Reviewerly/exHarmony huggingface dataset:
Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

Objective: Breast cancer is the most common invasive severity which leads to the second primary cause of death among women. The objective of this paper is to propose a computer-aided approach for the breast cancer classification from the digital mammograms. Methods: Designing an effective classification approach will assist in resolving the difficulties in analyzing digital mammograms. The proposed work utilized the Mammogram Image Analysis Society (MIAS) database for the analysis of breast cancer. Five distinct wavelet families are used for extraction of features from the mammograms of MIAS database. These extracted features are statistical in nature and served as input to the Linear Discriminant Analysis (LDA) and Cuckoo-Search Algorithm (CSA) classifiers. Results: Error rate, Sensitivity, Specificity and Accuracy are the performance measures used and the obtained results clearly state that the CSA used as a classifier affords an accuracy of 97.5% while compared with the LDA classifier. Conclusion: The results of comparative performance analysis show that the CSA classifier outperforms the performance of LDA in terms of breast cancer classification.
A Comparison of Visualizations for Identifying Correlation over Space and Time

Observing the relationship between two or more variables over space and time is essential in many domains. For instance, looking, for different countries, at the evolution of both the life expectancy at birth and the fertility rate will give an overview of their demographics. The choice of visual representation for such multivariate data is key to enabling analysts to extract patterns and trends. Prior work has compared geo-temporal visualization techniques for a single thematic variable that evolves over space and time, or for two variables at a specific point in time. But how effective visualization techniques are at communicating correlation between two variables that evolve over space and time remains to be investigated. We report on a study comparing three techniques that are representative of different strategies to visualize geo-temporal multivariate data: either juxtaposing all locations for a given time step, or juxtaposing all time steps for a given location; and encoding thematic attributes either using symbols overlaid on top of map features, or using visual channels of the map features themselves. Participants performed a series of tasks that required them to identify if two variables were correlated over time and if there was a pattern in their evolution. Tasks varied in granularity for both dimensions: time (all time steps, a subrange of steps, one step only) and space (all locations, locations in a subregion, one location only). Our results show that a visualization's effectiveness depends strongly on the task to be carried out. Based on these findings we present a set of design guidelines about geo-temporal visualization techniques for communicating correlation.
Binary Image Steganalysis Based on Histogram of Structuring Elements

Utilizing statistical models of binary images is a common and effective means to steganalyze binary images, and the design of the statistical model is essential to the performance of steganalysis. In this paper, we propose a new model based on a histogram of pixel structuring elements (SEs), which is a suitable representation of a binary image for the task of steganalysis. The texture property and the dependency among pixels are considered inside the SEs. The SEs with different patterns will be evaluated comprehensively according to a statistical criterion, and some of them will be selected to construct the feature set for training the steganalyzer. The distributions of these selected SEs, which contain many highly flippable pixels, will be emphasized by the criterion, and they can reflect the difference between cover images and stego-images. Finally, a series of experiments are conducted on two datasets, and the results show that the proposed scheme significantly outperforms state-of-the-art schemes.
Predicting Discourse Structure using Distant Supervision from Sentiment

Discourse parsing could not yet take full advantage of the neural NLP revolution, mostly due to the lack of annotated datasets.We propose a novel approach that uses distant supervision on an auxiliary task (sentiment classification), to generate abundant data for RSTstyle discourse structure prediction.Our approach combines a neural variant of multipleinstance learning, using document-level supervision, with an optimal CKY-style tree generation algorithm.In a series of experiments, we train a discourse parser (for only structure prediction) on our automatically generated dataset and compare it with parsers trained on human-annotated corpora (news domain RST-DT and Instructional domain).Results indicate that while our parser does not yet match the performance of a parser trained and tested on the same dataset (intra-domain), it does perform remarkably well on the much more difficult and arguably more useful task of inter-domain discourse structure prediction, where the parser is trained on one domain and tested/applied on another one.
Learning Invariant Representations of Social Media Users

The evolution of social media users' behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking.As a result, naïve approaches may fail to generalize to new users or even to future observations of previously known users.In this paper, we propose a novel procedure to learn a mapping from short episodes of user activity on social media to a vector space in which the distance between points captures the similarity of the corresponding users' invariant features.We fit the model by optimizing a surrogate metric learning objective over a large corpus of unlabeled social media content.Once learned, the mapping may be applied to users not seen at training time and enables efficient comparisons of users in the resulting vector space.We present a comprehensive evaluation to validate the benefits of the proposed approach using data from Reddit, Twitter, and Wikipedia.
Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents

