Reviewerly/exHarmony huggingface dataset:
3D Sparse Deformation Signature for Dynamic Face Recognition

This paper proposes a novel compact and memory efficient Sparse 3D Deformation Signature (S3DS) to represent a sparse 3D deformation signal for 3D Dynamic Face Recognition. S3DS is based on a non-linear 6D-space representation that secures physically plausible 3D deformations. A unique deformation indicator is computed per triangle in a triangulated mesh, thanks to a recent 3D Deformation Signature (3DS) that is based on Lie Bodies. The proposed S3DS sparsely concatenates unique triangular indicators to construct the facial signature for each temporal instance. The novel descriptor shall benefit domains like surveillance and security in providing non-intrusive bio-metric measurements. By construction, S3DS is resistant to common security attacks like presentation, template and adversarial attacks. Two dynamic datasets (BU4DFE and COMA) were examined in various sparse concatenation settings. Using high reduction rates of $\approx 500$, a first rank recognition accuracy similar to the state of the art was achieved. At low reduction rates of $\approx 40$, S3DS outperformed most existing literature on BU4DFE achieving 99.92%. On COMA, it achieved 99.93% which outperforms existing literature. In an open-world experimental setup, using thousands of distractors, the accuracy reached up to 100% in detecting unseen distractors with high reduction rates in the 3D facial descriptor size.
The ⊥-Illusion Is Not a T-Illusion

Variants of the capital Latin letter T were prepared with the straight strokes replaced by J-, C-, or S-curves, mimicking handwritten Ts. These were used to test the hypothesis that the overestimation of the length of the T’s undivided line, relative to the length of its divided line may be understood as an adaptation to a corresponding letter schema. The illusion was greater for S-curve Ts than for C- and J-curve Ts, suggesting that approximate bilateral mirror symmetry is more important for the illusion to occur than letterness. Despite the illusion, observers were quite sensitive to the different lengths of the Ts’ curves.
A Study of Data Requirements for Data Mining Applications in Banking

There are many studies that have applied data mining to banking.However, the lack of proper data mounts a serious obstacle to the employment of data mining techniques by banks.This paper examines previous data mining research in the field of banking to extract all served entities and attributes required for analytical purposes, categorize these attributes and ultimately present a data model for analysis.After analyzing a wide range of data mining applications in banking, 28 entities with 423 attributes were identified and the final proposed entity-relationship model was drawn.Also, a checklist was provided based on the model for auditing data gap in banks and applied to a real case.The results of this paper can be seen as a supportive tool for improving bank's business intelligence maturity from the data perspective and enabling managers for analyzing data requirement of information systems.
Encrypted Feedback Linearization and Motion Control for Manipulator with Somewhat Homomorphic Encryption

Encrypted control is a cryptographic approach to reducing the risks of eavesdropping attacks for networked control systems. An encrypted controller determines control inputs directly from encrypted data without decryption. This study considers an implementation of an encrypted motion controller, which consists of feedback linearization and PD control, for a manipulator using somewhat homomorphic encryption. A controller reconstruction method is proposed for separating a nonlinear time-varying controller into a linear part and a nonlinear part. A plant side device supports encrypted controller operations by generating the nonlinear part in advance of encryption. Simulation results demonstrate that the proposed encrypted control system can achieve the same control performance as the one that an unencrypted control system does.
Actor-Network Method of Assembling Intelligent Logistics Terminal

Actor-network theory based approach was used to assemble a model of logistics terminal viewed as a network of interacting heterogeneous actors. Elements of applied semiotics and logics of action were utilized to formalize some concepts of the actor-network theory. The system of fuzzy indicators was introduced to describe statuses and dynamics of the model system. A virtual automatic device to support coordination and decision making between the heterogeneous actors in the multimodal logistic process (including intelligent material products) was suggested. The paper aims at bringing attention of the AI researchers, MAS theorists, human-machine systems engineers, ergonomists, knowledge engineers, logistics specialists and broader research community to the actor-network paradigm and its applied potential in socio-technological systems research.
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry

We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner. The appearance generator network models the information related to appearance, including color, illumination, identity or category, while the geometric generator performs geometric warping, such as rotation and stretching, through generating deformation field which is used to warp the generated appearance to obtain the final image or video sequences. Two generators take independent latent vectors as input to disentangle the appearance and geometric information from image or video sequences. For video data, a nonlinear transition model is introduced to both the appearance and geometric generators to capture the dynamics over time. The proposed scheme is general and can be easily integrated into different generative models. An extensive set of qualitative and quantitative experiments shows that the appearance and geometric information can be well disentangled, and the learned geometric generator can be conveniently transferred to other image datasets that share similar structure regularity to facilitate knowledge transfer tasks.
Hybrid embroidery

