Reviewerly/exHarmony huggingface dataset:
Classification of multiclass imbalanced data using cost-sensitive decision tree C5.0

The multiclass imbalanced data problems in data mining were an interesting to study currently. The problems had an influence on the classification process in machine learning processes. Some cases showed that minority class in the dataset had an important information value compared to the majority class. When minority class was misclassification, it would affect the accuracy value and classifier performance. In this research, cost sensitive decision tree C5.0 was used to solve multiclass imbalanced data problems. The first stage, making the decision tree model uses the C5.0 algorithm then the cost sensitive learning uses the metacost method to obtain the minimum cost model. The results of testing the C5.0 algorithm had better performance than C4.5 and ID3 algorithms. The percentage of algorithm performance from C5.0, C4.5 and ID3 were 40.91%, 40, 24% and 19.23%.
Boundary-aware Semi-supervised Deep Learning for Breast Ultrasound Computer-Aided Diagnosis

Breast ultrasound (US) is an effective imaging modality for breast cancer diagnosis. US computer-aided diagnosis (CAD) systems have been developed for decades and have employed either conventional handcrafted features or modern automatic deep-learned features, the former relying on clinical experience and the latter demanding large datasets. In this paper, we developed a novel BASDL method that integrates clinical-approved breast lesion boundary characteristics (features) into a semi-supervised deep learning (SDL) to achieve accurate diagnosis with a small training dataset. Original breast US images are converted to boundary-oriented feature maps (BFMs) using a distance-transformation coupled with a Gaussian filter. Then, the converted BFMs are used as the input of SDL network, which is characterized as lesion classification guided unsupervised image reconstruction based on stacked convolutional auto-encode (SCAE). We compared the performance of BASDL with conventional SCAE method and SDL method that used the original images as inputs, as well as SCAE method that used BFMs as inputs. Experimental results on two breast US datasets show that BASDL ranked the best among the four networks, with classification accuracy around 92.00±2.38%, which indicated that BASDL could be promising for effective breast US lesion CAD using small datasets.
Influence of Developer Factors on Code Quality: A Data Study

Automatic source-code inspection tools help to assess, monitor and improve code quality. Since these tools only examine the software project's codebase, they overlook other possible factors that may impact code quality and the assessment of the technical debt (TD). Our initial hypothesis is that human factors associated with the software developers, like coding expertise, communication skills, and experience in the project have some measurable impact on the code quality. In this exploratory study, we test this hypothesis on two large open source repositories, using TD as a code quality metric and the data that may be inferred from the version control systems. The preliminary results of our statistical analysis suggest that the level of participation of the developers and their experience in the project have a positive correlation with the amount of TD that they introduce. On the contrary, communication skills have barely any impact on TD.
Sharing Pre-trained BERT Decoder for a Hybrid Summarization

Sentence selection and summary generation are two main steps to generate informative and readable summaries. However, most previous works treat them as two separated subtasks. In this paper, we propose a novel extractive-and-abstractive hybrid framework for single document summarization task by jointly learning to select sentence and rewrite summary. It first selects sentences by an extractive decoder and then generate summary according to each selected sentence by an abstractive decoder. Moreover, we apply the BERT pre-trained model as document encoder, sharing the context representations to both decoders. Experiments on the CNN/DailyMail dataset show that the proposed framework outperforms both state-of-the-art extractive and abstractive models.
Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Modified Ant Colony Optimization

In this paper, a unique combination among probabilistic roadmap, modified ant colony optimization, and third order B-spline curve has been proposed to solve path planning problems in complex and very complex environments.This proposed approach can be divided into three stages.First stage involves constructing a random roadmap depending on the environment complexity using probabilistic roadmap algorithm.Roadmap can be constructed by distributing N nodes randomly in complex and very complex static environments then pairing these nodes together according to some criteria or conditions.The constructed roadmap contains a huge number of possible random paths that may lead to connecting the start and the goal points together.Second stage includes finding path within the pre-constructed roadmap.Modified ant colony optimization has been proposed to find or to search the best path between start and goal points, where in addition to the proposed combination, ACO has been modified to increase its ability to find shorter path.Finally, the third stage uses B-spline curve to smooth and reduce the total length of the found path in the previous stage.The results of the proposed approach ensure the feasible path between start and goal points in complex and very complex environments.Also, the path is guaranteed to be short, smooth, continuous and safe.
Looking Outside the Box: Reflecting on Gaze Interaction in Gameplay

