Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds From Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction

Unsupervised feature learning for point clouds has been vital for large-scale point cloud understanding. Recent deep learning based methods depend on learning global geometry from self-reconstruction. However, these methods are still suffering from ineffective learning of local geometry, which significantly limits the discriminability of learned features. To resolve this issue, we propose MAP-VAE to enable the learning of global and local geometry by jointly leveraging global and local self-supervision. To enable effective local self-supervision, we introduce multi-angle analysis for point clouds. In a multi-angle scenario, we first split a point cloud into a front half and a back half from each angle, and then, train MAP-VAE to learn to predict a back half sequence from the corresponding front half sequence. MAP-VAE performs this half-to-half prediction using RNN to simultaneously learn each local geometry and the spatial relationship among them. In addition, MAP-VAE also learns global geometry via self-reconstruction, where we employ a variational constraint to facilitate novel shape generation. The outperforming results in four shape analysis tasks show that MAP-VAE can learn more discriminative global or local features than the state-of-the-art methods.
A Comparison of First-Difference and Forward Orthogonal Deviations GMM

This paper provides a necessary and sufficient instruments condition assuring two-step generalized method of moments (GMM) based on the forward orthogonal deviations transformation is numerically equivalent to two-step GMM based on the first-difference transformation. The condition also tells us when system GMM, based on differencing, can be computed using forward orthogonal deviations. Additionally, it tells us when forward orthogonal deviations and differencing do not lead to the same GMM estimator. When estimators based on these two transformations differ, Monte Carlo simulations indicate that estimators based on forward orthogonal deviations have better finite sample properties than estimators based on differencing.
Residue guided loop filter for HEVC post processing

The block-based coding structure in the hybrid coding framework gives rise to the obvious artifacts such as blocking, ringing . etc. Recently, some Convolutional Neural Network (CNN) based works apply reconstruction as the only input to reduce the artifacts. Though the performance of these works relying on powerful learning ability surpasses traditional loop-filter based methods in the High Efficiency Video Coding (HEVC) standard, how to enhance the high frequency signal is still not addressed. In addition to reconstruction, we first propose using the residue as the other input of our CNN-based loop filter. In essence, the residual signal as a high frequency indicator guides the CNN to augment the high frequency signal such as sharp shape and edge information. Second, we find out that the reconstruction and residue signals have different characteristics and should be handled with different network structures. For the reconstruction, we develop an All Frequency (reconstruction) CNN (AF-CNN) adopting the down sampling and up sampling pairs to learn all frequency signal with the global information. For the residue, we devise a High Frequency (residual) CNN (HF-CNN) customizing the Residual Blocks to adapt to the high frequency signal information. To the best of our knowledge, this is the first work that employs residual signal as a vital independent high frequency input to direct the learning of CNN- based loop filtering. We implement the proposed algorithms in the HEVC reference software. The experimental results show that our proposed approach of dual inputs of Residual and Reconstruction with HF-CNN and AF-CNN respectively (RRHA) presents significant BD-rate savings compared with the current CNN-based scheme.
Increasing Scalability of Process Mining using Event Dataframes: How Data Structure Matters

Process Mining is a branch of Data Science that aims to extract process-related information from event data contained in information systems, that is steadily increasing in amount. Many algorithms, and a general-purpose open source framework (ProM 6), have been developed in the last years for process discovery, conformance checking, machine learning on event data. However, in very few cases scalability has been a target, prioritizing the quality of the output over the execution speed and the optimization of resources. This is making progressively more difficult to apply process mining with mainstream workstations on real-life event data with any open source process mining framework. Hence, exploring more scalable storage techniques, in-memory data structures, more performant algorithms is a strictly incumbent need. In this paper, we propose the usage of mainstream columnar storages and dataframes to increase the scalability of process mining. These can replace the classic event log structures in most tasks, but require completely different implementations with regards to mainstream process mining algorithms. Dataframes will be defined, some algorithms on such structures will be presented and their complexity will be calculated.
Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler

Solving inverse problems without the use of derivatives or adjoints of the forward model is highly desirable in many applications arising in science and engineering. In this paper, we propose a new version of such a methodology, a framework for its analysis, and numerical evidence of the practicality of the method proposed. Our starting point is an ensemble of over-damped Langevin diffusions which interact through a single preconditioner computed as the empirical ensemble covariance. We demonstrate that the nonlinear Fokker-Planck equation arising from the mean-field limit of the associated stochastic differential equation (SDE) has a novel gradient flow structure, built on the Wasserstein metric and the covariance matrix of the noisy flow. Using this structure, we investigate large time properties of the Fokker-Planck equation, showing that its invariant measure coincides with that of a single Langevin diffusion, and demonstrating exponential convergence to the invariant measure in a number of settings. We introduce a new noisy variant on ensemble Kalman inversion (EKI) algorithms found from the original SDE by replacing exact gradients with ensemble differences; this defines the ensemble Kalman sampler (EKS). Numerical results are presented which demonstrate its efficacy as a derivative-free approximate sampler for the Bayesian posterior arising from inverse problems.
Approximation Capabilities of Neural Ordinary Differential Equations

