Reviewerly/exHarmony huggingface dataset:
On extreme learning machines in sequential and time series prediction: A non-iterative and approximate training algorithm for recurrent neural networks

Recurrent neural networks (RNN) are a type of artificial neural networks (ANN) that have been successfully applied to many problems in artificial intelligence. However, they are expensive to train since the number of learned weights grows exponentially with the number of hidden neurons. Non-iterative training algorithms have been proposed to reduce the training time, mainly on feedforward ANN. In this work, the application of non-iterative randomized training algorithms to various RNN architectures, including Elman RNN, fully connected RNN, and long short-term memory (LSTM), are investigated. The mathematical formulation and theoretical computational complexity of the proposed algorithms are presented. Finally, their performance is empirically compared to other iterative RNN training algorithms on time series prediction and sequential decision-making problems. Non-iteratively-trained RNN architectures showed promising results as significant training speedup of up to 99%, and improved repeatability were achieved compared to backpropagation-trained RNN. Although the decrease in prediction accuracy was found to be statistically significant based on Friedman and ANOVA testing, some applications like real-time embedded systems can tolerate and make use of that.
Jointly Learning Topics in Sentence Embedding for Document Summarization

Summarization systems for various applications, such as opinion mining, online news services, and answering questions, have attracted increasing attention in recent years. These tasks are complicated, and a classic representation using bag-of-words does not adequately meet the comprehensive needs of applications that rely on sentence extraction. In this paper, we focus on representing sentences as continuous vectors as a basis for measuring relevance between user needs and candidate sentences in source documents. Embedding models based on distributed vector representations are often used in the summarization community because, through cosine similarity, they simplify sentence relevance when comparing two sentences or a sentence/query and a document. However, the vector-based embedding models do not typically account for the salience of a sentence, and this is a very necessary part of document summarization. To incorporate sentence salience, we developed a model, called CCTSenEmb, that learns latent discriminative Gaussian topics in the embedding space and extended the new framework by seamlessly incorporating both topic and sentence embedding into one summarization system. To facilitate the semantic coherence between sentences in the framework of prediction-based tasks for sentence embedding, the CCTSenEmb further considers the associations between neighboring sentences. As a result, this novel sentence embedding framework combines sentence representations, word-based content, and topic assignments to predict the representation of the next sentence. A series of experiments with the DUC datasets validate CCTSenEmb's efficacy in document summarization in a query-focused extraction-based setting and an unsupervised ILP-based setting.
Information Flow in Software Testing – An Interview Study With Embedded Software Engineering Practitioners

Background: In order to make informed decisions, software engineering practitioners need information from testing. However, with the trend of increased automation, there is exponential growth and increased distribution of this information. This paper aims at exploring the information flow in software testing in the domain of embedded systems. Method: Data was collected through semi-structured interviews of twelve experienced practitioners with an average work experience of more than fourteen years working at five organizations in the embedded software industry in Sweden. Seventeen hours of audio recordings were transcribed and anonymized into 130 pages of text that was analyzed by means of thematic analysis. Results: The flow of information in software testing can be represented as feedback loops involving stakeholders, software artifacts, test equipment, and test results. The six themes that affect the information flow are how organizations conduct testing and trouble shooting, communication, processes, technology, artifacts, and the organization of the company. Seven main challenges for the flow of information in software testing are comprehending the objectives and details of testing; root cause identification; poor feedback; postponed testing; poor artifacts and traceability; poor tools and test infrastructure; and distances. Finally, five proposed approaches for enhancing the flow are: close collaboration between roles; fast feedback; custom test report automation; test results visualization; and the use of suitable tools and frameworks. Conclusions: The results indicate that there are many opportunities to improve the flow of information in software testing: a first mitigation step is to better understand the challenges and approaches. Future work is needed to realize this in practice, for example, on how to shorten feedback cycles between roles, as well as how to enhance exploration and visualization of test results.
Knowledge graph fact prediction via knowledge-enriched tensor factorization