This paper presents Hybrid Embroidery, a framework for interactive fabrication that leverages computational methods to broaden the possibilities of the craft of embroidery. Combining embroidery techniques, generative design methods, computer vision and a computerized embroidery machine, we show how this framework elicits a variety of innovative fabrication experiences that emphasize open-ended exploration, improvisation and play. The paper documents this framework, a series of sample results, challenges and next steps. It further outlines some of its implications for supporting creative exploration through real-time and direct manipulation of materials and close human-machine interaction.
ART: Adaptive and Real-time Fall Detection Using COTS Smart Watch

Fall detection is one of the most important issues for taking good care of the elder people. Previous solutions are mainly focusing on the fall detection accuracy to avoid unnecessary false alarm with wearable devices, which provide the detection ability after the fall events. In this study, we propose ART, an accurate and real-time fall detection algorithm using COTS smart watch, which could provide accurateter that, we extract the threshold of the acceleration and angle by using the method of SVM. Evaluation results show that, ART can detect fall effectively before the impact, and the accuracy of the forward fall, backward fall and the lateral fall are 100%,95.5% and 95.7% respectively. The average processing time is 276ms, which can provide relatively sufficient time for fall prevention.
Analysis of Improvement Potentials in Current Virtual Reality Applications by Using Different Ways of Locomotion

It has become common knowledge that the use of virtual reality (VR) applications can very often cause typical symptoms of motion sickness such as nausea and dizziness. For this reason, however, there are also more and more attempts to contain or completely avoid the side effect, which is meanwhile called cybersickness. This paper elaborates on a pre-study of a large-scale study investigating whether the use of treadmills for real, physical movement in virtual worlds can reduce the incidence of motion sickness compared to other common VR locomotion techniques and therefore enlarge the time participants can use VR for training or simulation in companies.
ParSecureML: An Efficient Parallel Secure Machine Learning Framework on GPUs

Machine learning has been widely used in our daily lives. Large amounts of data have been continuously produced and transmitted to the cloud for model training and data processing, which raises a problem: how to preserve the security of the data. Recently, a secure machine learning system named SecureML has been proposed to solve this issue using two-party computation. However, due to the excessive computation expenses of two-party computation, the secure machine learning is about 2x slower than the original machine learning methods. Previous work on secure machine learning mostly focused on novel protocols or improving accuracy, while the performance metric has been ignored. In this paper, we propose a GPU-based framework ParSecureML to improve the performance of secure machine learning algorithms based on two-party computation. The main challenges of developing ParSecureML lie in the complex computation patterns, frequent intra-node data transmission between CPU and GPU, and complicated inter-node data dependence. To handle these challenges, we propose a series of novel solutions, including profiling-guided adaptive GPU utilization, fine-grained double pipeline for intra-node CPU-GPU cooperation, and compressed transmission for inter-node communication. As far as we know, this is the first GPU-based secure machine learning framework. Compared to the state-of-the-art framework, ParSecureML achieves an average of 32.2x speedup. ParSecureML can be downloaded from https://github.com/ZhengChenCS/ParSecureML.
Intelligent System to Analyze Data About Powered Wheelchair Drivers

The research presented in this paper creates an intelligent system that collects powered wheelchair users’ driving session data. The intelligent system is based on a Python programming platform. A program is created that will collect data for future analysis. The collected data considers driving session details, the ability of a driver to operate a wheelchair, and the type of input devices used to operate a powered wheelchair. Data is collected on a Raspberry Pi microcomputer and is sent after each session via email. Data is placed in the body of the emails, in an attached file and saved on microcomputer memory. Modifications to the system is made to meet confidentiality and privacy concerns of potential users. Data will be used for future analysis and will be considered as a training data set to teach an intelligent system to predict future path patterns for different wheelchair users. In addition, data will be used to analyze the ability of a user to drive a wheelchair, and monitor users’ development from one session to another, compare the progress of various users with similar disabilities and identify the most appropriate input device for each user and path.
Discrete Kalman Filter and Linear Regression Comparison for Vessel Coordinate Prediction

Automatic Identification System (AIS) is one of the most prominent systems for monitoring vessel activity. Although significant advances in AIS coverage have been achieved in recent times, a vessel will typically still experience gaps in reception within its voyage. These gaps can vary from a few seconds to multiple hours depending on the route. A typical approach when dealing with these gaps when monitoring a vessel's voyage is to move from a pointal domain to a trajectory domain using a trajectory prediction algorithm. In this paper, we compare the performance of two trajectory prediction algorithms being (1) the Discrete Kalman filter (DKF) and (2) the Linear Regression Model (LRM). It is postulated that the DKF, with its added complexities in initiating the relevant parameters does not yield significant performance advantages over using a computationally simpler LRM.
An Integrated Strategy toward the Extraction of Contour and Region of Sonar Images