Neural Ordinary Differential Equations have been recently proposed as an infinite-depth generalization of residual networks. Neural ODEs provide out-of-the-box invertibility of the mapping realized by the neural network, and can lead to networks that are more efficient in terms of computational time and parameter space. Here, we show that a Neural ODE operating on a space with dimensionality increased by one compared to the input dimension is a universal approximator for the space of continuous functions, at the cost of loosing invertibility. We then turn our focus to invertible mappings, and we prove that any homeomorphism on a $p$-dimensional Euclidean space can be approximated by a Neural ODE operating on a $(2p+1)$-dimensional Euclidean space.
The next 700 compiler correctness theorems (functional pearl)

Compiler correctness is an old problem, with results stretching back beyond the last half-century. Founding the field, John McCarthy and James Painter set out to build a "completely trustworthy compiler". And yet, until quite recently, even despite truly impressive verification efforts, the theorems being proved were only about the compilation of whole programs, a theoretically quite appealing but practically unrealistic simplification. For a compiler correctness theorem to assure complete trust, the theorem must reflect the reality of how the compiler will be used. There has been much recent work on more realistic "compositional" compiler correctness aimed at proving correct compilation of components while supporting linking with components compiled from different languages using different compilers. However, the variety of theorems, stated in remarkably different ways, raises questions about what researchers even mean by a "compiler is correct. " In this pearl, we develop a new framework with which to understand compiler correctness theorems in the presence of linking, and apply it to understanding and comparing this diversity of results. In doing so, not only are we better able to assess their relative strengths and weaknesses, but gain insight into what we as a community should expect from compiler correctness theorems of the future.
The beauty of breaking rhythms: affective robot motion design using Jo-Ha-Kyū of bunraku puppet

One of the UNESCO intangible cultural heritages Bunraku puppets can play one of the most beautiful puppet motions in the world. The sophisticated motions of the Bunraku puppet can express emotions interactively with a fixed facial expression overcoming the so-called "Uncanny Valley", simultaneously. In the present paper, we study the Bunraku motions using the famous concept so-called "Jo(Introduction)-Ha(Breaking)-Kyū(Rapid)". These emotional motions are synchronized with the Jo-ha-kyū. As a result, an android robot can express different affective motion synchronized to the different type of emotional chant or narration with a story that we call Jōruri.
Factors Influencing the Chinese Consumers' Usage Intention of Korean Mobile Payment: UTAUT, ISS and TTF Integrated Model

With the mobile payment scale increasing rapidly in Korean, the Chinese customers market becomes one of the largest undeveloped ones, providing precious research opportunities. To maintain domestic customers and attract international customers,it is the priority mission for the Korean mobile payment providers to understand the influencing factors. In this study, Spss 24.0 software was used to describe the demographic data, and Amos 23.0 software was used to test the research hypothesis.We propose and examine a new model,which integrates the D&M ISS and the Task-Technology Fit models into the UTAUT model to assess the impact factors of Korean mobile payment. Based on the above hypothesis and analysis, the contribution of this study has both theoretical and practical aspects:Firstly, the perspective of the three integrated structures will provide richer insights.So we can easily find all variables(except the unaffecting actor of effort expectancy)positively affect the Chinese Usage Intention of the Korean mobile payment. Secondly, we suggest a series of solutions,especially point that aiming at the Chinese potential users' actual requirement factors and improving the quality of Korean mobile payment's customized services for Chinese users are key points in Kakaopay's acceptance and dissemination among the Chinese.
Person Localization in an Indoor Environment with Artificial Intelligence

Associative models are Artificial Intelligence tools and have been used in many applications such as pattern recognition, classification, encryption, among others. In this paper we applied these models to trace a person in an indoor environment by the means of the power of a Wi-Fi signal. We deal with this problem as a classification task. We used a preprocessing for the data to improve the results. Our performance was 95.75%.
A case against mission-critical applications of machine learning