We present a family of novel methods for embedding knowledge graphs into real-valued tensors. These tensor-based embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background knowledge provided by users or extracted automatically from existing knowledge graphs. In addition to providing more robust methods for knowledge graph embedding, we provide a provably-convergent, linear tensor factorization algorithm. We demonstrate the efficacy of our models for the task of predicting new facts across eight different knowledge graphs, achieving between 5% and 50% relative improvement over existing state-of-the-art knowledge graph embedding techniques. Our empirical evaluation shows that all of the tensor decomposition models perform well when the average degree of an entity in a graph is high, with constraint-based models doing better on graphs with a small number of highly similar relations and regularization-based models dominating for graphs with relations of varying degrees of similarity.
Quantitative Analysis of Immunochromatographic Strip Based on Convolutional Neural Network

Gold immunochromatographic assay (GICA) is a widespread rapid detection method with less cost but high efficiency. It is easy to operate and dispense with professional staff and equipment, which conforms to the trend of point-of-care testing that advocated by modern medicine. With the development and progression of medical detection technology, the qualitative analysis that could be easily performed with the naked eye is not satisfying anymore. In recent years, improving the performance of quantitative analysis of the GICA has become a hot research topic. However, the GICA is susceptible to noise interference due to various factors when used in the qualitative analysis in clinics. The rise of artificial intelligence has provided us with new ideas and directions. As a popular neural network in deep learning, convolutional neural network (CNN) has achieved excellent results in image processing and has been widely applied to many fields, including biomedical engineering. In this paper, CNN is applied to the image segmentation of gold immunochromatographic strip. The grayscale features of the pre-processed images are learned by the established CNN network, and then, the control and test lines are accurately extracted and further quantitative analysis is performed. The results show that the method proposed in this paper has a good segmentation effect on the GICA, and it also provides a new scheme for the quantitative analysis of the GICA.
Deep Hourglass for Brain Tumor Segmentation

The segmentation of a brain tumour in an MRI scan is a challenging task, in this paper we present our results for this problem via the BraTS 2018 challenge, consisting of 210 high grade glioma (HGG) and 75 low grade glioma (LGG) volumes for training. We train and evaluate a convolutional neural network (CNN) encoder-decoder network based on a singular hourglass structure. The hourglass network is able to classify the whole tumour (WT), enhancing (ET) tumour and core tumour (TC) in one pass. We apply a small amount of preprocessing to the data before feeding it to the network but no post processing. We apply our method to two different unseen sets of volumes containing 66 and 191 volumes. We achieve an overall Dice coefficient of 92% on the training set. On the first unseen set our network achieves Dice coefficients of 0.66, 0.82 and 0.72 for ET, WT and TC. On the second unseen set our network achieves Dice coefficients of 0.62, 0.79 and 0.65 on ET, WT and TC.
3D Hand Shape and Pose from Images in the Wild

We present in this work the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild. Our network consists of the concatenation of a deep convolutional encoder, and a fixed model-based decoder. Given an input image, and optionally 2D joint detections obtained from an independent CNN, the encoder predicts a set of hand and view parameters. The decoder has two components: A pre-computed articulated mesh deformation hand model that generates a 3D mesh from the hand parameters, and a re-projection module controlled by the view parameters that projects the generated hand into the image domain. We show that using the shape and pose prior knowledge encoded in the hand model within a deep learning framework yields state-of-the-art performance in 3D pose prediction from images on standard benchmarks, and produces geometrically valid and plausible 3D reconstructions. Additionally, we show that training with weak supervision in the form of 2D joint annotations on datasets of images in the wild, in conjunction with full supervision in the form of 3D joint annotations on limited available datasets allows for good generalization to 3D shape and pose predictions on images in the wild.
A Perceptron Algorithm for Forest Fire Prediction Based on Wireless Sensor Networks

Forest fire prediction constitutes a significant component of forest management. Timely and accurate forest fire prediction will greatly reduce property and natural losses. A quick method to estimate forest fire hazard levels... | Find, read and cite all the research you need on Tech Science Press
Dynamic Bayesian Network Modeling of Game-Based Diagnostic Assessments

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. Drawing from and advancing methods in dynamic Bayesian networks, cognitive diagnostic modeling, and analysis of process data, a Bayesian approach to model construction, calibration, and use in facilitating inferences about students on the fly is described, and implemented in the context of an educational video game.
On the classification of software change messages using multi-label active learning