Images and text co-occur constantly on the web, but explicit links between images and sentences (or other intra-document textual units) are often not present.We present algorithms that discover image-sentence relationships without relying on explicit multimodal annotation in training.We experiment on seven datasets of varying difficulty, ranging from documents consisting of groups of images captioned post hoc by crowdworkers to naturally-occurring user-generated multimodal documents.We find that a structured training objective based on identifying whether collections of images and sentences co-occur in documents can suffice to predict links between specific sentences and specific images within the same document at test time.
A Deep Factorization of Style and Structure in Fonts

Example fonts from the Capitals64 dataset.The task of font reconstruction involves generating missing glyphs from partially observed novel fonts.
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks

Semantic role labeling (SRL) involves extracting propositions (i.e.predicates and their typed arguments) from natural language sentences.State-of-the-art SRL models rely on powerful encoders (e.g., LSTMs) and do not model non-local interaction between arguments.We propose a new approach to modeling these interactions while maintaining efficient inference.Specifically, we use Capsule Networks (Sabour et al., 2017): each proposition is encoded as a tuple of capsules, one capsule per argument type (i.e.role).These tuples serve as embeddings of entire propositions.In every network layer, the capsules interact with each other and with representations of words in the sentence.Each iteration results in updated proposition embeddings and updated predictions about the SRL structure.Our model substantially outperforms the nonrefinement baseline model on all 7 CoNLL-2019 languages and achieves state-of-the-art results on 5 languages (including English) for dependency SRL.We analyze the types of mistakes corrected by the refinement procedure.For example, each role is typically (but not always) filled with at most one argument.Whereas enforcing this approximate constraint is not useful with the modern SRL system, iterative procedure corrects the mistakes by capturing this intuition in a flexible and contextsensitive way. 1
A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction

Spelling correction has attracted a lot of attention in the NLP community. However, models have been usually evaluated on artificiallycreated or proprietary corpora. A publiclyavailable corpus of authentic misspellings, annotated in context, is still lacking. To address this, we present and release an annotated data set of 6,121 spelling errors in context, based on a corpus of essays written by English language learners. We also develop a minimallysupervised context-aware approach to spelling correction. It achieves strong results on our data: 88.12% accuracy. This approach can also train with a minimal amount of annotated data (performance reduced by less than 1%). Furthermore, this approach allows easy portability to new domains. We evaluate our model on data from a medical domain and demonstrate that it rivals the performance of a model trained and tuned on in-domain data.
A Novel Robust Lane Change Trajectory Planning Method for Autonomous Vehicle

A novel trajectory planning method is proposed in this paper for lane change of autonomous vehicle. Since it is difficult to accurately capture the trajectory of other vehicles, which means the trajectory for autonomous vehicle couldn't always easy to generate quickly. Moreover, the motion planning, as a kind of high-dimensional optimization problem with multiple nonlinear constraints, requires lots of resources to find a right solution. Therefore, we present a trajectory monitoring strategy to keep robust in lane change scenario, which generates the lane change and monitoring trajectory at the same time. If the former does not produce a safe trajectory or is time out, the monitoring trajectory will be taken as the result output. To meet the constraints of vehicle's motion and real-time requirements, B-spline-based method will be employed to plan a continuous curvature path. And RRT-based method works as a supplement for keeping algorithm completeness. Then the monitory trajectory mainly obeys collision-free requirements, which computes deceleration that keeps vehicle stability. The results illustrate that both B-spline-based method and RRT-based could generate curvature continuous and meet the limitation for motion, however, both have the possibility of timeout. Especially, there are challenge to the success rate as environment becomes more complex.
Remote Blind State Preparation with Weak Coherent Pulses in the Field

Quantum computing has seen tremendous progress in past years. Due to implementation complexity and cost, the future path of quantum computation is strongly believed to delegate computational tasks to powerful quantum servers on the cloud. Universal blind quantum computing (UBQC) provides the protocol for the secure delegation of arbitrary quantum computations, and it has received significant attention. However, a great challenge in UBQC is how to transmit a quantum state over a long distance securely and reliably. Here, we solve this challenge by proposing a resource-efficient remote blind qubit preparation (RBQP) protocol, with weak coherent pulses for the client to produce, using a compact and low-cost laser. We experimentally verify a key step of RBQP-quantum nondemolition measurement-in the field test over 100 km of fiber. Our experiment uses a quantum teleportation setup in the telecom wavelength and generates 1000 secure qubits with an average fidelity of (86.9±1.5)%, which exceeds the quantum no-cloning fidelity of equatorial qubit states. The results prove the feasibility of UBQC over long distances, and thus serves as a key milestone towards secure cloud quantum computing.
Knowledge map construction for question and answer archives

