Reviewerly/exHarmony huggingface dataset:
Rate-distortion optimization guided autoencoder for isometric embedding in Euclidean latent space

To analyze high-dimensional and complex data in the real world, deep generative models, such as variational autoencoder (VAE) embed data in a low-dimensional space (latent space) and learn a probabilistic model in the latent space. However, they struggle to accurately reproduce the probability distribution function (PDF) in the input space from that in the latent space. If the embedding were isometric, this issue can be solved, because the relation of PDFs can become tractable. To achieve isometric property, we propose Rate- Distortion Optimization guided autoencoder inspired by orthonormal transform coding. We show our method has the following properties: (i) the Jacobian matrix between the input space and a Euclidean latent space forms a constantlyscaled orthonormal system and enables isometric data embedding; (ii) the relation of PDFs in both spaces can become tractable one such as proportional relation. Furthermore, our method outperforms state-of-the-art methods in unsupervised anomaly detection with four public datasets.
Generalized Multi-Relational Graph Convolution Network.

Graph Convolutional Networks (GCNs) have received increasing attention in recent machine learning. How to effectively leverage the rich structural information in complex graphs, such as knowledge graphs with heterogeneous types of entities and relations, is a primary open challenge in the field. Most GCN methods are either restricted to graphs with a homogeneous type of edges (e.g., citation links only), or focusing on representation learning for nodes only instead of jointly optimizing the embeddings of both nodes and edges for target-driven objectives. This paper addresses these limitations by proposing a novel framework, namely the GEneralized Multi-relational Graph Convolutional Networks (GEM-GCN), which combines the power of GCNs in graph-based belief propagation and the strengths of advanced knowledge-base embedding methods, and goes beyond. Our theoretical analysis shows that GEM-GCN offers an elegant unification of several well-known GCN methods as specific cases, with a new perspective of graph convolution. Experimental results on benchmark datasets show the advantageous performance of GEM-GCN over strong baseline methods in the tasks of knowledge graph alignment and entity classification.
An Improved Approach for Image Segmentation and Three-Dimensional Reconstruction

Themain contribution of this paper consists of introducing a novelmodel of three-dimensional reconstruction from multiple two-dimensional images. Actually, our proposedmodel presents the followingmain advantages; first, we improve the original region fitting energy in the general region-based level set method by an anisotropic diffusion to evolve the contour. Second, we use the Dijkstra algorithm to improve and allow simple and free initializations. Third, we adopt the domain decomposition method to reduce the computational cost for high-resolution images. Finally, in order to prove the efficiency and accuracy of our proposed method, experiments were performed on synthetic and real images.
THE BUYING RESPONSE MODEL “ATR” FOR SPORTS SPONSORSHIP ACTIVITIES -EVIDENCE FROM SPORTS SPONSORSHIP MARKET IN JORDAN

This study aims to investigate the effect of sports sponsorship activities on the buying behavior of sports audience, in addition to determine the statistical differences in the level of sports sponsorship activities as well as the buying response according to some organizational and demographics. The research population consists of Jordanian sports audience who living in Amman city. The purposive sampling technique is used. The researcher selects the respondents from the coffee shops, cafe, the famous malls in Amman, and the social media users. The required sample size is 385. The unit of analysis is the Jordanian who has an interest in attending and watching the sports activities and over 19 years old living in Amman. The study concludes that the sport sponsorship activities have a strong positive statistical effect on the buying response ATR of the Jordanian sports audience. It is found that there are statistically significant differences in the awareness, so the males are more aware than females and the university degree holders are more aware than others. It is also found that the age group between 42 - 46 years of the Jordanian audience is the most experienced people, but it is found that there are no differences between the sports audience segments in the repeated purchase stage. Finally, it is found that there are statistically significant differences in the sponsorship activities tend to the telecom companies in Jordan. The study presents a set of recommendations that enhance using the sponsorship in the local sports market. KEY WORDS: Sports Sponsorship , Buying Response , ATR Model , Jordan.
Energy-Based Processes for Exchangeable Data

Recently there has been growing interest in modeling sets with exchangeability such as point clouds. A shortcoming of current approaches is that they restrict the cardinality of the sets considered or can only express limited forms of distribution over unobserved data. To overcome these limitations, we introduce Energy-Based Processes (EBPs), which extend energy based models to exchangeable data while allowing neural network parameterizations of the energy function. A key advantage of these models is the ability to express more flexible distributions over sets without restricting their cardinality. We develop an efficient training procedure for EBPs that demonstrates state-of-the-art performance on a variety of tasks such as point cloud generation, classification, denoising, and image completion.
The gait analysis system based on the first-person video for applications on home health care

