systems with a fixed topology and a varying leader's velocity is obtained by using a linear control strategy and the input-to-state stability framework. Then, a new control protocol is proposed where the leader, called a perceptive leader, receives information from its followers. Finally, a study is provided in simulation and on a fleet of mobile robots showing the effectiveness of non perceptive and perceptive strategies in terms of speed of convergence, precision and connectivity maintenance.
A Survey of Research on Runtime Rerandomization Under Memory Disclosure

In current software environments, code injection attacks have been better defended. But it also forced attackers to use the code already exist in memory to construct code-reuse attack, making code-reuse attack become the focal point of the new round of offensive and defensive games. As an important defense against attacks based on address space layout, code randomization technology effectively defends code-reuse attack by randomizing memory address space. The ASLR proposed by the Pax team is now used by most operating systems. However, due to memory disclosure, the security premise of code randomization has been broken. Therefore, in order to effectively defend against code-reuse attack, the current code randomization technology needs to improve the ability to resist memory disclosure. This paper mainly discusses the current randomization scheme in the context of memory disclosure and summarizes the future development direction of current code randomization technology.
Study on 0.5THz Backward Wave Oscillator

The 0.5THz backward wave oscillator, with Two-section rectangular gratings involving cylindrical electron beam, was developed. The length of the two-section backward-wave oscillator was determined by that of one section structure. The drift part linking the first section and second section, was located according to electron bunch. The output power of backward-wave oscillator is improved by introducing the floating part which can make the electron beam losing more energy and enhance the beam-wave interaction. The length of floating part was optimized by output power with particle-incell (PIC) simulations. When the length of floating part is 0.3mm, the maximum output power increases to 1.4W, comparing with that of one section structure (0.3W). Besides, the influences of conductivity, magnetic field, beam voltage and current on the output power are investigated through the CST simulation. The optimum results will be appeared in detailed abstract.
Efficient Design and Layout of Capacitive 3D Accelerometer

An approach for a mostly automated design and layout of capacitive accelerometers is presented, which permits the efficient synthesis of accelerometers up to 3 axes. The functionality of the hierarchical approach is shown using the example of a 3D accelerometer for tire-pressure monitoring systems (TPMS) using the open platform technology XMB10 from X-FAB.
Implementation of min-max time consensus tracking on a multi-quadrotor testbed

This paper deals with the implementation of minmax time consensus tracking on a multi-quadrotor testbed. A leader quadrotor generates a reference trajectory onto which the remaining quadrotors converge in min-max time using a local feedback control strategy. This control strategy is known to be globally optimal. Further, the effect of finite communication/measurement rate on consensus tracking is analyzed and bounds on the resulting deviations of trajectories are characterized. The theoretical claims made are verified through experiments.
Classifying Venue Categories of Unlabeled Check-ins Using Mobility Patterns

Some location-based social networks (LBSNs) provide, besides other spatiotemporal data, the category of venues where the data was shared from. This information allows a wide range of semantic analyses which are very useful to understand city dynamics and urban social behavior. Despite being strategic to the study of cities and societies, some LBSNs do not offer the category of venues by default. In this study, we propose an approach to identify the category of venues of unlabeled check-ins according to their geographic locations. This new classification approach is inspired by the classic k-nearest neighbor algorithm improved by mobility pattern information captured through the user's transition information observed in LBSN data. The performance evaluation of the proposed approach is performed with real-world data from different cities: London, New York City, and Tokyo. Experiments show that, for all cities, we can achieve better performances when users' mobility is taken into account. Besides, we have an indication that transfer learning regarding mobility patterns could be feasible between similar cities.
Increase of DSTM Spectral Efficiency by the Extension of the Mathematical Weyl Group with Application to Differential MIMO Systems

In this paper, we propose a new mathematical group called the Extended Weyl group aiming at increasing the spectral efficiency of the conventional Differential Space-Time Modulation (DSTM) found in literature. More specifically, the paper aims at solving the problem of spectral efficiency decrease of DSTM when the number of transmit antennas of Multiple-Input Multiple-Output (MIMO) systems increases. Moreover, this work proposes a thorough analysis of the distance spectrum between DSTM matrices and adopts a near optimal matrices mapping to decrease the bit error probability. The analysis and simulation results show clearly the improvement and outperformance of the adopted strategies.
Target Detection Algorithm Based on Double Attention

In this paper, a Deep Supervision Target Detector (DSTD) was proposed. It is a new framework for target detection that includes two attention mechanisms. Local features and global dependencies are adaptively integrated through a dual attention mechanism. At the same time, based on the Single Shot Detector (SSD) network framework, the rich context dependency is captured by the self-attention mechanism, and the fusion attention mechanism fuses the different scale feature maps. Specifically, two types of attention modules are introduced in the basic network framework to model the interdependence of semantics in space and different scales. Spatial modules selectively aggregate the features of each location by weighting the sum of the location features. Feature fusion between different scales through fusion modules enhances the connection of global contexts. Combining the outputs of the two attention modules improves the feature representation, which helps with more accurate detection results. We used the DSTD input of 320X320 to obtain the mean average precision (mAP) of 80.1% in VOC 2007 test. This result shows that DSTD is superior to other single-stage detectors represented by SSD. This result shows that DSTD is superior to other single-stage detectors represented by SSD.
Match the Cube: Investigation of the Head-coupled Input with a Spherical Fish Tank Virtual Reality Display