Gaze interaction in games moved from being a tool for accessibility to be at the core of mass-market game franchises, offering enhanced controller performance and greater immersion. We propose to explore three different popular gaze-based interaction mechanics to create novel opportunities in the game design space. We developed Twileyed, a collection of three games that challenge the "common" use of gaze as a pointer to navigate; select; and aim; to pose a challenging new way to play with the eyes. We used the games as data to reflect on the gaze design space. We asked users to play the games to validate them, and we observed their experience and strategies. Based on the observations, we discussed through 5 themes, the dimensions of gaze interactions and the potential outcomes to create engaging and playful gaze-enabled games. We contribute a position in gaze gameplay design, but also a conversation starter to engage the EyePlay research community.
Relation Extraction Using Supervision from Topic Knowledge of Relation Labels

Explicitly exploring the semantics of a relation is significant for high-accuracy relation extraction, which is, however, not fully studied in previous work. In this paper, we mine the topic knowledge of a relation to explicitly represent the semantics of this relation, and model relation extraction as a matching problem. That is, the matching score between a sentence and a candidate relation is predicted for an entity pair. To this end, we propose a deep matching network to precisely model the semantic similarity between a sentence-relation pair. Besides, the topic knowledge also allows us to derive the importance information of samples as well as two knowledge-guided negative sampling strategies in the training process. We conduct extensive experiments to evaluate the proposed framework and observe improvements in AUC of 11.5% and max F1 of 5.4% over the baselines with state-of-the-art performance.
Cyberbullying Detection with BiRNN and Attention Mechanism

While the social network has brought a lot of conveniences to our lives, it has also caused a series of severe problems, which include cyberbullying. Cyberbullying is an aggressive and intentional act carried out by a group or an individual to attack a victim on the Internet. Most of the existing works related to cyberbullying detection focus on making use of swearwords to classify text or images with short titles. Although previous methods such as SVM and logistic regression show some advantages in the accuracy of detection, few of them capture the semantic information of non-swearwords which could also make big difference to the final results. In this paper, we propose to use BiRNN and attention mechanism to identify bullies. BiRNN is used to integrate the contextual information, and the attention model reflects the weight of different words for classification. Meanwhile, we convert the severity calculated by the attention layer to the level of cyberbullying. Experiments conducted on three real-world text datasets show that our proposed method outperforms the state-of-art algorithms on text classification and identification effect.
Lower Bounds for Multi-server Oblivious RAMs

In this work, we consider the construction of oblivious RAMs (ORAM) in a setting with multiple servers and the adversary may corrupt a subset of the servers. We present an $$\varOmega (\log n)$$ overhead lower bound for any k-server ORAM that limits any PPT adversary to distinguishing advantage at most 1/4k when only one server is corrupted. In other words, if one insists on negligible distinguishing advantage, then multi-server ORAMs cannot be faster than single-server ORAMs even with polynomially many servers of which only one unknown server is corrupted. Our results apply to ORAMs that may err with probability at most 1/128 as well as scenarios where the adversary corrupts larger subsets of servers. We also extend our lower bounds to other important data structures including oblivious stacks, queues, deques, priority queues and search trees.
Nefnir: A high accuracy lemmatizer for Icelandic

Lemmatization, finding the basic morphological form of a word in a corpus, is an important step in many natural language processing tasks when working with morphologically rich languages. We describe and evaluate Nefnir, a new open source lemmatizer for Icelandic. Nefnir uses suffix substitution rules, derived from a large morphological database, to lemmatize tagged text. Evaluation shows that for correctly tagged text, Nefnir obtains an accuracy of 99.55%, and for text tagged with a PoS tagger, the accuracy obtained is 96.88%.
Robust and Accurate Stopping Criteria for Adaptive Randomized Sampling in Matrix-Free Hierarchically Semiseparable Construction

We present new algorithms for randomized construction of hierarchically semiseparable (HSS) matrices, addressing several practical issues. The HSS construction algorithms use a partially matrix-free, adaptive randomized projection scheme to determine the maximum off-diagonal block rank. We develop both relative and absolute stopping criteria to determine the minimum dimension of the random projection matrix that is sufficient for desired accuracy. Two strategies are discussed to adaptively enlarge the random sample matrix: repeated doubling of the number of random vectors and iteratively incrementing the number of random vectors by a fixed number. The relative and absolute stopping criteria are based on probabilistic bounds for the Frobenius norm of the random projection of the Hankel blocks of the input matrix. We discuss parallel implementation and computation and communication cost of both variants. Parallel numerical results for a range of applications, including boundary element method matrices and quantum chemistry Toeplitz matrices, show the effectiveness, scalability, and numerical robustness of the proposed algorithms.
Shared-Autonomy Navigation for Mobile Robots Driven by a Door Detection Module

