Reviewerly/exHarmony huggingface dataset:
Ultrasound-Based Diagnosis of Breast Tumor with Parameter Transfer Multilayer Kernel Extreme Learning Machine

B-mode ultrasound (BUS) imaging is widely used for diagnosis of breast tumors. In recent years, another ultrasound modality, namely ultrasound elastography (UE), has also been successfully applied in clinical practice. Although the combination of bimodal ultrasound imaging can improve the diagnosis accuracy for breast tumors, the single-modal ultrasound-based diagnosis is more popular in clinical practice, especially in the rural areas. Since transfer learning (TL) can transfer knowledge from a source domain to a target domain to help train more effective model, we propose to develop a single-modal ultrasound-based computer-aided diagnosis (CAD) with the transferred information from an additional modality in this work. We propose a projective model (PM) based multilayer kernel extreme learning machine (ML-KELM-PM) algorithm, which performs the parameter transfer approach in classifier to perform TL in CAD. The experimental results on a bimodal ultrasound image dataset show that the proposed ML-KELM-PM algorithm outperforms all the compared algorithms for the single-modal ultrasound-based CAD for breast tumors.
Resource allocation and target setting based on virtual profit improvement

One application of Data Envelopment Analysis (DEA) is the resource allocation and target setting among homogeneous Decision Making Units (DMUs). In this paper, we assume that all units are under the supervision and control of a central decision making unit, for instance chain stores, banks, schools, etc. The aim is to allocate available resources among units in a way that the so-called organisational overall 'virtual profit' is maximized. Our method is highly flexible in decision making to achieve the goals of the Decision Maker (DM). The resulting production plans maintain the following characteristics: (1) the virtual profit of each unit is calculated with a common set of weights; (2) the selected weights for calculating the virtual profit prevent the virtual profit of the system from getting worse; (3) the virtual profits of less profitable units are improved as much as possible. The proposed method is illustrated with a simple numerical example and a real life application.
A Multi-channel Deep Learning Approach for Segmentation of the Left Ventricular Endocardium from Cardiac Images

Cardiac segmentation is the first most important step in assessing cardiac diseases. However, it still remains challenging owing to the complicated information of myocardium's boundary. In this work, we investigate approaches based on deep learning for fully automatic segmentation of the left ventricular (LV) endocardium using cardiac magnetic resonance (CMR) images. The deep convolutional neural network architectures, specifically, GoogleNet and U-Net, are modified and deployed to extract the features and then classify each pixel into either endocardium or background. Since adjacent frames for a given slice are imaged over a short time period across a cardiac cycle, the LV endocardium exhibit strong temporal correlation. To utilize the temporal information of heart motion to assist segmentation, we propose to construct multi-channel cardiac images by combining adjacent frames together with the current frame, which are used as the inputs for deep learning models. This allows the deep learning models to automatically learn spatial and temporal information. The performance of our constructed networks is evaluated by using the Dice metric to compare the segmented areas with the manually segmented ground truth. The experiments show that the multi-channel approaches converge more rapidly and achieve higher segmentation accuracy compared to the single channel approach.
Demo

Both cameras and IoT devices have their particular capabilities in tracking moving objects. Their correlations are, however, unclear. In this work, we consider using a drone to track ground objects. We demonstrate how to retrieve IoT data from devices, which are attached on human objects, and correctly tag them on the human objects captured by a drone view. This is the first work correlating IoT data and computer vision from a drone camera. Potential applications of this work include aerial surveillance, people tracking, and intelligent human-drone interaction.
To Combine or Not To Combine? A Rainbow Deep Reinforcement Learning Agent for Dialog Policies

We explore state-of-the-art deep reinforcement learning methods such as prioritized experience replay, double deep Q-Networks, dueling network architectures, distributional learning methods for dialog policy.Our main findings show that each individual method improves the rewards and the task success rate but combining these methods in a Rainbow agent, which performs best across tasks and environments, is a non-trivial task.We, therefore, provide insights about the influence of each method on the combination and how to combine them to form the Rainbow agent.
Discovery of closed spatio-temporal sequential patterns from event data