Never Too Late to Share Computational Thinking I commend Judy Robertson’s wonderful blog post “What Children Want to Know About Computers” (Oct. 19, 2018) for illustrating the challenges children face understanding computers, and for the challenges CS educators face helping them. It reminded me of a moment in 2018 when I was explaining programming to my daughter, a recent college graduate who majored in the humanities and was never very interested in computers. She asked, “How does the computer actually work? How does it add two numbers? ” It turned out to be the perfect opportunity to whip out my copy of Digital Equipment Corporation’s 1981 VAX Architecture Handbook. She found the details of the instruction sets, opcodes, registers, and memory fascinating and helped her begin to understand what computers and programs actually do behind the scenes. She has since been studying computers and software development online, as she considers a career in technology. As Robertson said, we can do a much better job teaching our children how computers work. I think it is important to add that young adults— everyone, really—can benefit from a greater understanding of computers and computational thinking. Geoffrey A. Lowney, Issaquah, WA, USA
A Comparative Study of Object Recognition Techniques: Softmax, Linear and Quadratic Discriminant Analysis Based on Convolutional Neural Network Feature Extraction

This paper presents a comparison study on using softmax, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) for object recognition. The least effort is needed for hyper-parameter tuning or selection for all such three classifiers. Convolutional neural network (CNN), using feed-forward-architecture deep learning neural network is employed here for efficient feature extraction and reduction. Then, the extracted, reduced features are fed into the classification comparison. The experiments rely on a small-image CIFAR-10 dataset such that a simple, four convolutional-layer CNN architecture can possibly handle effective feature extraction with hardly over-fitting. Recognition performance evaluations rely on averages of precision, recall, F1 scores and accuracy rates, based on 10-fold cross validation for bias reduction purpose. Such performance measures are implemented under balanced as well as unbalanced --class data, respectively referred to equal and uniform-random-sampling unequal --size class dataset. The results indicate a few bits of recognition performance differences regarding F1 scores as well as accuracy rates among the CNN-LDA, CNN-QDA and CNN-softmax, where the balanced-class and unbalanced-class are separately determined. However, the lowest and the highest of the largest wrong prediction cases are generated by CNN-QDA and CNN-softmax respectively for both balanced and unbalanced-class data.
One-Hop Out-of-Band Control Planes for Multi-Hop Wireless Sensor Networks

Separation of Control and Data Planes (SCDP) is a desirable paradigm for low-power multi-hop wireless sensor networks requiring high network performance and manageability. Existing SCDP networks generally adopt an in-band control plane scheme in that the control-plane messages are delivered by their data-plane networks. The physical coupling of the two planes may lead to undesirable consequences. Recently, multi-radio platforms (e.g., TI CC1350 and OpenMote B) are increasingly available, which make the physical separation of the control and data planes possible. To advance the network architecture design, we propose to leverage on the long-range communication capability of the Low-Power Wide-Area Network (LPWAN) radios to form one-hop out-of-band control planes. LoRaWAN, an open, inexpensive, and ISM band based LPWAN radio, is chosen to prototype our out-of-band control plane called LoRaCP. Several characteristics of LoRaWAN such as downlink-uplink asymmetry and primitive ALOHA media access control need to be dealt with to achieve high reliability and efficiency. To address these challenges, a TDMA-based multi-channel transmission control is designed, which features an urgent channel and negative acknowledgment. On a testbed of 16 nodes, LoRaCP is applied to physically separate the control-plane network of the Collection Tree Protocol (CTP) from its Zigbee-based data-plane network. Extensive experiments show that LoRaCP increases CTP’s packet delivery ratio from 65% to 80% in the presence of external interference, while consuming a per-node average radio power of 2.97mW only.
Aeolian traces: listening to the resonances of wind and human migration

Aeolian Traces is a multimedia artwork installed most recently at the CURRENTS NEW MEDIA festival in Santa Fe, New Mexico. The project collects human migration data to generate gusts of wind around the gallery space and trigger sounds in a database of recordings in the migrants' native languages. A work of data sonification and visualization, this project is a part of the author's long-standing interest in the aesthetics and cultural appropriations of environmental data. In Aeolian Traces, wind data is harvested as a metaphor for geographical and cultural nomadism.
Skycore: Moving Core to the Edge For Untethered and Reliable UAV-Based LTE Networks

Deploying LTE networks on modern UAVs requires a redesign of the key components of the LTE architecture. In this article, we propose a radical, edge-EPC design, called SkyCore, that pushes one of its central functionalities, namely its evolved packet core (EPC) to the extreme edge of the LTE network - collapses the EPC into a single, lightweight, selfcontained entity that is co-located with each of the UAV base-stations. SkyCore incorporates elements that are designed to address the unique challenges facing such a distributed design in the UAV environment, namely the resource-constrained nature of UAV platforms, and the distributed management of UAV and user mobility. We build and deploy a fully functional SkyCore system on a multi-UAV LTE network and showcase its (i) ability to interoperate with commercial LTE basestations as well as smartphones, (ii) support for both hotspot and standalone multi-UAV deployments, and (iii) robust control and data plane performance compared to other EPC variants in this inherently dynamic, unreliable environment.
ACIRS: A Comprehensive Item Based Clustering Approach to Recommend Appropriate Items in Recommender System

