Reviewerly/exHarmony huggingface dataset:
Domain problem‐solving expert identification in community question answering

Abstract Question‐Answering (Q&amp;A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&amp;A sites, or Community Question Answering (CQA), mainly depends on the high‐quality content continuously contributed by users with high‐level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&amp;A communities to encourage commitment. In a highly competitive market environment, CQA managers have to take measures to retain and nurture users, especially superior contributors. However, current expertise scoring techniques adopted in CQA often give much credit to very active users and fail to identify real experts. This study aims to develop a robust and practical expert identification framework for Q&amp;A communities, by combining well‐designed expertise scoring technique and probabilistic clustering model. With regard to expert identification, a numerical metric of users' expertise is developed as the optimal expert finding strategy, and a clustering algorithm based on Gaussian‐Gamma mixture model (GGMM) is proposed to efficiently distinguish experts from nonexperts. In the experiments, the proposed method is applied to real‐world datasets collected from subcommunities of Stack Exchange Q&amp;A networks. Results obtained from comparative experiments show that our method achieves better performance than the state‐of‐the‐art methods and demonstrate the effectiveness of the proposed framework. The analysis shows that the framework which combines the proposed expertise scoring technique and Gaussian–Gamma mixture clustering model is capable of detecting excellent domain problem‐solving experts who exhibit both domain interest and expertise.
Design and Testing of Frequency Adaptive Zero- Crossing Detector as a Synchronizing Technique

In this paper, a simple and efficient technique has been proposed for estimating the synchronizing signals. The proposed method is based on the zero-crossing detection technique. The proposed method works very well even when with variation in the frequency of the input signal while the conventional Zero Crossing Detector (ZCD) fails. The proposed technique shows a very fast response in case of phase change, distorted signal and frequency change. The technique has been simulated in MATLAB/Simulink environment and also tested experimentally. The necessary simulation and experimental results have been shown for the verification of the proposed technique.
Kernel-target alignment based non-linear metric learning

Distance metric learning aims to learn a measure of the pairwise distance between data instances, which is essential for various machine learning algorithms and applications. However, existing linear metric learning methods based on linear transformations fail to capture nonlinear relationships between instances, while most existing kernel-based metric learning algorithms may ignore the correlation between the kernel function and the target learning task during model selection, which results in a suboptimal selection of kernels. To address the aforementioned issues, we propose a method named Kernel Alignment based Metric Learning with Random Fourier Approximation (KAMLRFA). Specifically, on one hand, we utilize the random Fourier features to approximate the shift-invariant kernel function for distance metric learning. On the other hand, we attempt to maximize the degree of agreement between the kernel function and the target learning task. In this way, distance metric learning is performed in a discriminative feature space. Compared with those kernel metric learning algorithms whose kernel functions fall into a suboptimal, KAMLRFA achieves the optimal kernel function for the target task and improves classification accuracy. We develop an efficient solution to solve the proposed optimization problem. Extensive experiments are conducted on several benchmark datasets including NUS-WIDE-LITE, USPS, MNIST, LETTER and 10 UCI datasets to verify the effectiveness of KAMLRFA compared with state-of-the-art methods.
A Novel Framework for Neural Machine Translation of Indian-English Languages

The term 'Machine Translation' (MT) refers to computer systems that performs translation of natural language from one language to another. Machine Translation is especially necessitated in the Indian perspective because more than 50% of the data generated online is in English which is known by only 12%. Many systems has been proposed in the Indian perspective including rule-based, example-based, statistical based and a hybrid of these machine translation techniques. But, recent study has shown that Neural Machine Translation provides better results.In recent times Google and Facebook have developed Neural Machine Translation system. These systems are one-fits-all kind of systems which do not take into consideration the complexities in a language, like Indian languages. So, this paper proposes a broad framework for implementing Neural Machine Translation for Indian-English languages.
Interactive error resilience beyond 2/7

Interactive error correcting codes can protect interactive communication protocols against a constant fraction of adversarial errors, while incurring only a constant multiplicative overhead in the total communication. What is the maximum fraction of errors that such codes can protect against?
Optimizing Variational Representations of Divergences and Accelerating Their Statistical Estimation