In the paper, we first thoroughly examine and prove properties of the participation index of spatio-temporal sequential patterns. Then, we introduce notions of a closure of a spatio-temporal sequential pattern and a closed spatio-temporal sequential pattern, as well as investigate and prove their properties. In particular, we prove that the set of all participation index strong closed spatio-temporal sequential patterns constitues a lossless representation of all participation index strong spatio-temporal sequential patterns. We also propose an algorithm, called CST-SPMiner, for discovering all participation index strong closed spatio-temporal sequential patterns. CST-SPMiner is an adaptation of the STBFM algorithm, which was proposed recently for the discovery of spatio-temporal sequential patterns with high participation index. While STBFM uses the CSP-tree structure for time-efficient candidate patterns generation and evaluation, CST-SPMiner uses it also for fast identification of closed patterns. Efficiency and effectiveness of our algorithm were verified on real crime data for Boston.
Bob - A General Culture Game with Voice Interaction

This paper presents a general culture game, called Bob, implemented as a skill for Amazon’s software assistant Alexa. The main motivation of this work is to enable learning through games and smart devices, which are nowadays part of most children’s lives and homes. The Bob game provide users with general culture questions from the geography field, more specifically related to cities, countries, and lakes. The questions are formed by extracting and analyzing information from two major knowledge sources, DBpedia and Wikidata, both massively used in the natural language processing field. Bob can initiate general culture tests, during which questions will be automatically adapted to the knowledge level of the player through Computer Adaptive Testing (CAT). Following user specified settings, Bob also offers the possibility to rank players according to their performance and to post Twitter statistics on request. One major advantage of our solution comes from the fact that the application can be used through Amazon Echo or through a smartphone, allowing the child to access it from home or from any place with Internet connection.
Deep Learning for Crowd Counting: A Survey

The growth of deep learning for crowd counting is immense in the recent years. This results in numerous deep learning model developed with huge multifariousness. This paper aims to capture a big picture of existing deep learning models for crowd counting. Hence, the development of novel models for future works can be accelerated.
Auto-Selecting Receptive Field Network for Visual Tracking

Recently, Convolutional Neural Networks (CNNs) have shown tremendous potential in the visual tracking community.It is well-known that the receptive field is a critical factor for CNN affecting performance.However, standard CNNs based tracking methods design the receptive fields of artificial neurons in each layer that have the same size.We identify the main bottleneck of affecting the tracking accuracy as regular receptive fields.To settle the problem, we propose an Auto-Selecting Receptive Field Network (ASRF) to select receptive field information and effective clues dynamically.In particular, a Selective Receptive Field Block (SRFB) is designed to adaptively adjust receptive field size for each neuron according to multiple scales of input information.Additionally, we develop a Multi-Scale Receptive Field module (MSRF) that marks a further step in selecting effective clues from different scale receptive fields.The proposed ASRF method performs favorably against state-of-the-art trackers on five benchmarks, including OTB-2013, OTB-2015, UAV-123, VOT-2015, and VOT-2017 while running beyond real-time tracking speed.
Evaluating Automatic Speech Recognition for Child Speech Therapy Applications

Automatic speech recognition (ASR) technology can be a useful tool in mobile apps for child speech therapy, empowering children to complete their practice with limited caregiver supervision. However, little is known about the feasibility of performing ASR on mobile devices, particularly when training data is limited. In this study, we investigated the performance of two low-resource ASR systems on disordered speech from children. We compared the open-source PocketSphinx (PS) recognizer using adapted acoustic models and a custom template-matching (TM) recognizer. TM and the adapted models significantly out-perform the default PS model. On average, maximum likelihood linear regression and maximum a posteriori adaptation increased PS accuracy from 59.4% to 63.8% and 80.0%, respectively, suggesting that the models successfully captured speaker-specific word production variations. TM reached a mean accuracy of 75.8%
A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness.
Using the Cognitive Approach to Coherence Relations for Discourse Annotation