Recommender system is that which collect users preferences and suggest the user some similar item that the user might like. It increases our interest in e-commerce sites by suggesting items. Recommender system suggests the appropriate items to the users based on his previous inclination. In clustering techniques, similar users or products are clustered together that can be used for a product recommendation. But it is difficult for an individual clustering algorithm to find a correlation that can help in recommendation. An item-based recommendation system has been proposed here based on clustering algorithms that can find the similarity between items. Item-Item based clustering algorithm first analyzes the user-item rating matrix to find out the relationship between different items and then use these relationships to compute recommendation for users. Finally, our experimental result provides good performance in terms of mean absolute error, precision, recall, and f-measures.
Lambda calculus with algebraic simplification for reduction parallelization by equational reasoning

Parallel reduction is a major component of parallel programming and widely used for summarization and aggregation. It is not well understood, however, what sorts of nontrivial summarizations can be implemented as parallel reductions. This paper develops a calculus named λas, a simply typed lambda calculus with algebraic simplification. This calculus provides a foundation for studying parallelization of complex reductions by equational reasoning. Its key feature is δ abstraction. A δ abstraction is observationally equivalent to the standard λ abstraction, but its body is simplified before the arrival of its arguments by using algebraic properties such as associativity and commutativity. In addition, the type system of λas guarantees that simplifications due to δ abstractions do not lead to serious overheads. The usefulness of λas is demonstrated on examples of developing complex parallel reductions, including those containing more than one reduction operator, loops with jumps, prefix-sum patterns, and even tree manipulations.
‘Less is more?’: a video-based field study about patterned behavioral interaction and team effectiveness

This present study investigates the effect of behavioral interaction patterns of team leaders and followers on team effectiveness. At first, the focus is on the number of interaction patterns and its complexity. Secondly, the focus is on the specific behaviors displayed within the detected interaction patterns. This cross-sectional field study includes three methods: 1) inter-reliable coding of leader and follower behaviors during staff meetings; 2) surveys of 111 these same leaders and 1445 followers; 3) expert scores of each team’s level of effectiveness. Multiple hierarchical regression analyses showed that effective teams exhibit fewer and more simple interaction patterns. The predicted moderation effect of the degree of knowledge-intensive work was found in part. Focus on the specific nineteen behaviors within the detected interaction patterns led to the finding that transformational and transactional behavior were not significantly related with team effectiveness. However, counter-effective behavior of the leader was found significantly negative. It is concluded that the role of the follower in team performances must not be underestimated because its occurrence within interaction patterns is associated with team effectiveness. The findings of this study indicate that within patterned interaction, less is more. Practical implications, and strengths and limitations of this current study are discussed. Moreover, future research directions are proposed.
FACS at 40: facial action coding system panel

Creating emotionally impactful characters has been one of the biggest challenges in computer graphics. Key to this has been understanding how faces express emotion. In 1978 research started in coding human expressions. The Facial Action Coding System (FACS) would become one of the cornerstones of facial animation of digital characters. Today FACS is recognized as the gold standard in scientific facial research and animation. Forty years on, we discuss the strengths and limitations of FACS, its relevance in the age of Machine Learning and what people are doing to improve upon FACS in animation. The panel features leading experts: J.P. Lewis, Erika Rosenberg, Vladimir Mastilovic, Mark Sagar and hosted by Mike Seymour.
Student Relationship in Higher Education Using Data Mining Techniques

-The aim of research paper is to improve the current trends in the higher education systems to understand from the outside which factors might create loyal students. The necessity of having loyal students motivates higher education systems to know them well, one way to do this is by using valid management and processing of the students database. Data mining methods represent a valid approach for the extraction of precious information from existing students to manage relations with future students. This may indicate at an early stage which type of students will potentially be enrolled and what areas to concentrate upon in higher education systems for support. For this purpose the data mining framework is used for mining related to academic data from enrolled students. The rule generation process is based on the decision tree as a classification method. The generated rules are studied and evaluated using different evaluation methods and the main attributes that may affect the student’s loyalty have been highlighted. Software that facilitates the use of the generated rules is built using VB.net programming language which allows the higher education systems to predict thestudent’s loyalty (numbers of enrolled students) so that they can manage and prepare necessary resources for the new enrolled students.