Reviewerly/exHarmony huggingface dataset:
MEAD: Model-Based Vertical Auto-Scaling for Data Stream Processing

The unpredictable variability of Data Stream Processing (DSP) application workloads calls for advanced mechanisms and policies for elastically scaling the processing capacity of DSP operators. Whilst many different approaches have been used to devise policies, most of the solutions have focused on data arrival rate and operator resource utilization as key metrics for auto-scaling. We here show that, under burstiness in the data flows, overly simple characterizations of the input stream can yet lead to very inaccurate performance estimations that affect such policies, resulting in sub-optimal resource allocation.We then present MEAD, a vertical auto-scaling solution that relies on online state-based representation of burstiness to drive resource allocation. We use in particular Markovian Arrival Processes (MAPs), which are composable with analytical queueing models, allowing us to efficiently predict performance at run-time under burstiness. We integrate MEAD in Apache Flink, and evaluate its benefits over simpler yet popular auto-scaling solutions, using both synthetic and real-world workloads. Differently from existing approaches, MEAD satisfies response time requirements under burstiness, while saving up to 50% CPU resources with respect to a static allocation.
Real-Time Facial Landmark Detection by Attention-driven Lightweight Network

Facial landmark detection is a basic challenge in computer vision. Currently, CNNs are the most effective methods for facial landmark detection. However, this works usually bring in a mass of model parameters, lead to high computing resource cost. To simultaneously consider the accuracy and compactness of model, we propose a new model, namely attention-driven lightweight face alignment network(ALFAN), using MobileNetV3 block as backbone network. The proposed ALFAN, with 6.5Mb of model size and fast processing speed, achieves satisfactory precision compared with mainstream approach. Moreover, in order to make the most of feature maps from different layers, the channel attention block and spatial attention block are used for the high-level feature maps and the low-level feature maps respectively. In addition, we design a geometric-wing loss to guide the network to learn more facial geometric/structural information. The evaluation on 300W challenging facial landmark dataset show that our algorithm performs better than some state-of-the-art methods.
Generalized Net Model of Overall Telecommunication System with Queuing

Generalized Net (GN) model of overall telecommunication system with queuing is proposed. It is based on the classical conceptual model of overall telecommunication system which considers user’s behaviour, finite number of users and terminals, losses due to abandoned and interrupted dialing, blocked and interrupted switching, unavailable intent terminal, blocked and abandoned ringing and abandoned communication. A queuing system with finite capacities of the server and buffer and FIFO discipline of service of the requests is included in the Switching stage. The proposed model can be used in the analytical modeling of overall telecommunication systems.
A Flexible Baseline Measuring System Based on Optics for Airborne DPOS

Three-dimensional imaging for multi-node interferometric synthetic aperture radar (InSAR) or multi-task imaging sensors has become the prevailing trend in the field of aerial remote sensing, which requires multi-node motion information to carry out the motion compensation. A distributed position and orientation system (DPOS) can provide multi-node motion information for InSAR by transfer alignment technology. However, due to wing deformation, the relative spatial relationship between the nodes will change, which will lead to lower accuracy of the transfer alignment. As a result, the flexible baseline between the nodes affects the interferometric phase error compensation and further deteriorates the imaging quality. This paper proposes a flexible baseline measuring system based on optics, which achieves non-connect measurement and overcomes the problem that it is difficult to build an accurate wing deformation model. An accuracy test was conducted in the laboratory, and results showed that the measurement accuracy of the baseline under static and dynamic conditions was less than 0.3 mm and 0.67 mm, respectively.
Accelerating Distributed Learning in Non-Dedicated Environments

Machine learning (ML) models are increasingly trained with distributed workers possessing heterogeneous resources. In such scenarios, model training efficiency may be negatively affected by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stragglers</i> —workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible. In this article, we propose a novel strategy, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semi-dynamic load balancing</i> , to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iteration boundaries</i> , being non-intrusive to intra-iteration execution. Based on it we further develop LB-BSP, an integrated worker coordination mechanism that adapts workers’ load to their instantaneous processing capabilities—by right-sizing the sample batches at the synchronization barriers. We have designed distinct load tuning algorithms for ML in CPU clusters, in GPU clusters as well as in federated learning setups, based on their respective characteristics. LB-BSP has been implemented as a Python module for ML frameworks like TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical, effective and light-weight, and is able to accelerating distributed training by up to 54 percent.
Menu Design on Small Display User Interfaces: Measuring the Influence of Menu Type, Number of Preview Items, and Menu Breadth on Navigation Efficiency