Gait can be a concrete manifestation of an individual's athletic traits, but also can become an essential feature of the judgment of the physical condition. Making an accurate gait analysis has become one of the critical medical information needs. However, the measurement and analysis of gait in medical institutions are still mainly based on the visual experience of physicians, or expensive and complicated motion capture system that are not suitable for use in the home environment; and the simple wearable sensors do not provide enough information to assist medical judgment. In this research project, a novel gait analysis method based on an image recorder is proposed, which has the advantages of making the gait analysis more natural and more suitable for the home environment. A more realistic gait profile can be measured without having to be in close contact with the observed body parts like motion capture system or inertial measurement device. This research project, a gait analysis system based on the first-person video will be developed, and the characteristics of captured video will be transformed into the features of clinical medicine. This system will be used in combination with traditional clinical medicine. The experimental results show that the generated measurement information can be correctly converted for clinical medicine using, and is more suitable for home environments. Further, we will cooperate with clinicians to provide a useful evaluation of home exercise rehabilitation and predicting the longterm trend. That can give the appropriate treatment regimen matching the development of the disease.
Data association rules mining method based on genetic optimization algorithm

Abstract Traditional data mining methods usually need to visit the database repeatedly to determine the frequent item set, which makes the data server burden heavier and reduces the efficiency of data mining. To solve this problem, this paper combines the immune mechanism and genetic algorithm dynamically to improve the traditional genetic algorithm (GA), and proposes a data association rule mining method based on improved immune genetic algorithm (IIGA), which realize the effective analysis of big data. The experimental results show that the algorithm we proposed is better than immune genetic algorithm and Apriori algorithm in data mining time and association rules mining accuracy, which can be better applied to data analysis. The research results have positive reference significance for the field of data mining.
Multilevel Model-Based Clustering: A New Proposal of Maximum-A-Posteriori Assignment

We deal with the problem of latent variable prediction in the context of multilevel latent class models for categorical responses provided by individuals nested in groups. In particular, we propose a posterior assignment rule that jointly predicts the individual- and group-level latent variables. This proposal is alternative to the common maximum-a-posteriori rule, which is based on first predicting the latent variables at cluster level and, then, those at individual level. To illustrate the proposal, we show the results of two simulation studies and two applications on data related to the national and the international assessment of student skills.
An Ultrasonic Line Follower Robot to Detect Obstacles and Edges for Industrial and Rescue Operations

Line follower is a smart autonomous robot that detects or follows a visible line embedded in the ground.The trail is predetermined and can be selected with a high contrast color or with a black line visible on the trail surface.Infrared sensors are used to detect these lines.Typically speaking, the area unit of the infrared sensors is used to locate the path that the robot has to follow.The robot movement is automatic and can be used for applications of long distances.It is the fundamental line follower robot's function.The device proposed for commercial, medical, rescue and military operations are extremely useful.In particular, these past constraints are no longer necessary with recent technological advances in computing.The production of tracking systems can now be made more capable of reliably estimating the target location behind the obstacle.The benefit of these technologies consists in the possibility of using an ultrasonic method for measurement without direct contact with a target.Different models and systems for indoor and outdoor object detection have been described in the literature.Using optical, heat base, infrared and ultrasonic approaches, object localization techniques were introduced.Indoor positioning systems monitor and locate objects and enclose environments inside buildings.Wireless methods, optical tracking, and ultrasonic techniques are used for object position detection systems.The goal of this study is to develop a monitoring system that follows certain paths and can detect objects and edges using ultrasonic frequencies.If some object is put, a regular line follower will try to move and smash the obstacle.This prototype of line follower robot tries to push the limit little to overcome this issue.It has been built in a way that any obstacle in front of it can be identified.It will stop and will not pass until the barrier remains.Also, it is able to identify every front edge and comply similarly.In industries, such as material handling, this type of robot performs many tasks.These robots are also used as machine-controlled carrier instruments in old conveyor belts switching industries.
How to Efficiently Store Intermediate Results in Quantum Computing: Theoretical Explanation of the Current Algorithm

In complex time-consuming computations, we rarely have uninterrupted access to a high performance computer: usually, in the process of computation, some interruptions happen, so we need to store intermediate results until computations resume. To decrease the probability of a mistake, it is often necessary to run several identical computations in parallel, in which case several identical intermediate results need to be stored. In particular, for quantum computing, we need to store several independent identical copies of the corresponding qubits—quantum versions of bits. Storing qubit states is not easy, but it is possible to compress the corresponding multi-qubit states: for example, it is possible to store the resulting 3-qubit state by using only two qubits. In principle, there are many different ways to store the state of 3 independent identical qubits by using two qubits. In this paper, we show that the current algorithm for such storage is uniquely determined by the natural symmetry requirements.
QOPT: Optimistic Value Function Decentralization for Cooperative Multi-Agent Reinforcement Learning.