The rapid increase in the number of community-based question-and-answer services has built up large archives of questions and answers. These archives deliver a plethora of valuable knowledge to users who primarily browse to locate information. Question–answer pairs, which not only include knowledge content but also indicate knowledge needs, are a new form of information presentation. To facilitate browsing question and answer archives and alleviate information overload during the browsing process, this paper constructs a knowledge map for question and answer archives by exploiting question–answer pair characteristics to determine a more precise location of question–answer pairs. First, the questions and answers are modeled and then the knowledge map structure is completed. Questions and answers are the two main dimensions of this knowledge map, and their intersection comprises a cluster of corresponding question–answer pairs. The knowledge map can be widely and deeply extended to provide a comprehensive representation of the question and answer archive. When labeling the knowledge map to aid in interpretation and understanding, the key words are identified, and typical question–answer pair extraction methods are proposed. Finally, we conduct extensive experiments on a real dataset, and the results show that the proposed approach is feasible and performs well.
Dual memory network model for sentiment analysis of review text

In sentiment analysis of product reviews, both user and product information are proven to be useful. Current works handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product information for reviews classification using separate memory networks. Then, the two representations are used jointly for sentiment analysis. The use of separate models aims to capture user profiles and product information more effectively. Comparing with state-of-the-art unified prediction models, evaluations on three benchmark datasets (IMDB, Yelp13, and Yelp14) show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values.
Real-Time Foot-Ground Contact Detection for Inertial Motion Capture Based on an Adaptive Weighted Naive Bayes Model

Detecting foot-ground contact is important for inertial motion capture systems because it can provide kinematic constraints to improve human motion capture accuracy.The popular contact detection methods based on zero-velocity detection are only applicable to applications with regular movement patterns, such as pedestrian dead reckoning and gait analysis.As for arbitrary locomotion in motion capture, reliable foot-ground contact detection for universal inertial motion capture is challenging in the presence of motion kinematics and dynamics estimation errors.This paper proposes a novel foot-ground contact detection method for inertial motion capture by the fusion of inaccurate estimations of body kinematics and dynamics with a Naive Bayes probabilistic contact model.Based on physical analysis of contact, a series of kinematic and dynamic motion features calculated from motion capture are considered as observations of contact status classification.The proposed framework consists of offline model training based on a constructed labeled multi-person foot contact dataset, real-time contact detection, and an online model adaptation mechanism for enhancing detection performance of different users and setups.Quantitative evaluations of the proposed contact detection method are presented, and the resulting average foot-ground contact detection accuracy among various locomotion is 95.8%.
From Reports to Bug-Fix Commits

Bugs appear in almost any software development. Solving all or at least a large part of them requires a great deal of time, effort, and budget. Software projects typically use issue tracking systems as a way to report and monitor bug-fixing tasks. In recent years, several researchers have been conducting bug tracking analysis to better understand the problem and thus provide means to reduce costs and improve the efficiency of the bug-fixing task. In this paper, we introduce a new dataset composed of more than 70,000 bug-fix reports from 10 years of bug-fixing activity of 55 projects from the Apache Software Foundation, distributed in 9 categories. We have mined this information from Jira issue track system concerning two different perspectives of reports with closed/resolved status: static (the latest version of reports) and dynamic (the changes that have occurred in reports over time). We also extract information from the commits (if they exist) that fix such bugs from their respective version-control system (Git). We also provide a change analysis that occurs in the reports as a way of illustrating and characterizing the proposed dataset. Once the data extraction process is an error-prone nontrivial task, we believe such initiatives like this could be useful to support researchers in further more detailed investigations.
Efficient Privacy-Preserving Access Control of Mobile Multimedia Data in Cloud Computing