Variational representations of divergences and distances between high-dimensional probability distributions offer significant theoretical insights and practical advantages in numerous research areas. Recently, they have gained popularity in machine learning as a tractable and scalable approach for training probabilistic models and for statistically differentiating between data distributions. Their advantages include: 1) They can be estimated from data as statistical averages. 2) Such representations can leverage the ability of neural networks to efficiently approximate optimal solutions in function spaces. However, a systematic and practical approach to improving the tightness of such variational formulas, and accordingly accelerate statistical learning and estimation from data, is currently lacking. Here we develop such a methodology for building new, tighter variational representations of divergences. Our approach relies on improved objective functionals constructed via an auxiliary optimization problem. Furthermore, the calculation of the functional Hessian of objective functionals unveils the local curvature differences around the common optimal variational solution; this quantifies and orders the tightness gains between different variational representations. Finally, numerical simulations utilizing neural network optimization demonstrate that tighter representations can result in significantly faster learning and more accurate estimation of divergences in both synthetic and real datasets (of more than 1000 dimensions), often accelerated by nearly an order of magnitude.
Fundamental Proof Methods in Computer Science: A Computer-Based Approach, by Arkoudas and Musser, The MIT Press, Cambridge, USA, ISBN 978-0-262-03553-8

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Local-Variance-Based Attention For Visual Tracking

The RoIAlign module incorporated into the deep tracking-by-detection framework, which can thus receive the entire image as the input to the convolutional layer, alleviating high computational complexity induced by multiple proposals. Nevertheless, this would also produce an ambiguous feature discriminative boundary between the target and background in the feature map, which makes the following target identification and localization very difficult. To solve this problem, we apply a novel local-variance-based regularization for optimizing the convolutional layer, the local variance calculated from the attention map, i.e., the average pooling of the convolutional feature map. Therefore, the binary classification loss function integrated with local-variance-based regularization item can explicitly make the response of the target and background very distinguishable, specifically strengthening the response of target and weakening that of background. Extensive experiments on large-scale benchmark data sets demonstrate that the proposed algorithm is highly comparable to other state-of-the-art methods.
Oecs: Towards Online Extrinsics Correction For The Surround-View System

A typical surround-view system consists of four fisheye cameras. By performing an offline calibration that determines both the intrinsics and extrinsics of the system, surround-view images can be synthesized at runtime. However, poses of calibrated cameras sometimes may change. In such a case, if cameras' extrinsics are not updated accordingly, observable geometric misalignment will appear in surround-views. Most existing solutions to this problem resort to re-calibration, which is quite cumbersome. Thus, how to correct cameras' extrinsics in an online manner without using re-calibration is still an open issue. In this paper, we attempt to propose a novel solution to this problem and the proposed solution is referred to as “Online Extrinsics Correction for the Surround-view system OECS for short. We first design a Bi-Camera error model, measuring the photometric discrepancy between two corresponding pixels on images captured by two adjacent cameras. Then, by minimizing the system's overall BiCamera error, cameras' extrinsics can be optimized and the optimization is conducted within a sparse direct framework. The efficacy and efficiency of OECS are validated by experiments. Data and source code used in this work are publicly available at https://z619850002.github.io/OECage/.
Height and Uprightness Invariance for 3D Prediction From a Single View

Current state-of-the-art methods that predict 3D from single images ignore the fact that the height of objects and their upright orientation is invariant to the camera pose and intrinsic parameters. To account for this, we propose a system that directly regresses 3D world coordinates for each pixel. First, our system predicts the camera position with respect to the ground plane and its intrinsic parameters. Followed by that, it predicts the 3D position for each pixel along the rays spanned by the camera. The predicted 3D coordinates and normals are invariant to a change in the camera position or its model, and we can directly impose a regression loss on these world coordinates. Our approach yields competitive results for depth and camera pose estimation (while not being explicitly trained to predict any of these) and improves across-dataset generalization performance over existing state-of-the-art methods.
A (2, 2) Visual Cryptography Technique to Share Two Secrets

