Reviewerly/exHarmony huggingface dataset:
The Extension of Phase Correlation to Image Perspective Distortions Based on Particle Swarm Optimization

Phase correlation is one of the widely used image registration method in medical image processing and remote sensing. One of the main limitations of the phase correlation-based registration method is that it can only cope with Euclidean transformations, such as translation, rotation and scale, which constrain its application in wider fields, such as multi-view image matching, image-based navigation, etc. In this paper, we extended the phase correlation to perspective transformation by the combination of particle swarm optimization. Inspired by optic lens alignment based on interference, we propose to use the quality of PC fringes as the similarity, and then the aim of registration is to search for the optimized geometric transformation operator, which obtain the maximize value of PC-based similarity function through particle swarm optimization approach. The proposed method is validated by image registration experiments using simulated terrain shading, texture and natural landscape images containing different challenges, including illumination variation, lack of texture, motion blur, occlusion and geometric distortions. Further, image-based navigation experiments are carried out to demonstrate that the proposed method is able to correctly recover the trajectory of camera using multimodal target and reference image. Even under great radiometric and geometric distortions, the proposed method is able to achieve 0.1 sub-pixel matching accuracy on average while other methods fail to find the correspondence.
Deterministic Reduction of Integer Nonsingular Linear System Solving to Matrix Multiplication

We present a deterministic reduction to matrix multiplication for the problem of linear system solving: given as input a nonsingular A \in \Z^n \times n and b \in \Z^n \times 1 , compute A^-1 b. We give an algorithm that computes the minimal integer e such that all denominators of the entries in 2^eA^-1 are relatively prime to 2. Then, for a b that has entries with bitlength O(n) times as large as the bitlength of entries in A, we give an algorithm to produce the 2-adic expansion of 2^eA^-1 b up to a precision high enough such that A^-1 b over \Q can be recovered using rational number reconstruction. Both e and the 2-adic expansion can be computed in O(\MM(n,łog n + łog ||A||) \times (łog n) (łog n + łoglog ||A||)) bit operations. Here, ||A||= \max_ij |A_ij | and \MM(n,d) is the cost to multiply together, modulo 2^d, two n \times n integer matrices. Our approach is based on the previously known reductions of linear system solving to matrix multiplication which use randomization to find an integer lifting modulus X that is relatively prime to \det A. Here, we derandomize by first computing a permutation P, a unit upper triangular M, and a diagonal S with \det S a power of two, such that U := APMS^-1 is an integer matrix with 2 \perp \det U. This allows our modulus X to be chosen a power of 2.
Creek: Inter Many-to-Many Coflows Scheduling for Datacenter Networks

Datacenter networked applications, often require multiple data transfer flows that semantically constitute a coflow group. A coflow is thus considered completed when all the transfers in the coflow are completed. Hence, application performance is optimized whenever the completion time of a coflow is minimized, rather than that of the flows composing it. Currently, popular coflow scheduling algorithms are mostly centralized, and they incur high overheads. The decentralized approach in the “many-to-many” scenario also incurs high communication overheads due to the communication among the local controllers. In this paper, we present a coflow scheduling mechanism that aims to minimize the coflow completion time for coflows that show a many-to-many communication pattern, and as a byproduct communication overhead cost is also minimized. Our algorithm preserves compatibility with existing commodity switches and network protocols and improves the coflow completion times on average by 1.8 times compared to the baseline as demonstrated via testbed implementation and large-scale simulation.
Characterizing the Impact of Geometric Properties of Word Embeddings on Task Performance

Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors. However, geometric properties of the continuous feature space contribute directly to the use of embedding features in downstream models, and are largely unexplored. We consider four properties of word embedding geometry, namely: position relative to the origin, distribution of features in the vector space, global pairwise distances, and local pairwise distances. We define a sequence of transformations to generate new embeddings that expose subsets of these properties to downstream models and evaluate change in task performance to understand the contribution of each property to NLP models. We transform publicly available pretrained embeddings from three popular toolkits (word2vec, GloVe, and FastText) and evaluate on a variety of intrinsic tasks, which model linguistic information in the vector space, and extrinsic tasks, which use vectors as input to machine learning models. We find that intrinsic evaluations are highly sensitive to absolute position, while extrinsic tasks rely primarily on local similarity. Our findings suggest that future embedding models and post-processing techniques should focus primarily on similarity to nearby points in vector space.
Major Writing Challenges Experienced by EFL Learners in Soran University