In this paper, an integrated underwater sonar image extraction strategy, which combines two improved methods, namely the level set method (LSM) and the Lattice Boltzmann Method (LBM), is proposed. First, sonar images are processed by a clustering method and a connected domain analysis to generate the target minimum rectangle frame. Next, the segmentation task is decomposed into two subtasks, namely a coarse segmentation task to obtain the initial contour and a fine segmentation task after embedding the initial contour. Finally, the improved LSM is used to obtain the target contour, and the coarse contour of the segment is embedded into the LBM to obtain the region segmentation of the target in the sonar images. The main contributions of the paper are as follows: (1) The contours and regions of the sonar images are extracted simultaneously. (2) The original LBM method is enhanced to solve the level set iteration problem. (3) The region segmentation with the original image background is extracted, and a more intuitive region segmentation result than that of directly extracting the contour of the level set is achieved. Experimental results based on four evaluation indices of image segmentation show that our method is effective, accurate, and superior to other existing methods.
Mineral exploration using hyperspectral data

In this chapter, an overview is given on a conceptual basis of different applications of hyperspectral data for mineral exploration. The narrow and contiguous spectral bands of hyperspectral sensors can detect to detect the diagnostic absorption signatures of different minerals and rocks. Many surface proxies (alteration zone, caprock of mineralization, etc.) of concealed or covered mineral deposits formed under different ore genetic processes are constituted of spectrally distinguishable minerals and these minerals are discriminable using narrowband hyperspectral data. The mapping of these anomalies is important for mineral exploration. Hyperspectral data also help in accurately mapping the lithology (i.e., host rock) of mineral deposits and the spectrometric parameters of absorption features (i.e., quantitative parameters) are useful in estimating the grade of monomineralic deposits. In this chapter, the major steps followed for hyperspectral data processing for mineral exploration are also highlighted along with the physical basis of the absorption features of different minerals. Some key areas of interest in hyperspectral data utilization in advanced problems of mineral exploration and approaches to be followed for integrating hyperspectral data with other geoscientific data for the operational utilization of mineral deposits are also discussed.
Implementation-induced Inconsistency and Nondeterminism in Deterministic Clustering Algorithms

A deterministic clustering algorithm is designed to always produce the same clustering solution on a given input. Therefore, users of clustering implementations (toolkits) naturally assume that implementations of a deterministic clustering algorithm A have deterministic behavior, that is: (1) two different implementations I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> of A are interchangeable, producing the same clustering on a given input D, and (2) an implementation produces the same clustering solution when run repeatedly on D. We challenge these assumptions. Specifically, we analyze clustering behavior on 528 datasets, three deterministic algorithms (Affinity Propagation, DBSCAN, Hierarchical Agglomerative Clustering) and the deterministic portion of a fourth (K-means), as implemented in various toolkits; in total, we examined 13 algorithm-toolkit combinations. We found that different implementations of deterministic clustering algorithms make different choices, e.g., default parameter settings, noise insertion, input dataset characteristics. As a result, clustering solutions for a fixed algorithm-dataset combination can differ across runs (nondeterminism) and across toolkits (inconsistency). We expose several root causes of such behavior. We show that remedying these root causes improves determinism, increases consistency, and can even improve efficiency. Our approach and findings can benefit developers, testers, and users of clustering algorithms.
Modeling Performance of a Stereo Camera Sensor for Optimization

The geometric model and performance criteria of a stereo camera sensor are proposed for the purpose of sensor performance optimization. In particular, the details of the geometric model is presented to characterize the configuration parameters of the stereo camera sensor. Six effective criteria are then synthesized to measure the sensor performance based on the geometric model, including depth resolution, permissible degree of orientation, visual resolution, field of view, focus, and occlusion. A sample optimization task is presented to show the effectiveness of the model and the performance criteria with the simulation validation using the gradient-ascent based algorithm and the improved genetic algorithm respectively.
Spaceborne Radar Functional Architecture for Debris Bayesian Inference