In this paper, we present a multi-label active learning-based approach to handle the problem of classification of commit messages. The approach will help developers track software changes, e.g., adding or updating existing features, fixing user-reported errors, improving software performance, etc. We first constructed an unlabeled dataset of commit messages where each commit message is represented as a vector of feature values. The set of adopted features were automatically generated from the original commit messages using Term Frequency-Inverse Document Frequency (TF-IDF) technique. Because many commit messages can be assigned more than one commit class at the same time and in order to reduce the effort needed to assign labels to each instance in a large set of commit messages, we adopted an Active Learning multi-label approach. Experimentations have shown that we could train an accurate multi-label classifier model, in our case, a binary relevance with logistic regression as a base classifier, by actively querying an oracle for labels during the training process and with a reasonable number of labeled instances.
When Users Assist the Voice Assistants

We conducted an in situ study of six households in domestic and driving situations in order to better understand how voice assistants (VA) are used and evaluate the efficiency of vocal interactions in natural contexts. The filmed observations and interviews revealed activities of supervision, verification, diagnosis and problem-solving. These activities were not only costly in time, but they also interrupted the flow in the inhabitants' other activities. Although the VAs were expected to facilitate the accomplishment of a second, simultaneous task, they in fact were a hindrance. Such failures can cause abandonment, but the results nevertheless revealed a paradox of use: the inhabitants forgave and accepted these errors, while continuing to appropriate the vocal system.
The psychology of cryptocurrency prices

This letter analyzes the dynamic of cryptocurrency prices through the lens of behavioral economics. Cryptocurrency market participants seem to behave irrationally. We provide evidence of the presence of significant small price bias for the cryptocurrency market, consistent with the hypothesis that investors react to the news differently according to the price-level. We find that low-priced cryptocurrencies are much more volatile than their high-priced counterparts.
Paraphrase Generation for Semi-Supervised Learning in

Semi-supervised learning is an efficient way to improve performance for natural language processing systems. In this work, we propose Para-SSL, a scheme to generate candidate utterances using paraphrasing and methods from semi-supervised learning. In order to perform paraphrase generation in the context of a dialog system, we automatically extract paraphrase pairs to create a paraphrase corpus. Using this data, we build a paraphrase generation system and perform one-to-many generation, followed by a validation step to select only the utterances with good quality. The paraphrase-based semi-supervised learning is applied to five functionalities in a natural language understanding system. Our proposed method for semi-supervised learning using paraphrase generation does not require user utterances and can be applied prior to releasing a new functionality to a system. Experiments show that we can achieve up to 19% of relative slot error reduction without an access to user utterances, and up to 35% when leveraging live traffic utterances.
Using Level-1 Homomorphic Encryption to Improve Threshold DSA Signatures for Bitcoin Wallet Security

Recently Gennaro et al. (ACNS ’16) presented a threshold-optimal signature algorithm for DSA. Threshold-optimality means that if security is set so that it is required to have $$t+1$$ servers to cooperate to sign, then it is sufficient to have $$n=t+1$$ honest servers in the network. Obviously threshold optimality compromises robustness since if $$n=t+1$$ , a single corrupted player can prevent the group from signing. Still, in their protocol, up to t corrupted players cannot produce valid signatures. Their protocol requires six rounds which is already an improvement over the eight rounds of the classic threshold DSA of Gennaro et al. (Eurocrypt ’99) (which is not threshold optimal since $$n \ge 3t+1$$ if robust and $$n \ge 2t+1$$ if not). We present a new and improved threshold-optimal DSA signature scheme, which cuts the round complexity to four rounds. Our protocol is based on the observation that given an encryption of the secret key, the encryption of a DSA signature can be computed in only four rounds if using a level-1 Fully Homomorphic Encryption scheme (i.e. a scheme that supports at least one multiplication), and we instantiate it with the very efficient level-1 FHE scheme of Catalano and Fiore (CCS ’15). As noted in Gennaro et al. (ACNS ’16), the schemes have very compelling application in securing Bitcoin wallets from thefts happening due to DSA secret key exposure. Given that network latency can be a major bottleneck in an interactive protocol, a scheme with reduced round complexity is highly desirable. We implement and benchmark our scheme and find it to be very efficient in practice.
Sampling Bias in Deep Active Classification: An Empirical Study

Ameya Prabhu, Charles Dognin, Maneesh Singh. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Stability of Graph Scattering Transforms