Visual Cryptography (VC) is used for sharing secret images. The traditional VC technique is capable of sharing only a single secret. Many researchers have developed techniques for sharing multiple secrets. Most of these techniques use either a circular or cylindrical or ring shaped shares. These shares must be rotated by a specific angle to reveal the secrets. Theoretically, these techniques may give good results but practical implementation of these techniques is tedious. These techniques use ring or cylindrical shaped shares which are difficult to handle. The measurement of the angle by which the share has to be rotated is difficult. In this paper, we have proposed a (2, 2) VC technique capable of sharing two secrets using only two shares. The first secret can be revealed by overlapping both the shares, while for revealing the second secret, we have to flip the second share horizontally and then overlap on the first share. The other advantage of the proposed technique is that the two secret images can have different dimensions, such that the rows and columns of one secret image are less than or equal to the other secret image. The proposed technique is applied on different type of binary images and the quality of recovered secret images is evaluated using different performance metrics.
Environmental Impacts Management of the Coachella Valley Music and Arts Festival

The sustainability of music festivals is an urgent issue that must be discussed. Without careful consideration of environmental impacts, the short-term economic gain becomes a long-term fiasco. Coachella, the second-largest music festival in the US and the highest-grossing festival in the world, is possible to harm the environment. It provides pressure by bringing a hundred thousands of people at the same time and place. If the festival damages the environment, it will be costly and taking a long time to preserve the ecosystem. Therefore, it is crucial to assess the management of environmental impacts to recognise how a music festival could increase its environmental sustainability. The analysis ascertained qualitatively by using an approach of energy usage, waste generated, and transport emission produced (Fredline et al., 2005).The result shows that much work has been done by Coachella organisers to reduce the environmental impact of the music festival they organised. First, the energy used in Coachella does not have too much impact on the environment because Coachella has applied sustainable energy framework. Next, in term of waste management, Coachella has been trying to minimise waste generated during the event by organising a massive waste sorting program. It can be said that the program has been quite successful at managing waste. Last, emissions produced from transport have the worst impact on the environment because it directly contributes to climate change. Furthermore, the organisers cannot control the number of vehicles used by attendees because this subject is at the preference of each attendee, and the organiser cannot fully control it.
Notice of Violation of IEEE Publication Principles: Deep Learning Assisted Image Interactive Framework for Brain Image Segmentation

Notice of Violation of IEEE Publication Principles <br><br> "Deep Learning Assisted Image Interactive Framework for Brain Image Segmentation," <br> by Y. Han and Z. Zhang, <br> in IEEE Access, vol. 8, June 2020, pp. 117028-117035 <br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br> This paper contains significant portions of text from the papers cited below that were paraphrased without attribution. <br><br> “DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation," <br> by Guotai Wang, Maria A. Zuluaga, Wenqi Li, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, and Tom Vercauteren <br> in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 7, July 2019, pp. 1559-1572 <br><br> "Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning," <br> by Guotai Wang, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sébastien Ourselin, and Tom Vercauteren <br> in IEEE Transactions on Medical Imaging, vol. 37, no. 7, July 2018, pp. 1562-1573, <br><br> <br/> Exacting medical imaging, surgical planning, and many others are very important to handle brain image segmentation. The Convolutional Neural Networks (CNN) has been developed by the efficient auto segmentation technology. In fact, the clinical outcomes are not appropriately specific and detailed. Nevertheless, the lack of sensitivity to images and lack of generality is reduced in traditionally invisible object classes. In this paper, Deep Learning Assisted Image Interactive Medical Image Segmentation (DL-IIMIS) is proposed to tackle these difficulties by including CNNs in the bounding box and scribble-based pipeline. To adapt a CNN model to one test frame, it is proposed that image fine tuning and geodesic transformations can be either unsupervised or supervised. In this frame, two applications are involved: 2-D multi-organ magnetic resonance (MR) segmentation, with only two types of training and 3-D segmentation within brain tumor center and in entire brain tumors with different MR sequences where only one MR sequence is reported. Compared with other algorithms, the proposed framework can output a better performance in brain image segmentation.
Image Demosaicing Based on Generative Adversarial Network