With the nature of allowing the encryptor to define the access policy before encrypting a message, Ciphertext-policy attribute-based encryption (CP-ABE) has been widely adopted as a primitive to design cloud-assisted mobile multimedia data sharing system. However, in most previous works, the access policy sent along with ciphertext remains in the palintext form, which would expose the user's privacy to anyone who can get the ciphertext even if he is not authorized to decrypt. In addition, the resource-constrained mobile devices can not carry out frequent encryption and decryption task caused by CP-ABE. Such drawbacks may reduce the enthusiasm of consumers to share the multimedia data via their mobile devices. In this paper, we proposed PPCMM, a privacy-preserving cloud-assisted mobile multimedia data sharing scheme, where each attribute is described by an attribute name and an attribute value. The attribute values are embedded in the ciphertext and only the attribute names are revealed in the access policy. The encryption is divided to two phase: online and offline. In the offline stage, the data owner can prepare the intermediate ciphertext components. Once receiving the encryption requirement of a specific access policy and the multimedia data, the data owner can quickly form the final legal ciphertext in the online stage. By employing the decryption outsourcing technique, most computation overhead of matching test and decryption is offload to the cloud server. The security proof showed that PPCMM is adaptively secure in the standard model. The performance analysis indicated that PPCMM greatly reduces the computation cost in both online encryption and user decryption.
Prediction of Heart Disease by Clustering and Classification Techniques

Every year 19 million people approximately die from heart disease worldwide.A heart patient shows several symptoms and it is very tough to attribute them to the heart disease in so many steps of disease progression.Data mining, as an answer to extract a hidden pattern from the clinical dataset, are applied to a database in this analysis.All available algorithms in classification technique are compared to each other to achieve the highest accuracy.To further increase the correctness of the solution, the dataset is preprocessed by different unsupervised and supervised algorithms.The two important tasks which are needed for the development of classifier come under data mining and they are clustering and classification.In K-means clustering the initial point selection effects on the results of the algorithm, both in the number of clusters found and their centroids.Methods to enhance the k-means clustering algorithm are discussed.With the help of these methods efficiency, accuracy and performance are improved.So, to improve the performance of clusters the Normalization which is a preprocessing stage is used to enhance the Euclidean distance by calculating more nearer centers, which result in a reduced number of iterations which will reduce the computational time as compared to k-means clustering.Finally, the classifiers are developed with Logistic regression by using the data extracted by K-Means Clustering.The techniques adopted in the design of classifier perform relatively well in terms of classification results better compared to clustering techniques.
Modeling Quantification and Scope in Abstract Meaning Representations

In this paper, we propose an extension to Abstract Meaning Representations (AMRs) to encode scope information of quantifiers and negation, in a way that overcomes the semantic gaps of the schema while maintaining its cognitive simplicity.Specifically, we address three phenomena not previously part of the AMR specification: quantification, negation (generally), and modality.The resulting representation, which we call "Uniform Meaning Representation" (UMR), adopts the predicative core of AMR and embeds it under a "scope" graph when appropriate.UMR representations differ from other treatments of quantification and modal scope phenomena in two ways: (a) they are more transparent; and (b) they specify default scope when possible.
Multi-Accent Adaptation Based on Gate Mechanism

When only a limited amount of accented speech data is available, to promote multi-accent speech recognition performance, the conventional approach is accent-specific adaptation, which adapts the baseline model to multiple target accents independently. To simplify the adaptation procedure, we explore adapting the baseline model to multiple target accents simultaneously with multi-accent mixed data. Thus, we propose using accent-specific top layer with gate mechanism (AST-G) to realize multi-accent adaptation. Compared with the baseline model and accent-specific adaptation, AST-G achieves 9.8% and 1.9% average relative WER reduction respectively. However, in real-world applications, we can't obtain the accent category label for inference in advance. Therefore, we apply using an accent classifier to predict the accent label. To jointly train the acoustic model and the accent classifier, we propose the multi-task learning with gate mechanism (MTL-G). As the accent label prediction could be inaccurate, it performs worse than the accent-specific adaptation. Yet, in comparison with the baseline model, MTL-G achieves 5.1% average relative WER reduction.
Open IoT Architecture for Continuous Patient Monitoring in Emergency Wards

Due to multiple reasons, emergency wards can become overloaded with patients, some of which can be in critical health conditions. To improve the emergency service and avoid deaths and serious adverse events that could be potentially prevented, it is mandatory to do a continuous monitoring of patients physiological parameters. This is a good fit for Internet of Things (IoT) technology, but the scenario imposes hard constraints on autonomy, connectivity, interoperability, and delay. In this paper, we propose a full Internet-based architecture using open protocols from the wearable sensors up to the monitoring system. Particularly, we use low-cost and low-power WiFi-enabled wearable physiological sensors that connect directly to the Internet infrastructure and run open communication protocols, namely, oneM2M. At the upper end, our architecture relies on openEHR for data semantics, storage, and monitoring. Overall, we show the feasibility of our open IoT architecture exhibiting 20–50 ms end-to-end latency and 30–50 h sensor autonomy at a fraction of the cost of current non-interoperable vertical solutions.