The current research aims to investigate the challenges that EFL learners face in essay writing skills and the common errors they make in their English writing. One of the major aspects of language development and academic achievement among students at all levels of the education system is the capacity to achieve communicative competence in writing skills. Though, one of the essential problem learner faces at university is, learning to master essay writing skills. A descriptive survey research design was adopted in this study. The sample of the study are 32 randomly chosen third year students of English Department at the Soran University. To achieve the aims of the research, a mixed methodology was selected, through which data was collected through the students' questionnaire, and a collection analysis of thirty-two randomly chosen essays. A research results obtained from students' questionnaires indicated that major difficulties that student' encounter are in grammar, organizing ideas, punctuations, and vocabulary. The challenges that students face in writing essay include: incorrect use of grammar, first language interference, limited vocabulary, punctuation marks, and word order. Based on these results, it is important for teachers to take into account students' writing challenges, and the explanation of more writing sessions are among the pedagogical implications.
An efficient distributed storage strategy for blockchain

As the blockchain technology grows, the scale of the network is becoming huger and huger, which brings us a troublesome question: how to allocate our data into each computer of a blockchain network? For example, we can store our data from one computer, until the memory space filled, and then switch to another, spending a lot of time searching in sole computer. Or we can only put a small amount of data into one computer, resulting in a huge network and plenty of transmission time. In this paper, we compute the cost of all the allocations according to the mathematical model. By studying the formula, we get the solution of balancing the search time and the space occupancy by users' input. Based on the result, we design a data allocation strategy that can decide how many data allocated to an individual computer.
Real-Time Head Pose Estimation by Tracking and Detection of Keypoints and Facial Landmarks

We introduce a novel fusion framework for real-time head pose estimation using a tailored Kalman Filter. This approach estimates the pose from intensity images in monocular video data. The method is robust to extreme head rotations and varying illumination, with real-time capability. Our framework incorporates the head pose computed from a keypoint-based tracking scheme into the prediction step of the Kalman Filter and the head pose computed from a facial-landmark-based detection scheme into the correction step. The head pose from the tracking scheme is estimated from 2D keypoints tracked in two consecutive frames in the region of the head and their 3D projection on a simple geometric model. In contrast, the head pose from the detection scheme is estimated from 2D facial landmarks detected in each frame and their 3D correspondences retrieved through triangulation. In each scheme, the head pose results from the minimization of the reprojection error from the 3D-2D correspondences. In each iteration, we update the state transition matrix of the filter and subsequently the estimated covariance. We evaluated our approach on a publicly available dataset and compared with related methods of the state of the art. Our approach could achieve similar performance in terms of mean average error, while operating in real time. Furthermore, we tested our method on our own dataset, to evaluate its performance in the presence of large head rotations. We show good results even in cases where facial landmarks are partially occluded.
Virtual Reality Scene Construction Based on Multimodal Video Scene Segmentation Algorithm

Video scene segmentation has become one of the research hotspots in the video field because of its important role in improving retrieval accuracy, and plays a very important role in the construction of virtual scenes. In order to realize fast and accurate video scene segmentation, this paper proposes a multi-modal video scene segmentation algorithm based on ant colony algorithm. The algorithm extracts the physical features of different modes in key frames based on the idea of multi-modal feature fusion. The similarity between the same modal data and the correlation of different modal data are combined, and the similarity between different lenses is calculated. The lens similarity matrix is constructed and the ant colony algorithm is used to segment the video scene. The experimental data proves that the algorithm has a good segmentation effect on the video scene.
Unlocking the Black Box of Wearable Intelligence: Ethical Considerations and Social Impact

Computational intelligence is making its way into a variety of popular consumer products, including wearable physiological monitors such as activity trackers and sleep trackers. Such products are very convenient for the user, but this convenience is the result of a trade-off that has ethical implications, since in almost all cases it denies the user access to their own raw data underlying the easy-to-understand analyses that the products generate for them. One problem with this is that the user is not made aware of the uncertainty of the conclusions or analyses drawn from the data; another is that it is difficult for the user to reuse his or her data in other contexts, such as to combine data from multiple sources. Even if the user did have full control of the data, this would only solve part of the problem, because most people do not have the special skills required to analyze such data. This overall problem could be solved through collaboration between the data owner and a data analysis expert, though this again introduces further problems, notably that of preserving the data owner's privacy. In this paper we analyze the aforementioned issues pertaining to the ethics of wearable intelligence, propose possible approaches to handling them, and discuss the potential social impact of the technology if the issues can be successfully overcome.
QoS-Aware Framework for Performance Enhancement of SOA in Enterprise IT Environments