An experiment was conducted to determine menu navigation efficiency (in terms of time, accuracy, and subjective ratings) of mobile devices (smartphones and smart watches) displaying menus in Chinese language; the study compares the performance of menus with three types and three different numbers of preview items. The compared menu type variants were full, collapsible, and cascading menus. The latter two types had submenus, while the first one did not. The participants were instructed to find the target item. The relationship among menu type, number of preview items, menu breadth, and navigation efficiency was found. This indicated that the menu type significantly influenced navigation efficiency. When the menu breadth was large, collapsible, and cascading menus were suitable for smartphones and watches, respectively. The number of preview items also had a significant effect, especially on watches; 16 and 4 preview items were suitable for phones and watches, respectively.
Wall-Following Navigation for Mobile Robot Based on Random Forest and Genetic Algorithm

Due to the high real-time requirements of robot navigation tasks, the nonlinearity of the robot system itself makes it difficult to model accurately, in addition, the existing "black box" navigation control model is difficult to verify, hard to achieve static and lack of interpretability. Therefore, in this paper, a navigation method for wall-following mobile robots based on random forest and genetic algorithm is proposed. In our proposed method, robots based on perceiving environment information need to autonomously decide the actions to be performed to implement subsequent navigation behaviors. On the UCI robot navigation dataset, the random forest model is first trained, and then control rules are extracted from the trained random forest, and genetic algorithms are used to optimize and filter these rules. Therefore, we have obtained a rule base with good generalization performance, that is, the controller. A comparative experiment on the same data set shows that the method proposed in this paper has a high accuracy rate of 89.19% and a recall rate of 89%. In 6 sets of comparative experiments, compared with the gravity search and particle swarm optimization assisted artificial neural network method and the gravity search and feedforward neural network method, the average accuracy of our proposed method is improved by 16.43% and 29.66%, respectively.
An Efficient and Flexible Automatic Search Algorithm for Convolution Network Architectures

As many superior convolutional neural networks (CNNs) have been proposed in recent years, CNNs have played an important role in computer vision. However, manually-designing CNN architecture is difficult since expertise is required. Therefore, several automatic search algorithms have been proposed for neural architecture search, which usually have considerable computational complexity and the search space is limited. To address these problems, an efficient and flexible CNN architecture search algorithm (EF-CNN) is proposed in this paper. In EF-CNN, a flexible architecture search space is constructed by considering the depth, width, and lightweight blocks. In order to improve the reliability of the architecture while reducing the computational time, a multi-objective fitness correction method is proposed in EF-CNN based on the divided datasets, where the accuracy and computational complexity of architecture are considered simultaneously to design CNN. The experimental results on CIFAR-10 and CIFAR-100 indicate that the performance of CNN architecture designed by EF-CNN is very competitive while the computational time is greatly reduced.
An Improved Random Forest Classifier for Imbalanced Learning

There are many application scenarios involving imbalanced datasets, whereas many traditional machine learning methods have limited ability to adapt to this kind of data. These methods usually have a bias to identify the majority classes while the minority classes are more important in many cases. In this study, we propose a variant of the completely random forest called HCRF. To improve the classification performance of imbalanced data, we introduced 2 mechanisms: random hybrid-resampling and a cost function that focuses on the minority classes. Verified on several imbalanced datasets, HCRF outperforms all comparison methods, demonstrating excellent performance on imbalanced learning.
Verified Multiple-Time Signature Scheme from One-Time Signatures and Timestamping

Buldas, Laanoja, and Truu designed a family of server-assisted digital signature schemes (BLT signatures) built around cryptographic timestamping and forward-resistant tag systems. The original constructions had either expensive key generation phase or stateful client-side computations. In this paper, we construct a stateless tag system with efficient key generation from one-time signature schemes. We prove that the proposed tag system is forward-resistant and when combined with cryptographic timestamping, it induces a secure (existentially unforgeable) multiple-time signature scheme. Our constructions are developed and verified using the EasyCrypt framework.
Dehazing cost volume for deep multi-view stereo in scattering media with airlight and scattering coefficient estimation