Scattering transforms are non-trainable deep convolutional architectures that exploit the multi-scale resolution of a wavelet filter bank to obtain an appropriate representation of data. More importantly, they are proven invariant to translations, and stable to perturbations that are close to translations. This stability property dons the scattering transform with a robustness to small changes in the metric domain of the data. When considering network data, regular convolutions do not hold since the data domain presents an irregular structure given by the network topology. In this work, we extend scattering transforms to network data by using multi-resolution graph wavelets, whose computation can be obtained by means of graph convolutions. Furthermore, we prove that the resulting graph scattering transforms are stable to metric perturbations of the underlying network. This renders graph scattering transforms robust to changes on the network topology, making it particularly useful for cases of transfer learning, topology estimation or time-varying graphs.
Hierarchical Optimal Transport for Document Representation

The ability to measure similarity between documents enables intelligent summarization and analysis of large corpora. Past distances between documents suffer from either an inability to incorporate semantic similarities between words or from scalability issues. As an alternative, we introduce hierarchical optimal transport as a meta-distance between documents, where documents are modeled as distributions over topics, which themselves are modeled as distributions over words. We then solve an optimal transport problem on the smaller topic space to compute a similarity score. We give conditions on the topics under which this construction defines a distance, and we relate it to the word mover's distance. We evaluate our technique for k-NN classification and show better interpretability and scalability with comparable performance to current methods at a fraction of the cost.
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection

Recently, Neural Architecture Search has achieved great success in large-scale image classification. In contrast, there have been limited works focusing on architecture search for object detection, mainly because the costly ImageNet pretraining is always required for detectors. Training from scratch, as a substitute, demands more epochs to converge and brings no computation saving. To overcome this obstacle, we introduce a practical neural architecture transformation search(NATS) algorithm for object detection in this paper. Instead of searching and constructing an entire network, NATS explores the architecture space on the base of existing network and reusing its weights. We propose a novel neural architecture search strategy in channel-level instead of path-level and devise a search space specially targeting at object detection. With the combination of these two designs, an architecture transformation scheme could be discovered to adapt a network designed for image classification to task of object detection. Since our method is gradient-based and only searches for a transformation scheme, the weights of models pretrained in ImageNet could be utilized in both searching and retraining stage, which makes the whole process very efficient. The transformed network requires no extra parameters and FLOPs, and is friendly to hardware optimization, which is practical to use in real-time application. In experiments, we demonstrate the effectiveness of NATS on networks like {\em ResNet} and {\em ResNeXt}. Our transformed networks, combined with various detection frameworks, achieve significant improvements on the COCO dataset while keeping fast.
The Option Keyboard: Combining Skills in Reinforcement Learning

The ability to combine known skills to create new ones may be crucial in the solution of complex reinforcement learning problems that unfold over extended periods. We argue that a robust way of combining skills is to define and manipulate them in the space of pseudo-rewards (or cumulants). Based on this premise, we propose a framework for combining skills using the formalism of options. We show that every deterministic option can be unambiguously represented as a cumulant defined in an extended domain. Building on this insight and on previous results on transfer learning, we show how to approximate options whose cumulants are linear combinations of the cumulants of known options. This means that, once we have learned options associated with a set of cumulants, we can instantaneously synthesise options induced by any linear combination of them, without any learning involved. We describe how this framework provides a hierarchical interface to the environment whose abstract actions correspond to combinations of basic skills. We demonstrate the practical benefits of our approach in a resource management problem and a navigation task involving a quadrupedal simulated robot.
Multi-View Image Fusion

We present an end-to-end learned system for fusing multiple misaligned photographs of the same scene into a chosen target view. We demonstrate three use cases: 1) color transfer for inferring color for a monochrome view, 2) HDR fusion for merging misaligned bracketed exposures, and 3) detail transfer for reprojecting a high definition image to the point of view of an affordable VR180-camera. While the system can be trained end-to-end, it consists of three distinct steps: feature extraction, image warping and fusion. We present a novel cascaded feature extraction method that enables us to synergetically learn optical flow at different resolution levels. We show that this significantly improves the network's ability to learn large disparities. Finally, we demonstrate that our alignment architecture outperforms a state-of-the art optical flow network on the image warping task when both systems are trained in an identical manner.