Reviewerly/exHarmony huggingface dataset:
Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm

Nested sampling is an efficient algorithm for the calculation of the Bayesian evidence and posterior parameter probability distributions. It is based on the step-by-step exploration of the parameter space by Monte Carlo sampling with a series of values sets called live points that evolve towards the region of interest, i.e., where the likelihood function is maximal. In presence of several local likelihood maxima, the algorithm converges with difficulty. Some systematic errors can also be introduced by unexplored parameter volume regions. In order to avoid this, different methods are proposed in the literature for an efficient search of new live points, even in presence of local maxima. Here we present a new solution based on the mean shift cluster recognition method implemented in a random walk search algorithm. The clustering recognition is integrated within the Bayesian analysis program NestedFit. It is tested with the analysis of some difficult cases. Compared to the analysis results without cluster recognition, the computation time is considerably reduced. At the same time, the entire parameter space is efficiently explored, which translates into a smaller uncertainty of the extracted value of the Bayesian evidence.
Quantum informational properties of the Landau–Streater channel

We study the Landau–Streater quantum channel Φ:B(Hd)↦B(Hd), whose Kraus operators are proportional to the irreducible unitary representation of SU(2) generators of dimension d. We establish SU(2) covariance for all d and U(3) covariance for d = 3. Using the theory of angular momentum, we explicitly find the spectrum and the minimal output entropy of Φ. Negative eigenvalues in the spectrum of Φ indicate that the channel cannot be obtained as a result of Hermitian Markovian quantum dynamics. Degradability and antidegradability of the Landau–Streater channel is fully analyzed. We calculate classical and entanglement-assisted capacities of Φ. Quantum capacity of Φ vanishes if d = 2, 3 and is strictly positive if d ⩾ 4. We show that the channel Φ ⊗ Φ does not annihilate entanglement and preserves entanglement of some states with Schmidt rank 2 if d ⩾ 3.
A multi-attention deep neural network model base on embedding and matrix factorization for recommendation

Matrix factorization is a popular method in recommendation system. However, the quality of recommendation algorithm based on matrix decomposition is greatly affected by the sparsity of rating data. This paper presents a multi-attention deep neural network model base on Embedding and matrix factorization for recommendation. By integrating user / item embedding representation and matrix factorization representation, data sparsity and cold start problems can be effectively alleviated. The scalability of model can be solved by using the deep neural network via GPU technology. The experimental results on real data sets show that, with other classical matrix factorization recommendation algorithms, the proposed algorithm can produce higher prediction results and effectively improve the recommendation quality and performance.
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge

We present a new benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge.PA-RADE contains paraphrases that overlap very little at the lexical and syntactic level but are semantically equivalent based on computer science domain knowledge, as well as nonparaphrases that overlap greatly at the lexical and syntactic level but are not semantically equivalent based on this domain knowledge.Experiments show that both state-of-the-art neural models and non-expert human annotators have poor performance on PARADE.For example, BERT after fine-tuning achieves an F1 score of 0.709, which is much lower than its performance on other paraphrase identification datasets.PARADE can serve as a resource for researchers interested in testing models that incorporate domain knowledge.We make our data and code freely available.
Curriculum By Smoothing

Convolutional Neural Networks (CNNs) have shown impressive performance in computer vision tasks such as image classification, detection, and segmentation. Moreover, recent work in Generative Adversarial Networks (GANs) has highlighted the importance of learning by progressively increasing the difficulty of a learning task [26]. When learning a network from scratch, the information propagated within the network during the earlier stages of training can contain distortion artifacts due to noise which can be detrimental to training. In this paper, we propose an elegant curriculum based scheme that smoothes the feature embedding of a CNN using anti-aliasing or low-pass filters. We propose to augment the train-ing of CNNs by controlling the amount of high frequency information propagated within the CNNs as training progresses, by convolving the output of a CNN feature map of each layer with a Gaussian kernel. By decreasing the variance of the Gaussian kernel, we gradually increase the amount of high-frequency information available within the network for inference. As the amount of information in the feature maps increases during training, the network is able to progressively learn better representations of the data. Our proposed augmented training scheme significantly improves the performance of CNNs on various vision tasks without either adding additional trainable parameters or an auxiliary regularization objective. The generality of our method is demonstrated through empirical performance gains in CNN architectures across four different tasks: transfer learning, cross-task transfer learning, and generative models.
Machine Learning Pipelines: From Research to Production