We propose a learning-based multi-view stereo (MVS) method in scattering media, such as fog or smoke, with a novel cost volume, called the dehazing cost volume. Images captured in scattering media are degraded due to light scattering and attenuation caused by suspended particles. This degradation depends on scene depth; thus, it is difficult for traditional MVS methods to evaluate photometric consistency because the depth is unknown before three-dimensional (3D) reconstruction. The dehazing cost volume can solve this chicken-and-egg problem of depth estimation and image restoration by computing the scattering effect using swept planes in the cost volume. We also propose a method of estimating scattering parameters, such as airlight, and a scattering coefficient, which are required for our dehazing cost volume. The output depth of a network with our dehazing cost volume can be regarded as a function of these parameters; thus, they are geometrically optimized with a sparse 3D point cloud obtained at a structure-from-motion step. Experimental results on synthesized hazy images indicate the effectiveness of our dehazing cost volume against the ordinary cost volume regarding scattering media. We also demonstrated the applicability of our dehazing cost volume to real foggy scenes.
The Application of Computer “Virtual Simulation” Experimental Teaching in Basic Football Tactics

Football tactics are complex and play an important role in football match. Based on analyzing the characteristics and advantages of the virtual simulation teaching platform, this paper uses virtual simulation technology to help students improve their abilities of making tactical decisions in the football game. In this experiment, real competition scenes are reduced to virtual competition scenes, which enables students to judge and make decisions from the first perspective. The method ensures the reality in teaching and effectively improves the efficiency of teaching and learning. It also adapts to the new trend of teaching reform.
Three-Dimensional Area Coverage with UAV Swarm based on Deep Reinforcement Learning

In this paper, we study the fast coverage problem of 3D irregular terrain surfaces with a hierarchical UAV swarm. We first build a 3D model of a random irregular terrain and project the 3D terrain surface into many weighted 2D patches. Then we develop a two-level hierarchical UAV swarm architecture, including the low-level follower UAVs (FUAVs) and the high-level leader UAVs (LUAVs). For FUAVs, we adopt the traditional coverage trajectory algorithm to carry out specific coverage tasks within patches based on the star communication topology. For LUAVs, we propose a swarm deep Q-learning (SDQN) reinforcement learning algorithm to select patches. The numerical results show that the total coverage time of the SDQN is less than that of existing methods, which demonstrates the effectiveness of the proposed algorithm.
Compressing Gradients by Exploiting Temporal Correlation in Momentum-SGD

An increasing bottleneck in decentralized optimization is communication. Bigger models and growing datasets mean that decentralization of computation is important and that the amount of information exchanged is quickly growing. While compression techniques have been introduced to cope with the latter, none has considered leveraging the temporal correlations that exist in consecutive vector updates. An important example is distributed momentum-SGD where temporal correlation is enhanced by the low-pass-filtering effect of applying momentum. In this paper we design and analyze compression methods that exploit temporal correlation in systems both with and without error-feedback. Experiments with the ImageNet dataset demonstrate that our proposed methods offer significant reduction in the rate of communication at only a negligible increase in computation complexity. We further analyze the convergence of SGD when compression is applied with error-feedback. In the literature, convergence guarantees are developed only for compressors that provide error-bounds point-wise, i.e., for each input to the compressor. In contrast, many important codes (e.g. rate-distortion codes) provide error-bounds only in expectation and thus provide a more general guarantee. In this paper we prove the convergence of SGD under an expected error assumption by establishing a bound for the minimum gradient norm.
Learning-based encoder algorithms for VVC in the context of the optimized VVenC implementation

Versatile Video Coding (VVC) is the most recent and efficient video-compression standard of ITU-T and ISO/IEC. It follows the principle of a hybrid, block-based video codec and offers a high flexibility to select a coded representation of a video. While encoders can exploit this flexibility for compression efficiency, designing algorithms for fast encoding becomes a challenging problem. This problem has recently been attacked with data-driven methods that train suitable neural networks to steer the encoder decisions. On the other hand, an optimized and fast VVC software implementation is provided by Fraunhofer’s Versatile Video Encoder VVenC. The goal of this paper is to investigate whether these two approaches can be combined. To this end, we exemplarily incorporate a recent CNN-based approach that showed its efficiency for intra-picture coding in the VVC reference software VTM to VVenC. The CNN estimates parameters that restrict the multi-type tree (MTT) partitioning modes that are tested in rate-distortion optimization. To train the CNN, the approach considers the Lagrangian rate-distortion-time cost caused by the parameters. For performance evaluation, we compare the five operational points reachable with the VVenC presets to operational points that we reach by using the CNN jointly with the presets. Results show that the combination of both approaches is efficient and that there is room for further improvements.
Fault Detection Method based on Auto-associative Kernel Regression and Interval Type-2 Fuzzy Logic System for Multivariate Process