Fish Tank Virtual Reality (FTVR) displays create a compelling 3D effect with the motion parallax cue using the head-coupled perspective. While the head-coupled viewpoint control provides natural visuomotor coupling, the motion parallax cue has been found to be underutilized with minimal head motion detected when manual input becomes available to users. We investigate whether users can effectively use head-coupling in conjunction with manual input in a mental rotation task involving inspection and comparison of a pair of 3D cubes. We found that participants managed to incorporate the head-coupled viewpoint control with the manual touch input in the task. They used the touch input as the primary input and the head as the secondary input with the input ratio of 4.2:1. The combined input approach appears to be sequential with only 8.63% duration when the head and manual input are co-activated. The result of this study provides insights for designing head-coupled interactions in many 3D interactive applications.
A Broadband Linear Millimeter-Wave Power Amplifier With an Adaptive Bias Circuit

A broadband linear millimeter-wave power amplifier (PA) with an adaptive bias circuit implemented in 0.25 um SiGe BiCMOS for 5G millimeter-wave phased arrays is presented in this paper. The PA demonstrates a measured 21.7-dB small signal gain, 17.1-dBm saturated output power and 26.5% peak power added efficiency (PAE) at 27 GHz and the peak PAE maintains over 20% from 25 GHz to 32 GHz. At 27.5 GHz, the proposed PA is tested with an 800 MHz bandwidth 64-QAM signal without digital predistortion, which achieves a PAE of 11.56%, error vector magnitude (EVM) of −29.7 dB, and adjacent channel leakage ratio (ACLR) of −28.01 dBc at an average output power of 8.77 dBm.
Analyzing Energy Efficiency for IoT Devices with DRX Capability and Poisson Arrivals

Energy-efficient communications is one important consideration for Internet of Things (IoT) devices, and it can be achieved via the discontinuous reception (DRX) technology. In this paper, we consider an IoT device with the DRX capability. The device is functioning based on the LTE standard and it is communicating with the base station over a Nakagami-$m$ fading channel. Data are generated with fixed length and Poisson processes. Under these settings, we develop a cross-layer analytical model to analyze 1) the energy efficiency, 2) stationary probability and 3) state holding time of this device. Simulation results show that the proposed model can approximate the three performance of a IoT device accurately.
Functions-based CFG Embedding for Malware Homology Analysis

Malware homology analysis aims at detecting whether different malicious code originates from the same set of malicious code or is written by the same author or team, and whether it has intrinsic relevance and similarity. At the same time, the homology analysis of malicious code is also an important part of studying the groups behind different APT (Advanced Persistent Threat) attacks. At present, homology identification still relies on manual analysis and security experts' experience in the anti-malware industry. In addition, research on large-scale malicious code automated homology analysis is still insufficient. The method proposed in this paper is to solve the problem of large-scale malicious code homology automatic analysis, and hope to provide auxiliary information for discovering the group behind the APT attack. In this paper, we collected samples of different APT groups from public threat intelligence and proposed a novel approach to classify these samples into different APT groups to further analyze the homology of malware. We combined the CFG (Control Flow Graph) of the malicious code function and the disassembled code of the stripped malware to generate the embedding, i.e., a numeric vector, which formed a function feature database of the APT group, and presented a neural network model used for APT group classification. We have implemented our approach in a prototype system called MCrab. Our extensive evaluation showed that MCrab could produce high accuracy results, with few to no false positives. Our research also showed that deep learning can be successfully applied to malware homology analysis.
All-Digital Time-to-Digital Converter Design Methodology Based on Structured Data Paths

Time-to-Digital Converters (TDC) are popular circuits in many applications, where high resolution time measurements are required, for example, in Positron Emission Tomography (PET). Besides its resolution, the TDC’s linearity is also an important performance indicator, therefore calibration circuits usually play an important role on TDCs architectures. This paper presents an all-digital TDC implemented using Structured Datapath to reduce the need for calibration circuitry and cells custom design, without compromising the TDC’s linearity. The proposed design is fully implementable using a Hardware Description Language (HDL) and enables a complete design flow automation, reducing both development time and system’s complexity. The TDC is based on a Delay Locked Loop (DLL) paired with a coarse counter to increase measurement range. The proposed architecture and the design approach have proven to be efficient in developing a high resolution TDC with high linearity. The proposed TDC was implemented in TSMC 0.18 $\mu {\mathrm{ m}}$ CMOS technology process achieving a resolution of 180ps, with Differential Non-Linearity (DNL) and Integral Non-Linearity (INL) under 0.6 LSB.