The Cognitive approach to Coherence Relations (Sanders, Spooren, &amp; Noordman, 1992) was originally proposed as a set of cognitively plausible primitives to order coherence relations, but is also increasingly used as a discourse annotation scheme. This paper provides an overview of new CCR distinctions that have been proposed over the years, summarizes the most important discussions about the operationalization of the primitives, and introduces a new distinction (disjunction) to the taxonomy to improve the descriptive adequacy of CCR. In addition, it reflects on the use of the CCR as an annotation scheme in practice. The overall aim of the paper is to provide an overview of state-of-the-art CCR for discourse annotation that can form, together with the original 1992 proposal, a comprehensive starting point for anyone interested in annotating discourse using CCR.
"Superstition" in the Network: Deep Reinforcement Learning Plays Deceptive Games

Deep reinforcement learning has learned to play many games well, but failed on others. To better characterize the modes and reasons of failure of deep reinforcement learners, we test the widely used Asynchronous Actor-Critic (A2C) algorithm on four deceptive games, which are specially designed to provide challenges to game-playing agents. These games are implemented in the General Video Game AI framework, which allows us to compare the behavior of reinforcement learning-based agents with planning agents based on tree search. We find that several of these games reliably deceive deep reinforcement learners, and that the resulting behavior highlights the shortcomings of the learning algorithm. The particular ways in which agents fail differ from how planning-based agents fail, further illuminating the character of these algorithms. We propose an initial typology of deceptions which could help us better understand pitfalls and failure modes of (deep) reinforcement learning.
Text Localization in Natural Scene Images Using Extreme Learning Machine

Text localization from natural scenes is a challenging task and has many applications in the modern era of autonomous systems, IOT and so on. It involves many challenges such as variation in shape and sizes of texts, variable illumination, occlusions, variable orientations, and a huge number of non-text objects in nature which has a form similar to textual elements. In our proposed approach we have introduced the use of ELM in this domain based on histogram of oriented gradients and entropy based features. Along with MSER based candidate region selection, filtering false positives with stroke width transform, region merging with morphological operations, a complete system is proposed and tested on the ICDAR 2015 dataset. We achieved with a precision of 94.02%, recall of 95.39% and F-measure of 94.70%. The proposed algorithm surpasses many other approaches on the dataset. The ELM classifier performs better than other common classifiers like the SVM, Bayes or KNN. It is also shown to be more statistically significant than strong classifiers like SVM.
A Research Landscape on Formal Verification of Software Architecture Descriptions

One of the many different purposes of software architecture descriptions is contributing to an early analysis of the architecture with respect to quality attributes. The critical nature of many software systems calls for formal approaches aiming at precisely verifying if their designed architectures can meet important properties such as consistency, completeness, and correctness. In this context, it is worthwhile investigating the role of architecture descriptions to support the formal verification of software architectures to ensure their quality, as well as how such a process happens and is supported by existing languages and verification tools. To evaluate the research landscape on this subject, we have carried out a systematic mapping study in which we collected and analyzed studies available at the literature on formal verification of architecture descriptions. This work contributes with (i) a structured overview and taxonomy of the current state of the art on this topic and (ii) the elicitation of important issues to be addressed in future research.
Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English

This paper presents the NICT's supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks.For all the translation directions, we built state-of-the-art supervised neural (NMT) and statistical (SMT) machine translation systems, using monolingual data cleaned and normalized.Our combination of NMT and SMT performed among the best systems for the four translation directions.We also investigated the feasibility of unsupervised machine translation for lowresource and distant language pairs and confirmed observations of previous work showing that unsupervised MT is still largely unable to deal with them.
Identify Factors that Influence Healthcare Quality by Adoption Mobile Health Application in KSA E-Health

The main purpose of this study was to identify factors that affect healthcare quality in the ksa context Mobile Health Application (MHAs) identified to positively influence the quality of health service provision in different health centers with a decrease in the number of health costs in various centers. The objectives of this research to proposes the use of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, which will highlight the positive gains to be identified with the acceptance of the MHA quality. Across the health care, sector UTAUT model has been used to evaluate the adoption and integration of the MHA quality. The primary aim of the research is to test trust information elements which are identified to influence adoption and integration on the preference to use MHA quality by different healthcare practitioners.Methods: The study proposes to use surveys across the Saudi Arabia hospitals to gather all required data from different healthcare centres and professionals who are already familiar with the MHA system. Total of 266 respondents is used from 522 surveyed forms and conduct necessary data analysis.Results: the hypotheses H1, H2, H3, H5, H6 and H7were supported.
Annealing strategy for an enhance rule pruning technique in ACO-Based rule classification