Digital cameras with a single sensor use a color filter array (CFA) that captures only one color component in each pixel. Therefore, noise and artifacts will be generated when reconstructing the color image, which reduces the resolution of the image. In this paper, we proposed an image demosaicing method based on generative adversarial network (GAN) to obtain high-quality color images. The proposed network does not need any initial interpolation process in the data preparation phase, which can greatly reduce the computational complexity. The generator of the GAN is designed using the U-net to directly generate the demosaicing images. The dense residual network is used for the discriminator to improve the discriminant ability of the network. We compared the proposed method with several interpolation-based algorithms and the DnCNN. Results from the comparative experiments proved that the proposed method can more effectively eliminate the image artifacts and can better recover the color image.
A Comparative Study of Meta Heuristic Model to Assess the Type of Breast Cancer Disease

Breast cancer is the expansion of cancer from the breast tissue and a lump in the breast, a change in breast shape, dimpling of the skin, fluid coming from the nipple or a red scaly patch of skin. This disease is becoming a leading cause of death among women in the whole world; meanwhile, if it is recognized in the early stage and accurate diagnosis of this disease can ensure a long survival of women. Here, the knowledge base has been created using the concept of data mining to recognize the disease based on the early data. In this paper, data mining has been used to create the association rules and statistical, soft computing and evolution algorithms have been used to select optimal classifier based on earlier data. In this context, three types (training on the whole dataset and new tested data have been created, tenfold and 98% training and 2%tested data) of techniques have been used to estimate the disease type (named consequent item) based on the nine attributes (named antecedent item) of the breast cancer data set.
Communication-efficient View-Pooling for Distributed Multi-View Neural Networks

Multi-view object detection or the problem of detecting an object using multiple viewpoints, is an important problem in computer vision with varied applications such as distributed smart cameras and collaborative drone swarms. Multi-view object detection algorithms based on deep neural networks (DNNs) achieve high accuracy by view pooling, or aggregating features corresponding to the different views. However, when these algorithms are realized on networks of edge devices, the communication cost incurred by view pooling often dominates the overall latency and energy consumption.In this paper, we propose techniques for communication-efficient view pooling that can be used to improve the efficiency of distributed multi-view object detection and apply them to state-of-the-art multi-view DNNs. First, we propose significance-aware feature selection, which identifies and communicates only those features from each view that are likely to impact the pooled result (and hence, the final output of the DNN). Second, we propose multi-resolution view pooling, which divides views into dominant and non-dominant views, and down-scales the features from non-dominant views using an additional network layer before communicating them for pooling. The dominant and non-dominant views are pooled separately and the results are jointly used to derive the final classification. We implement and evaluate the proposed pooling schemes using a model test-bed of twelve Raspberry Pi 3b+ devices and show that they achieve 9X - 36X reduction in data communicated and 1.8X reduction in inference latency, with no degradation in accuracy.
L2L: A Highly Accurate Log_2_Lead Quantization of Pre-trained Neural Networks

Deep Neural Networks are one of the machine learning techniques which are increasingly used in a variety of applications. However, the significantly high memory and computation demands of deep neural networks often limit their deployment on embedded systems. Many recent works have considered this problem by proposing different types of data quantization schemes. However, most of these techniques either require post-quantization retraining of deep neural networks or bear a significant loss in output accuracy. In this paper, we propose a novel quantization technique for parameters of pre-trained deep neural networks. Our technique significantly maintains the accuracy of the parameters and does not require retraining of the networks. Compared to the single-precision floating-point numbers-based implementation, our proposed 8-bit quantization technique generates only ~1% and the ~0.4%, loss in top-1 and top-5 accuracies respectively for VGG16 network using ImageNet dataset.
Promises and Perils of Mobile Voting

Voter turnout is often low and unequal, but the opportunity to vote on a mobile device could drastically lower the cost of democratic participation. In 2018, West Virginia became the first U.S. state to utilize mobile voting in a federal election, allowing it for overseas voters from 24 of its counties. I utilize this trial to assess the likely effects of mobile voting on the size and composition of the voting population. Implementing a differences-in-differences design with individual-level administrative data, I estimate that the ability to vote with a mobile device increased turnout by three to five percentage points, and I find little evidence that the effects vary across age, party, or military status. At the same time, new survey data shows that many Americans are understandably wary of online voting.