Shared-autonomy approaches are the most appealing for what concerns the control of assistive devices such as wheelchairs and mobile robots, designed to aid disabled and elderly people. In this paper, we propose a shared-autonomy navigation for mobile robots, that combines the user’s interaction as well as the robots’ perception and the environment knowledge, with the information of important landmarks, namely the doors. In order to facilitate the control of the robot, our system exploits a door detection module, aiming to detect doors and especially to identify their open/close status, making the robot pass through narrow doorways without any user’s intervention. We tested the proposed system on a real mobile robot to verify the feasibility.
Empirical Analysis of Hidden Technical Debt Patterns in Machine Learning Software

[Context/Background] Machine Learning (ML) software has special ability for increasing technical debt due to ML-specific issues besides having all the problems of regular code. The term “Hidden Technical Debt” (HTD) was coined by Sculley et al. to address maintainability issues in ML software as an analogy to technical debt in traditional software. [Goal] The aim of this paper is to empirically analyse how HTD patterns emerge during the early development phase of ML software, namely the prototyping phase. [Method] Therefore, we conducted a case study with subject systems as ML models planned to be integrated into the software system owned by Västtrafik, the public transportation agency in the west area of Sweden. [Results] During our case study, we could detect HTD patterns, which have the potential to emerge in ML prototypes, except for “Legacy Features”, “Correlated features”, and “Plain Old Data Type Smell”. [Conclusion] Preliminary results indicate that emergence of significant amount of HTD patterns can occur during prototyping phase. However, generalizability of our results require analyses of further ML systems from various domains.
Real-time high-resolution video stabilization using high-frame-rate jitter sensing

Abstract In this study, the novel approach of real-time video stabilization system using a high-frame-rate (HFR) jitter sensing device is demonstrated to realize the computationally efficient technique of digital video stabilization for high-resolution image sequences. This system consists of a high-speed camera to extract and track feature points in gray-level $$512\times 496$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>512</mml:mn><mml:mo>×</mml:mo><mml:mn>496</mml:mn></mml:mrow></mml:math> image sequences at 1000 fps and a high-resolution CMOS camera to capture $$2048\times 2048$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>2048</mml:mn><mml:mo>×</mml:mo><mml:mn>2048</mml:mn></mml:mrow></mml:math> image sequences considering their hybridization to achieve real-time stabilization. The high-speed camera functions as a real-time HFR jitter sensing device to measure an apparent jitter movement of the system by considering two ways of computational acceleration; (1) feature point extraction with a parallel processing circuit module of the Harris corner detection and (2) corresponding hundreds of feature points at the current frame to those in the neighbor ranges at the previous frame on the assumption of small frame-to-frame displacement in high-speed vision. The proposed hybrid-camera system can digitally stabilize the $$2048\times 2048$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>2048</mml:mn><mml:mo>×</mml:mo><mml:mn>2048</mml:mn></mml:mrow></mml:math> images captured with the high-resolution CMOS camera by compensating the sensed jitter-displacement in real time for displaying to human eyes on a computer display. The experiments were conducted to demonstrate the effectiveness of hybrid-camera-based digital video stabilization such as (a) verification when the hybrid-camera system in the pan direction in front of a checkered pattern, (b) stabilization in video shooting a photographic pattern when the system moved with a mixed-displacement motion of jitter and constant low-velocity in the pan direction, and (c) stabilization in video shooting a real-world outdoor scene when an operator holding hand-held hybrid-camera module while walking on the stairs.
Natural Language Processing utilization in Healthcare

The significance of consolidating Natural Language Processing (NLP) techniques in clinical informatics research has been progressively perceived over the previous years, and has prompted transformative advances. Ordinarily, clinical NLP frameworks are created and assessed on word, sentence, or record level explanations that model explicit traits and highlights, for example, archive content (e.g., persistent status, or report type), record segment types (e.g., current meds, past restorative history, or release synopsis), named substances and ideas (e.g., analyses, side effects, or medicines) or semantic qualities (e.g., nullification, seriousness, or fleetingness). While some NLP undertakings consider expectations at the individual or gathering client level, these assignments still establish a minority. Here we give an expansive synopsis and layout of the difficult issues engaged with characterizing suitable natural and outward assessment strategies for NLP look into that will be utilized for clinical results research, and the other way around. A specific spotlight is set on psychological wellness investigate, a zone still generally understudied by the clinical NLP look into network, however where NLP techniques are of prominent importance. Ongoing advances in clinical NLP strategy improvement have been huge, yet we propose more accentuation should be put on thorough assessment for the field to progress further. To empower this, we give noteworthy recommendations, including an insignificant convention that could be utilized when announcing clinical NLP strategy improvement and its assessment.
NTT Neural Machine Translation Systems at WAT 2019