Machine learning (ML) and artificial intelligence (AI) are getting lot of attention these days, and an increasing number of people are interested in becoming data scientists. Many imagine ML/AI as a black box that does its magic and is somehow able to make predictions. Well, the magic is that an ML algorithm is designed in such a way that it is able to find a pattern in data that have the correct answers and use that to predict the next answer. The learning task may vary from predicting the next word in a sentence, a sentiment, or what else would you like to buy based on what you already have in your basket.
Unsupervised Text Generation by Learning from Search

In this work, we present TGLS, a novel framework to unsupervised Text Generation by Learning from Search. We start by applying a strong search algorithm (in particular, simulated annealing) towards a heuristically defined objective that (roughly) estimates the quality of sentences. Then, a conditional generative model learns from the search results, and meanwhile smooth out the noise of search. The alternation between search and learning can be repeated for performance bootstrapping. We demonstrate the effectiveness of TGLS on two real-world natural language generation tasks, paraphrase generation and text formalization. Our model significantly outperforms unsupervised baseline methods in both tasks. Especially, it achieves comparable performance with the state-of-the-art supervised methods in paraphrase generation.
Open Research Data and Open Peer Review: Perceptions of a Medical and Health Sciences Community in Greece

Recently significant initiatives have been launched for the dissemination of Open Access as part of the Open Science movement. Nevertheless, two other major pillars of Open Science such as Open Research Data (ORD) and Open Peer Review (OPR) are still in an early stage of development among the communities of researchers and stakeholders. The present study sought to unveil the perceptions of a medical and health sciences community about these issues. Through the investigation of researchers‘ attitudes, valuable conclusions can be drawn, especially in the field of medicine and health sciences, where an explosive growth of scientific publishing exists. A quantitative survey was conducted based on a structured questionnaire, with 179 valid responses. The participants in the survey agreed with the Open Peer Review principles. However, they ignored basic terms like FAIR (Findable, Accessible, Interoperable, and Reusable) and appeared incentivized to permit the exploitation of their data. Regarding Open Peer Review (OPR), participants expressed their agreement, implying their support for a trustworthy evaluation system. Conclusively, researchers need to receive proper training for both Open Research Data principles and Open Peer Review processes which combined with a reformed evaluation system will enable them to take full advantage of the opportunities that arise from the new scholarly publishing and communication landscape.
Semantic Role Labeling as Syntactic Dependency Parsing

We reduce the task of (span-based) PropBankstyle semantic role labeling (SRL) to syntactic dependency parsing.Our approach is motivated by our empirical analysis that shows three common syntactic patterns account for over 98% of the SRL annotations for both English and Chinese data.Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format.This representation allows us to train statistical dependency parsers to tackle SRL and achieve competitive performance with the current state of the art.Our findings show the promise of syntactic dependency trees in encoding semantic role relations within their syntactic domain of locality, and point to potential further integration of syntactic methods into semantic role labeling in the future.
Gabor Layers Enhance Network Robustness

We revisit the benefits of merging classical vision concepts with deep learning models. In particular, we explore the effect of replacing the first layers of various deep architectures with Gabor layers (i.e. convolutional layers with filters that are based on learnable Gabor parameters) on robustness against adversarial attacks. We observe that architectures with Gabor layers gain a consistent boost in robustness over regular models and maintain high generalizing test performance. We then exploit the analytical expression of Gabor filters to derive a compact expression for a Lipschitz constant of such filters, and harness this theoretical result to develop a regularizer we use during training to further enhance network robustness. We conduct extensive experiments with various architectures (LeNet, AlexNet, VGG16, and WideResNet) on several datasets (MNIST, SVHN, CIFAR10 and CIFAR100) and demonstrate large empirical robustness gains. Furthermore, we experimentally show how our regularizer provides consistent robustness improvements.
Multiparty Session Programming With Global Protocol Combinators

Multiparty Session Types (MPST) is a typing discipline for communication protocols. It ensures the absence of communication errors and deadlocks for well-typed communicating processes. The state-of-the-art implementations of the MPST theory rely on (1) runtime linearity checks to ensure correct usage of communication channels and (2) external domain-specific languages for specifying and verifying multiparty protocols.To overcome these limitations, we propose a library for programming with global combinators - a set of functions for writing and verifying multiparty protocols in OCaml. Local behaviours for all processes in a protocol are inferred at once from a global combinator. We formalise global combinators and prove a sound realisability of global combinators - a well-typed global combinator derives a set of local types, by which typed endpoint programs can ensure type and communication safety. Our approach enables fully-static verification and implementation of the whole protocol, from the protocol specification to the process implementations, to happen in the same language. We compare our implementation to untyped and continuation-passing style implementations, and demonstrate its expressiveness by implementing a plethora of protocols. We show our library can interoperate with existing libraries and services, implementing DNS (Domain Name Service) protocol and the OAuth (Open Authentication) protocol.
Improving deformable image registration with point metric and masking technique for postoperative breast cancer radiotherapy