In multivariate processes (e.g., mechanical, chemical process and power plant, etc.), because several systems are complexly connected and operated, equipment faults can lead to failure or malfunction of the entire process. To improve the safety and reliability of the process, there is a need for a fault detection technology capable of preventing an accident by detecting the fault of an object in advance. In this paper, we detect the faults in multivariate processes using auto-associative kernel regression(AAKR), which is mainly used to detect faults due to benefits applicable to multiple-mode processes. AAKR is a nonparametric multivariate method that compares the similarity between the training data stored in memory and the query vector (testing data), and then assigns high weights to the high similarity vectors to compute the estimated vector. However, since there is a problem that you need to set the proper bandwidth parameter of the weighting function to calculate the weight, it is very important to reduce the parameter uncertainty by properly adjusting the bandwidth parameter according to the properties and characteristics of the input data. The conventional methods for setting of bandwidth parameter is used by trial and error. This method takes a lot of time and computation power. In this paper, we set the proper bandwidth parameter of weighting function in AAKR algorithm using interval type-2 fuzzy logic system (IT2FLS) that can deal with uncertainty. Dataset simulated from a multivariate dynamic process model was used to experiments, and the fault data was generated by applying two types of disturbance (e.g., bias and drift) to the simulated data. Experimental results show that the proposed fault detection method has better performance than the conventional method for setting of bandwidth parameter.
Evaluation of eye tracking for a decision support application

Eye tracking is used widely to investigate attention and cognitive processes while performing tasks in electronic medical record (EMR) systems. We explored a novel application of eye tracking to collect training data for a machine learning-based clinical decision support tool that predicts which patient data are likely to be relevant for a clinical task. Specifically, we investigated in a laboratory setting the accuracy of eye tracking compared to manual annotation for inferring which patient data in the EMR are judged to be relevant by physicians. We evaluated several methods for processing gaze points that were recorded using a low-cost eye-tracking device. Our results show that eye tracking achieves accuracy and precision of 69% and 53%, respectively compared to manual annotation and are promising for machine learning. The methods for processing gaze points and scripts that we developed offer a first step in developing novel uses for eye tracking for clinical decision support.
Patents – Indirect Infringement and Intermediaries

Chapter 6 explores the doctrines of indirect (secondary) patent infringement in the United States, Europe, and Japan, focusing primarily on novel statutory interpretation issues brought about by 3D printing technology. Indirect infringement generally requires knowledge of the patent or some sort of intent to infringe. Because 3D printing technology will empower many legally unsophisticated actors to assist – even if unwittingly – others to infringe, virtually every jurisdiction will need to clarify how to measure knowledge or intent when numerous individuals or small businesses are involved. In Europe, courts will also need to decide the fundamental issue of whether the knowledge requirement implies a culpable mental state. Additional statutory interpretation issues include what constitutes a “component” under U.S. law and what constitutes “means, relating to an essential element of the invention” under many countries’ statutes. In Japan, courts or lawmakers will need to clarify whether a “product” includes 3D printable files. The chapter concludes by recommending that courts and lawmakers resolve interpretive issues in a manner that captures at least the most egregious actors, namely, those who repeatedly and knowingly facilitate infringement by distributing DMFs.
Construction of Learner Group Characteristics Model Based on Network Learning Data Perception and Mining

With the continuous maturity of online learning methods, a large amount of learning data is stored on various learning platforms. By using big data technology, collecting and analyzing distributed network data, mining learner attribute information, learning behavior and learning results of three types of related data with hidden value. The cluster analysis method is used to extract learner group characteristic parameters, and a six-element learner group characteristic model based on knowledge level, learning motivation, learning attitude, learning style, interest preference and cognitive ability is constructed. The model adopts a multi-level three-dimensional structure, which mainly includes four functional levels: data perception layer, data analysis layer, application service layer, and user visualization layer. Under the effect of the dual mode of learner group characteristic analysis and user group adaptive feedback update, the recommendation function of the hybrid intelligent recommendation engine based on collaborative filtering algorithm and deep learning is used to realize user-visualized personalized recommendation and customized services. Under the function of this model, it meets the actual needs of personalized learning and promotes the development of intelligent learning.