Service-oriented architecture (SOA) has gained great attention in the enterprise information technology environments (EITE) due to its technically adapted performance and its affordable cost. As a part of the successful quality of service (QoS) scenario, providing software development in a service-based conceptual style for the business companies has become a vital issue. However, this would require more hardware resources, which increase cost and complexity. The main objective of this study is to introduce a new performance-oriented integration design (POID) framework with five middleware algorithms to reliably achieve SOA constraints. The POID framework is proposed to provide two features: (i) acting as a decision support system (DSS) to guide the software architects and designers to build software architectures with better QoS attributes in terms of the scalability and end-to-end performance, and (ii) achieving high accuracy in recommending the best composite services in the simple and complex SOA integration contexts. A set of case studies based on real experiments are conducted in a telecom environment are demonstrated. The experimental results prove that the POID framework achieves better accuracy (97%-98%), average availability (92.18% - 97.89%), and enhances the average response time by 17%.
Learnability for the Information Bottleneck

The Information Bottleneck (IB) method provides an insightful and principled approach for balancing compression and prediction for representation learning. The IB objective I ( X ; Z ) − β I ( Y ; Z ) employs a Lagrange multiplier β to tune this trade-off. However, in practice, not only is β chosen empirically without theoretical guidance, there is also a lack of theoretical understanding between β , learnability, the intrinsic nature of the dataset and model capacity. In this paper, we show that if β is improperly chosen, learning cannot happen—the trivial representation P ( Z | X ) = P ( Z ) becomes the global minimum of the IB objective. We show how this can be avoided, by identifying a sharp phase transition between the unlearnable and the learnable which arises as β is varied. This phase transition defines the concept of IB-Learnability. We prove several sufficient conditions for IB-Learnability, which provides theoretical guidance for choosing a good β . We further show that IB-learnability is determined by the largest confident, typical and imbalanced subset of the examples (the conspicuous subset), and discuss its relation with model capacity. We give practical algorithms to estimate the minimum β for a given dataset. We also empirically demonstrate our theoretical conditions with analyses of synthetic datasets, MNIST and CIFAR10.
Hierarchical Guided-Image-Filtering for Efficient Stereo Matching

Stereo matching is complicated by the uneven distribution of textures on the image pairs. We address this problem by applying the edge-preserving guided-Image-filtering (GIF) at different resolutions. In contrast to most multi-scale stereo matching algorithms, parameters of the proposed hierarchical GIF model are in an innovative weighted-combination scheme to generate an improved matching cost volume. Our method draws its strength from exploiting texture in various resolution levels and performing an effective mixture of the derived parameters. This novel approach advances our recently proposed algorithm, the pervasive guided-image-filtering scheme, by equipping it with hierarchical filtering modules, leading to disparity images with more details. The approach ensures as many different-scale patterns as possible to be involved in the cost aggregation and hence improves matching accuracy. The experimental results show that the proposed scheme achieves the best matching accuracy when compared with six well-recognized cutting-edge algorithms using version 3 of the Middlebury stereo evaluation data sets.
Mining Public Opinion on Ride-Hailing Service Providers using Aspect-Based Sentiment Analysis

The use of ride-hailing services as a solution to current transportation problems is currently attracting much attention.Their benefits and convenience mean many people use them in their everyday lives and discuss them in the social media.As a result, ride-hailing service providers utilize social media to capture customers' opinions and to market their services.If these opinions and comments are analyzed, service providers can obtain feedback to evaluate their services in order to achieve customer satisfaction.This study combines the text mining approach, in the form of aspect-based sentiment analysis to identify topics in customer opinions and their sentiments, with scoring of ride-hailing service providers in general, and more specifically based on the topics and sentiments.The study analyzes customers' opinions on Twitter of three ride-hailing service providers.Text data were classified based on six topics derived from the topic modeling process, along with the sentiments expressed on them.Scoring of the three ride-hailing service providers was based on the number of positive and negative comments in relation to each topic, as well as overall comments.The results of the study can be used as input to evaluate and improve the service in Indonesia, thus the customer satisfaction and loyalty can be maintained and improved.
Robust3D: a robust 3D face reconstruction application