Background: Deformable image registration (DIR) is increasingly used for target volume definition in radiotherapy.However, this method is challenging for postoperative breast cancer patients due to the large deformations and non-correspondence caused by tumor resection and clip insertion.In this study, an improved B-splines based DIR method was developed to address this issue for higher registration accuracy. Methods:The conventional B-splines based DIR method was improved with the introduction of point metric and masking technique.The point metric minimizes the distance between 2 point sets with known correspondence for regularization of intensity-based B-splines registration.The masking technique reduces the influence of non-corresponding regions in breast computed tomography (CT) images.Two sets of CT images before and after breast surgery were used for image registration.One set was the diagnostic CT image acquired before surgery, and another set was the planning CT image acquired after surgery for breast cancer radiotherapy.A total of 26 sets of CT images from 13 patients were collected retrospectively for the test.The improved DIR method's registration accuracy was evaluated by target registration error (TRE), the Jacobian determinant, and visual assessment.Results: For soft tissue, the difference in the median TRE between the improved DIR method and the conventional DIR method was statistically significant (2.27 vs. 5.88, P<0.05).The Jacobian determinant of the deformation field was positive for all patients.For visual assessment, the improved DIR method with point metric achieved better matching for soft tissue. Conclusions:The improved DIR method's registration accuracy was higher than the conventional DIR method based on the preliminary results.With point metric and masking technique, the influence of large deformations and non-correspondence on registration between pre-and post-operative CT images can be effectively reduced.Therefore, this method provides a feasible way for target volume definition in postoperative breast cancer radiotherapy treatment planning.
Enhancing the Data Mining Tool WEKA

Data Mining is the key to get the best conclusive intuition behind a large chunk of data. WEKA has been the center of the talk because of its wide functionalities which include Data Mining Algorithms like Naϊve Bayes Classifier, Support Vector Machine, Decision Tree, KStar etc. which is helping various industries today to make their work quicker and superior. Even though it provides functionalities there are some disadvantages to the same. This paper aims at overcoming the major disadvantages/drawbacks of the Tool by proposing different approaches. There have been experiments carried out for a few approaches to validate the approach as well.
A Multi-Degradation Aided Method for Unsupervised Remote Sensing Image Super Resolution With Convolution Neural Networks

In remote sensing, it is desirable to improve image resolution by using the image super-resolution (SR) technique. However, there are two challenges: the first one is that high-resolution (HR) images are insufficient or unavailable; another one is that the single degradation model such as bicubic (BIC) cannot super-resolve favorable images in the real world. To address the above two problems, this article presents a multi-degradation, unsupervised SR method based on deep learning. This framework consists of a degrader <inline-formula> <tex-math notation="LaTeX">$ {D}$ </tex-math></inline-formula> to fit the image degradation model and a generator <inline-formula> <tex-math notation="LaTeX">$ {G}$ </tex-math></inline-formula> to generate SR image. By introducing <inline-formula> <tex-math notation="LaTeX">$ {D}$ </tex-math></inline-formula>, calculating the loss function between SR image and HR image as supervised SR methods did can be converted into calculating loss between low resolution (LR) image and image degraded by SR image, thereby realizing unsupervised learning. Experiments on several degradation models show that our method renders the state-of-the-art results compared with existing unsupervised SR methods, and achieves competitive results in contrast with supervised SR methods. Moreover, for real remote sensing images obtained by the Jilin-1 satellite, our method obtained more plausible results visually, which demonstrate the potential in real-world applications.
Visual Social Distance Alert System Using Computer Vision &amp; Deep Learning