In this paper, we describe our systems that were submitted to the translation shared tasks at WAT 2019.This year, we participated in two distinct types of subtasks, a scientific paper subtask and a timely disclosure subtask, where we only considered English-to-Japanese and Japanese-to-English translation directions.We submitted two systems (En-Ja and Ja-En) for the scientific paper subtask and two systems (Ja-En, texts, items) for the timely disclosure subtask.Three of our four systems obtained the best human evaluation performances.We also confirmed that our new additional web-crawled parallel corpus improves the performance in unconstrained settings.
Medical Image Segmentation by Combining Adaptive Artificial Bee Colony and Wavelet Packet Decomposition

Segmentation of MRI images plays a significant and helpful job in anticipation and treatment preliminaries. Be that as it may, the power in homogeneity, sporadic fringes and one of the most exceedingly terrible pieces of the difference may cause incredible challenges in the pieces of the seeping from brain MRI images. Heaps of specialists have made in therapeutic imaging. We proposed the novel technique for image segmentation. Our technique depends on the discrete wavelet packet decomposition and ant colony optimization to reduce the disadvantage of the conventional computations in handling of the surprising shapes in restorative images preparing. To improve the exhibition of our proposed procedure we utilize the artificial bee colony to optimize and classify the feature selected or extracted by the WPD. Results shows that our method perform better to segment the curvy shapes and haemorrhagic areas in MRI images.
Understanding Chat Messages for Sticker Recommendation in Messaging Apps

Stickers are popularly used in messaging apps such as Hike to visually express a nuanced range of thoughts and utterances to convey exaggerated emotions. However, discovering the right sticker from a large and ever expanding pool of stickers while chatting can be cumbersome. In this paper, we describe a system for recommending stickers in real time as the user is typing based on the context of the conversation. We decompose the sticker recommendation (SR) problem into two steps. First, we predict the message that the user is likely to send in the chat. Second, we substitute the predicted message with an appropriate sticker. Majority of Hike's messages are in the form of text which is transliterated from users' native language to the Roman script. This leads to numerous orthographic variations of the same message and makes accurate message prediction challenging. To address this issue, we learn dense representations of chat messages employing character level convolution network in an unsupervised manner. We use them to cluster the messages that have the same meaning. In the subsequent steps, we predict the message cluster instead of the message. Our approach does not depend on human labelled data (except for validation), leading to fully automatic updation and tuning pipeline for the underlying models. We also propose a novel hybrid message prediction model, which can run with low latency on low-end phones that have severe computational limitations. Our described system has been deployed for more than 6 months and is being used by millions of users along with hundreds of thousands of expressive stickers.
Multi-Sensor Time-Series Classification for Activity Tracking Under Variable Length

Recent advances in mobile devices, wearable sensors and data analytics are enabling greater adoption of remote health monitoring systems, where human activity recognition (HAR) plays a key role. Most HAR systems assume that consecutive data segments are independent and aim at accurately classifying each individual data segment. However, because training and operating contexts are often different, and collecting labels can be time-consuming and expensive, the recognition accuracy is typically considerably lower in the new (operating) context. In this paper, we propose a framework for HAR that has several unique capabilities: 1) Turns predictions into useful information (e.g. daily distribution of activities for a user) that could be assessed by a clinician or health professional. Our goal is to mitigate the effects of variation between a training and an operating context. 2) Leverages the dependence between consecutive data samples to perform robust activity recognition. And 3) Performs activity classification for variable length set of activities. The framework we present encompasses two main modules. The first is a Recurrent Long Short-Term Memory deep neural network, the core component which gives us the capability to capture the dependence between consecutive sensor data samples and generate labels for every micro segment. The second is a post-processing step performed for uncertainty filtering and label smoothing of decisions. The filtering method enables the system to recognize activities in the presence of difference in the training and testing data distributions, and the smoothing method makes the system properly capture the distribution of activities. We demonstrate that our deep learning architecture can achieve 90% accuracy in standard training scenarios. Furthermore, we show that, in the case of inter-subject variation, the two methods allow us to still accurately capture the distribution of activities.