In the process of reconstructing a historical event such as a rock concert only from video, the reconstruction of faces and expressions of the musicians is obviously important. However, in the process of rebuilding appearance, because of the low quality of the video of the recorded concert, the result of the reconstruction may be far from the real appearance. In this paper, a robust 3D face reconstruction application is described that can be applied to a video recording. The application first uses DeblurGAN program to run anti-ambiguity calculation and removes the ambiguity in the concert video. Then, the super-resolution program is used to enlarge every frame of the concert video by four times, thus making every frame of the video clearer. Finally, the 3D faces are obtained after 3D reconstruction of the processed video frames via the 3DMM_CNN program.
Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities

We propose weighted inner product similarity (WIPS) for neural network-based graph embedding. In addition to the parameters of neural networks, we optimize the weights of the inner product by allowing positive and negative values. Despite its simplicity, WIPS can approximate arbitrary general similarities including positive definite, conditionally positive definite, and indefinite kernels. WIPS is free from similarity model selection, since it can learn any similarity models such as cosine similarity, negative Poincaré distance and negative Wasserstein distance. Our experiments show that the proposed method can learn high-quality distributed representations of nodes from real datasets, leading to an accurate approximation of similarities as well as high performance in inductive tasks.
An Enhanced High-Order Variational Model Based on Speckle Noise Removal With $G^0$ Distribution

Speckle noise removal problem has been researched under the framework of regularization-based approaches. The regularizer is normally defined as total variation (TV) that induces staircase effect. Although higher-order regularizer can conquer the staircase effect to some extent, it often leads to blurred. Considering the upper questions, the combination of first and second-order regularizer will be an effective and prior method to tackle speckle noise removal. So a variational model with hybrid TV and higher-order total curvature (TC) term is proposed in this paper, the data fidelity term is derived based on G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> distribution. In order to preserve the edge detail better, the boundary detection function is combined with the regularizer. Furthermore, the Mellin transform is used to estimate the parameters of the model. To address the speckle noise removal optimization problem, alternating direction method of multipliers (ADMM) framework is employed to design a convex numerical method for the proposed model. The numerical method can be used to update the variables flexibly as required by the hybrid regularizer. The numerous experiments were performed on both synthetic and real SAR images. Compared with some classical and state-of-theart SAR despeckling methods, experiment results demonstrate the improved performance of the proposed method, including that speckle noise can be removed effectively, and staircase effect can be prevented while preserving image feature.
Utilizing Non-Parallel Text for Style Transfer by Making Partial Comparisons

Text style transfer aims to rephrase a given sentence into a different style without changing its original content. Since parallel corpora (i.e. sentence pairs with the same content but different styles) are usually unavailable, most previous works solely guide the transfer process with distributional information, i.e. using style-related classifiers or language models, which neglect the correspondence of instances, leading to poor transfer performance, especially for the content preservation. In this paper, we propose making partial comparisons to explicitly model the content and style correspondence of instances, respectively. To train the partial comparators, we propose methods to extract partial-parallel training instances automatically from the non-parallel data, and to further enhance the training process by using data augmentation. We perform experiments that compare our method to other existing approaches on two review datasets. Both automatic and manual evaluations show that our approach can significantly improve the performance of existing adversarial methods, and outperforms most state-of-the-art models. Our code and data will be available on Github.
A Proposed Model-Driven Approach to Manage Architectural Technical Debt Life Cycle

Architectural Technical Debt (ATD) is a metaphor used to describe consciously decisions taken by software architects to accomplish short-term goals but possibly negatively affecting the long-term health of the system. However, difficulties arise when repayment strategies are defined because software architects need to be aware of the consequences of these strategies over others decisions in the software architecture. This article proposes REBEL, a semi-automated model-driven approach that exploits natural language processing, machine learning and model checking techniques on heterogeneous project artifacts to build a model that allows to locate and visualize the impact produced by the consciously injected ATD and its repayment strategy on the other architectural decisions. The technique is illustrated with a data analytics project in Colombia where software architects are unaware of the consequences of the repayment strategies. This proposal seeks to support teams of architects to make explicit the current and future impact of the ATD injected as a result of decisions taken, focusing on the architectural level rather than code level.
Cinematic scientific visualization

The Advanced Visualization Lab (AVL) is part of the the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. The AVL is led by Professor Donna Cox, who coined the term "Renaissance Team", with the belief that bringing together specialists of diverse backgrounds creates a team that is greater than the sum of its parts, and members of the AVL team reflect that in our interdisciplinarity. We specialize in creating high-quality cinematic scientific visualizations of supercomputer simulations for public outreach.