One of the principles and best measures to contain the ongoing viral episode is the support of the alleged social distancing (S D).To agree to this limitation, governments are receiving limitations over the base between close to home separation between individuals.Given this real situation, it is critical to enormously gauge the consistence to such physical requirement in our life, so as to make sense of the purposes behind the potential breaks of such separation impediments and comprehend if this suggests a likely danger.To this end, the proposed research work presents the Video S ocial Distancing issue, characterized as the programmed assessment of the between close to home good ways from a picture, and the portrayal of related individuals' conglomerations.Video S ocial Distancing is significant for a non-obtrusive investigation of whether individuals follow the Social Distancing limitation, and to give insights about the degree of security of explicit territories at whatever point this imperative is abused.It has been first viewed that, estimating Video Social Distancing isn't just a mathematical issue, however it additionally infers a more profound comprehension of the social conduct in the scene.The point is to genuinely identify possibly risky circumstances while keeping away from bogus alerts (e.g., a family with youngsters or family members, a senior with their guardians), the entirety of this by following current security strategies.At that point, the proposed research work will discuss about how video social distancing is related with past writing in social signal processing and show a way to investigate new computer vision techniques that can give an answer for such issue.This paper is concluded with future moves that are identified with the viability of video social distancing frameworks, moral ramifications and future application situations.
Augmented Reality in Education

Gaining from books is an unremarkable and latent cycle. The content and images in the books are most certainly not interactive; this prompts the basic barricades to learning looked by students, for example, constraints in comprehending the hypothetical ideas, absence of explanatory, basic reasoning. These detours are overwhelmed by computerized books, however paper-based books are frequently favored over computerized books due to their adaptability and portability. In this paper, we present a remarkable arrangement that utilizes augmented reality to make the learning measure more interactive and fascinating. The application when focused on text or image shows significant 3-dimensional(3D) model or video on the smart phone screen. The application gives some assistance to the students by encouraging them to learn new ideas utilizing graphical guide. Aside from utilization in schooling, it can likewise be utilized in the field of commercial, the travel industry, gaming, medication.
A Long-Short-Term-Memory Based Model for Predicting ATM Replenishment Amount

The advent of Automatic Teller Machines (ATMs) enable self-service, time-independent, easy to use, mechanism through which a financial institution supports large number of services to its users. Cash withdrawal from the ATM is still one of the major transactional loads for these networks. ATM cash replenishment is the process by which ATM machines are filled with the cash so that the users can withdraw it. The rapid adaptation and standardization of these network give rises to many challenging problems that requires intelligent management of these resources. ATM cash replenishment amount prediction is one such problem, predicting the right amount for everyday use such that the minimum amount of cash always available before the next replenishment. In this way there will be no customer dissatisfaction through empty ATM. The paper proposes a machine learning approach to ATM replenishment amount prediction by using a data driven approach for the estimation of right amount for each ATM or some group of ATMs. The data comprises of replenishment of 2241 ATMs for last 22 months from 6 different Banks of Pakistan. The Long Short-Term Memory (LSTM) based model produce Root Mean Squared Error (RMSE) of 132.53, which is quite encouraging for this problem.
An Assessment of GANs for Identity-related Applications

Generative Adversarial Networks (GANs) are now capable of producing synthetic face images of exceptionally high visual quality. In parallel to the development of GANs themselves, efforts have been made to develop metrics to objectively assess the characteristics of the synthetic images, mainly focusing on visual quality and the variety of images. Little work has been done, however, to assess overfitting of GANs and their ability to generate new identities. In this paper we apply a state of the art biometric network to various datasets of synthetic images and perform a thorough assessment of their identity-related characteristics. We conclude that GANs can indeed be used to generate new, imagined identities meaning that applications such as anonymisation of image sets and augmentation of training datasets with distractor images are viable applications. We also assess the ability of GANs to disentangle identity from other image characteristics and propose a novel GAN triplet loss that we show to improve this disentanglement.
Polymorphic Accelerators for Deep Neural Networks

Deep neural networks (DNNs) come with many forms, such as convolutional neural networks, multilayer perceptron, and recurrent neural networks, to meet diverse needs of machine learning applications. However, existing DNN accelerator designs, when used to execute multiple neural networks, suffer from underutilization of processing elements, heavy feature map traffic, and large area overhead. In this article, we propose a novel approach, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Polymorphic Accelerators</i> , to address the flexibility issue fundamentally. We introduce the abstraction of logical accelerators to decouple the fixed mapping with physical resources. Three procedures are proposed that work collaboratively to reconfigure the accelerator for the current network that is being executed and to enable cross-layer data reuse among logical accelerators. Evaluation results show that the proposed approach achieves significant improvement in data reuse, inference latency and performance, e.g., 1.52x and 1.63x increase in throughput compared with state-of-the-art flexible dataflow approach and resource partitioning approach, respectively. This demonstrates the effectiveness and promise of polymorphic accelerator architecture.