This paper outlines a conceptual and functional digression on a novel Spaceborne Radar (SBR) payload architecture for augmenting space-based measurements and monitoring capabilities for near-Earth Space Situational Awarness (SSA). Taking into account state-of-the art space-qualified technologies in both digital and Radio Frequency (RF) domains, feasible onboard processing subsytems are identified as high level conceptual block schemes allowing for Bayesian inference on small-size debris dynamic states and ancillary scattering signatures. A description of a bespoke Active Electronically Scanned Array (AESA) based Pulse Doppler Radar in the Ka Band is illustrated from a functional perspective built upon a digital modular archetype, along with programmable temporal hierarchies for acquiring a complex data hyper-cube. Operative modes are also addressed including the novel Low Pulse Repetion Frequency (Low-PRF) Range & Range-Rate Search (RRRS) in cascade with a Pause While Scan (PWS) strategy for Bayesian Multiple Target Tracking (MTT).
Metrics and Dashboard for Level 2 – Experience

In the recent years, the amount of metrics collected and monitored in a project has been grown. This increases the time and effort required to create and track the success of an organization or of a particular activity, but often does not lead to an improvement in project management. In this article we will show a different approach to finding those essential metrics that should not be missing in any project status report, especially for smaller teams or application projects. We will show how to obtain a complete list of generally useful metrics per process (taking into account the VDA scope) and how to extract from this list the two or three key metrics per process based on a risk evaluation to finally arrive at a lean metric framework. The processes Change Request Management (SUP.10) and Software Requirements Analysis (SWE.1) are used as examples described in detail.
Runtime prediction of high-performance computing jobs based on ensemble learning

In high-performance computing job scheduling systems, to accurate predict the job runtime can effectively utilize the idle resource fragments generated during cluster computing as well as backfill them for improving scheduling performance. Because the runtime of high-performance computing jobs are affected by many factors thus are complicated non-linear problem. The ensemble machine learning method is used to predict the runtime of jobs in cluster computing. By comparing the prediction results of different models on the job log data sets from three real high-performance computing systems, it is found that the LightGBM algorithm has higher prediction accuracy, faster computation speed, shorter training time of the model and achieve better overall performance.
Method of Recurrent Neural Network Hardware Implementation

Real-time data processing using recurrent neural networks (NN) is non-trivial task, due to tight timing constraints requirements. It is proposed hardware implementation of recurrent echo state NN (ESN) on the basis of the Cyclone IV FPGA. Advantages of the hardware implementation are high computational parallelism and low power consumption. To solve the problem of neuron weight storage, it is proposed to reduce the space of their values to a set of integers of low capacity. It was determined that the proposed NN model decreases need in hardware resources for the reservoir implementation in 2–3 orders of magnitude in comparison with conventional NN. Modeling results, implementation and testing of the FPGA project confirmed effectiveness of the proposed integer NN in hardware applications #CSOC1120.
The Trajectory PHD Filter for Jump Markov System Models and Its Gaussian Mixture Implementation

Based on the recently proposed set of trajectories, the trajectory probability hypothesis density (TPHD) filter is capable of producing trajectory estimates in first principle without adding labels or tags. In this paper, we propose a robust TPHD filter to track maneuvering targets through compatible the jump Markov system (JMS) model that the highly dynamic targets movement switches between multiple models, referred as JMS-TPHD filter. Firstly, we extend the concept of JMS to set of trajectories and derive the recursion for the proposed filter. Then, we develop the linear Gaussian Mixture (LGM) implementation of JMS-TPHD recursion and also consider the L-scan approximations of the implementations for computational efficiency. Finally, in a challgenging multiple maneuvering targets tracking scenario, the simulation results demonstrate the performance of the JMS-TPHD filter.
The Future is Graphic: Animated notation for contemporary practice

A growing number of musicians are recognising the importance of re-thinking notation and its capacity to support contemporary practice. New music is increasingly more collaborative and polystylistic, engaging a greater range of sounds from both acoustic and electronic instruments. Contemporary compositional approaches combine composition, improvisation, found sounds, production and multimedia elements, but common practice music notation has not evolved to reflect these developments. While traditional notations remain the most effective way to communicate information about tempered harmony and the subdivision of metre for acoustic instruments, graphic and animated notations may provide an opportunity for the representation and communication of electronic music. If there is a future for notating electronic music, the micro-tonality, interactivity, non-linear structures, improvisation, aleatoricism and lack of conventional rhythmic structures that are features of it will not be facilitated by common practice notation. This article proposes that graphic and animated notations do have this capacity to serve electronic music, and music that combines electronic and acoustic instruments, as they enable increased input from performers from any musical style, reflect the collaborative practices that are a signpost of current music practice. This article examines some of the ways digitally rendered graphic and animated notations can represent contemporary electronic music-making and foster collaboration between musicians and composers of different musical genres, integrating electronic and acoustic practices.