We propose a novel value-based algorithm for cooperative multi-agent reinforcement learning, under the paradigm of centralized training with decentralized execution. The proposed algorithm, coined QOPT, is based on the optimistic training scheme using two action-value estimators with separate roles: (i) true action-value estimation and (ii) decentralization of optimal action. By construction, our framework allows the latter action-value estimator to achieve (ii) while representing a richer class of joint action-value estimators than that of the state-of-the-art algorithm, i.e., QMIX. Our experiments demonstrate that QOPT newly achieves state-of-the-art performance in the StarCraft Multi-Agent Challenge environment. In particular, ours significantly outperform the baselines for the case where non-cooperative behaviors are penalized more aggressively.
Unveiling the implicit knowledge, one scenario at a time

When defining virtual reality applications with complex procedures, such as medical operations or mechanical assembly or maintenance procedures, the complexity and the variability of the procedures make the definition of the scenario difficult and time-consuming. Indeed, the variability complicates the definition of the scenario by the experts, and its combinatorics demand a comprehension effort for the developer, which is often out of reach. Additionally, the experts have a hard time explaining the procedures with a sufficient level of details, as they usually forget to mention some actions that are, in fact, important for the application. To ease the creation of scenario, we propose a complete methodology, based on (1) an iterative process composed of: (2) the recording of actions in virtual reality to create sequences of actions and (3) the use of mathematical tools that can generate a complete scenario from a few of those sequences, with (4) graphical visualization of the scenarios and complexity indicators. This process helps the expert to determine the sequences that must be recorded to obtain a scenario with the required variability.
Wave Height Interval Distribution Method Based on Nonlinear Shallow Water Wave Function

Li, X.; Zhao, Y., and Jin, H., 2020. Wave height interval distribution method based on nonlinear shallow water wave function. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 843–846. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the ability to analyze and judge the interval distribution of wave height, it is necessary to estimate the interval distribution of wave height. An interval distribution estimation method of wave height based on nonlinear shallow water wave function is proposed. A statistical time series analysis model for estimating the interval distribution of sea wave height is constructed, large data mining and feature extraction for estimating the interval distribution of sea wave height are carried out by adopting a large data feature detection method, ordered clustering of statistical feature sequences of the interval distribution of sea wave height is carried out based on a nonlinear shallow water wave function estimation idea, information clustering and attribute merging in the process of estimating the interval distribution of sea wave height are carried out by combining a fuzzy C-means clustering analysis method. The feature quantity of association rules of statistical time series of wave height interval distribution is extracted, and the association rule set of statistical time series of wave height interval distribution is analyzed by using principal component analysis method, so as to realize accurate estimation of wave height interval distribution quantity in weighted Markov chain. The simulation results show that the method has high accuracy in estimating the interval distribution of wave height and improves the quantitative analysis capability of the interval distribution of wave height.
OCR Quality and NLP Preprocessing

We present initial experiments to evaluate the performance of tasks such as Part of Speech Tagging on data corrupted by Optical Character Recognition (OCR). Our results, based on English and German data, using artificial experiments as well as initial real OCRed data indicate that already a small drop in OCR quality considerably increases the error rates, which would have a significant impact on subsequent processing steps.
Accurate DOA Estimation Using Modified ESPRIT Algorithm