&lt;span&gt;Ant colony optimization (ACO) was successfully applied to data mining classification task through ant-mining algorithms. Exploration and exploitation are search strategies that guide the learning process of a classification model and generate a list of rules. Exploitation refers to the process of intensifying the search for neighbors in good regions, &lt;/span&gt;&lt;span&gt;whereas exploration aims towards new promising regions during a search process. &lt;/span&gt;&lt;span&gt;The existing balance between exploration and exploitation in the rule construction procedure is limited to the roulette wheel selection mechanism, which complicates rule generation. Thus, low-coverage complex rules with irrelevant terms will be generated. This work proposes an enhancement rule pruning procedure for the ACO algorithm that can be used in rule-based classification. This procedure, called the annealing strategy, is an improvement of ant-mining algorithms in the rule construction procedure. Presented as a pre-pruning technique, the annealing strategy deals first with irrelevant terms before creating a complete rule through an annealing schedule. The proposed improvement was tested through benchmarking experiments, and results were compared with those of four of the most related ant-mining algorithms, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with hybrid pruner. &lt;/span&gt;&lt;span&gt;Results display that our proposed technique achieves better performance in terms of classification accuracy, model size, and &lt;/span&gt;&lt;span&gt;computational time. &lt;/span&gt;&lt;span&gt;The proposed annealing schedule can be used in other ACO variants for different applications to improve classification accuracy.&lt;/span&gt;
An evidential integrated method for maintaining case base and vocabulary containers within CBR systems

Cases and vocabulary maintenance presents a crucial task to preserve high competent Case-Based Reasoning (CBR) systems, since the accuracy of their offered solutions are strongly dependent on stored cases and their describing attributes quality. The maintenance aims generally at eliminating two types of undesirable knowledge which are noisy and redundant data. However, inexpedient Case Base Maintenance (CBM) or vocabulary maintenance may not only greatly decrease CBR competence in solving new problems, but also reduce its performance in term of retrieval time. Besides, to provide a high maintenance quality, it is necessary to manage uncertainty within knowledge since "real-world data are never perfect" and stored cases within a CBR system's Case Base (CB) describe real-world experiences. Hence, we propose, in this paper, a new integrated method that maintains both of the CB and the vocabulary knowledge containers of CBR systems by offering a new alternating technique to properly detect noisiness and redundancy whether in cases or features. During the learning steps of our new integrated maintenance policy, which drives the decision making about cases and attributes selection, we manage uncertainty using one among the most powerful tools called the Belief Function Theory.
The Development of the Chinese Sentiment Lexicon for Internet

This paper examines the development of the Chinese Sentiment Lexicon for Internet (CSLI), a sentiment lexicon for capturing the valence and arousal in Chinese online social media texts. We first review the current sentiment lexicons and their building process, including the collection of words, judging the emotionality of words, and testing reliability and validity. In Study 1, we develop CSLI and test its initial reliability and validity. In Study 2, we further test the convergent validity of CSLI by examining its correlations with human judgement in 429 aggregated Weibo comments. In Study 3, the predictive validity of CSLI is examined by linking its results to personality traits among 52 undergraduates. Two replication studies are also conducted to verify the findings in Study 2 and 3. The results have generally supported the reliability and validity of CSLI. Therefore, CSLI can be used as a research tool to capture the degree of valence and arousal in Chinese online social media texts. Its potential to promote human well-being is also discussed.
Improving the security of LSB image steganography

Steganography is the technique of hiding secret data (message) within any media such as digital color image. In this paper we will merge steganography process with cryptography process in order to increase the security of the proposed method. The steganography process will based on LSB method, while the cryptography process will based on generating a huge private key and selecting a special function for encryption decryption. The proposed method will be implemented in order to calculate some performance parameters to prove the efficiency of the proposed method.
Contextualized Word Representations from Distant Supervision with and for NER

We describe a special type of deep contextualized word representation that is learned from distant supervision annotations and dedicated to named entity recognition.Our extensive experiments on 7 datasets show systematic gains across all domains over strong baselines, and demonstrate that our representation is complementary to previously proposed embeddings.We report new state-of-the-art results on CONLL and ONTONOTES datasets.