Direction of Arrival (DOA) estimation represents one of the key issues in the field of signal processing for the present and future 5G and radar engineering applications.The problem of the inaccuracy of DOA estimation has attracted considerable interest from academia.One of the advantageous common techniques that have been followed to improve the accuracy of DOA is the Estimation of the Signal Parameters via the Rotational Invariance Technique (ESPRIT) algorithm, albeit ESPRIT suffers from the challenge of estimation bias at short separation distances when two sources are very close to each other.Hence, in this paper, a modified-ESPRIT algorithm is proposed to improve the accuracy of the DOA.The modification of ESPRIT is presented by increasing the phase difference between the received signals.The idea is to add a pre-processing before the frame of the ESPRIT algorithm; this is done by introducing a double-balanced mixer and 90 o signal splitter at the output of each antenna.The performance evaluation shows that using only the ESPRIT algorithm exhibits large estimation bias when two sources are very close to each other, while the performance is improved noticeably with the proposed modified-ESPRIT.It can detect and resolve two sources with an enhancement of 83.09% over traditional ESPRIT when the number of snapshots is very short, while an enhancement of 54.4% is achieved when source separation between (1 to 4) degrees and 77.8% achieved when Signal to Noise Ratio (SNR) between 0-10 dB and of 79.04% when four or fewer elements are used.The obtained results validate the performance efficiency of the proposed algorithm in terms of less Root Mean Square Error (RMSE), and a smaller number of snapshots.
A Generalization of Self-Improving Algorithms

Ailon et al. [SICOMP'11] proposed self-improving algorithms for sorting and Delaunay triangulation (DT) when the input instances x₁,⋯,x_n follow some unknown product distribution. That is, x_i comes from a fixed unknown distribution 𝒟_i, and the x_i’s are drawn independently. After spending O(n^{1+e}) time in a learning phase, the subsequent expected running time is O((n+ H)/e), where H ∈ {H_S,H_DT}, and H_S and H_DT are the entropies of the distributions of the sorting and DT output, respectively. In this paper, we allow dependence among the x_i’s under the group product distribution. There is a hidden partition of [1,n] into groups; the x_i’s in the k-th group are fixed unknown functions of the same hidden variable u_k; and the u_k’s are drawn from an unknown product distribution. We describe self-improving algorithms for sorting and DT under this model when the functions that map u_k to x_i’s are well-behaved. After an O(poly(n))-time training phase, we achieve O(n + H_S) and O(nα(n) + H_DT) expected running times for sorting and DT, respectively, where α(⋅) is the inverse Ackermann function.
Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors.

Convolutional neural network (CNN) models for computer vision are powerful but lack explainability in their most basic form. This deficiency remains a key challenge when applying CNNs in important domains. Recent work for explanations through feature importance of approximate linear models has moved from input-level features (pixels or segments) to features from mid-layer feature maps in the guise of concept activation vectors (CAVs). CAVs contain concept-level information and could be learnt via Clustering. In this work, we rethink the ACE algorithm of Ghorbani et~al., proposing an alternative concept-based explanation framework. Based on the requirements of fidelity (approximate models) and interpretability (being meaningful to people), we design measurements and evaluate a range of dimensionality reduction methods for alignment with our framework. We find that non-negative concept activation vectors from non-negative matrix factorization provide superior performance in interpretability and fidelity based on computational and human subject experiments. Our framework provides both local and global concept-level explanations for pre-trained CNN models.
Matching Affinity Clustering: Improved Hierarchical Clustering at Scale with Guarantees

Hierarchical clustering is a stronger extension of one of today's most influential unsupervised learning methods: clustering. The goal of this method is to create a hierarchy of clusters, thus constructing cluster evolutionary history and simultaneously finding clusterings at all resolutions. We propose four traits of interest for hierarchical clustering algorithms: (1) empirical performance, (2) theoretical guarantees, (3) balance (the minimum ratio between cluster sizes), and (4) scalability. While a number of algorithms are designed to achieve one to two of these traits at a time, there exist none that achieve all four. Inspired by Bateni et al.'s scalable and empirically successful Affinity Clustering [NeurIPs 2017], we introduce Affinity's successor, Matching Affinity Clustering. Like its predecessor, Matching Affinity Clustering maintains strong empirical performance, even outperforming Affinity when the dataset is size 2n and clusters are balanced, and uses Massively Parallel Communication as its distributed model. Designed to maintain provably balanced clusters, we show that our algorithm achieves a (1/3-e)-approximation for Moseley and Wang's revenue (the dual to Dasgupta's cost) when the data set is of size 2n, and a (1/9-e)-approximation in general. We prove the former approximation is tight, and also that Affinity Clustering cannot do better than a 1/O(n)-approximation. In addition, we see that our algorithm empirically performs similarly to Affinity Clustering and k-Means, outperforming many state-of-the-art serial algorithms. Along the way, we also introduce an efficient k-sized maximum matching algorithm in the MPC model.