Reviewerly/exHarmony huggingface dataset:
Explainable Learning for Disease Risk Prediction Based on Comorbidity Networks

Disease risk modeling is of great interest to clinicians and healthcare policy makers in reducing preventable harm and associated costs. A disease risk prediction model is preferable if it (1) exhibits superior prediction performance and (2) constructs explainable rules to allow medical professionals to understand why and how the prediction was made. Existing studies usually focus on one of the two features. In this study, we propose a comorbidity network involved end-to-end trained disease risk prediction model. The adoption of side information and the end-to-end framework together ensure both high accuracy and transparency to the model. The prediction performances of the proposed model are demonstrated by using a real case study based on three years of medical histories from the Hong Kong Hospital Authority is considered. Results show that the proposed model exhibits superior prediction performance while learns explainable rules.
Perspectives on Deep Multimodel Robot Learning

In the last decade, deep learning has revolutionized various components of the conventional robot autonomy stack including aspects of perception, navigation and manipulation. There have been numerous advances in perfecting individual tasks such as scene understanding, visual localization, end-to-end navigation and grasping, which has given us a critical understanding on how to create individual architectures for a specific task. This now brings us to the question, as to whether this disjoint learning of models for robotic tasks, effective in the real-world and whether it is scalable? And more generally, is training task specific models on task specific datasets beneficial to architecting robot intelligence as a whole? In this paper, we argue that multimodel learning or joint multi-task learning is an effective strategy for enabling robots to excel across multiple domains. We describe how multimodel learning can facilitate generalization to unseen scenarios by utilizing domain-specific cues from auxiliary tasks and discuss some of the current mechanisms that can be employed to design multimodel frameworks for robot autonomy.
Analysis of Distribution Test-based and Feature-based Approaches toward Automatic Modulation Classification

Automatic modulation classification (AMC) is a signal processing approach to determine a received signal's modulation scheme without any a priori information about that signal's configuration. Three main approaches have been proposed for this task, namely Likelihood-based, Feature-based and Distribution Test-based AMC. Each of these approaches has different levels of computational complexity affecting their ability to conduct AMC in real-time and achieving different levels of modulation classification accuracy. Thus, there is a trade-off between computational complexity and classification accuracy in this domain. This paper investigates the real-world scenarios of AMC applications with the emphasis on the two most feasible low-complexity approaches: Feature-based (FB) and Distribution Test-based (DT) AMC. This study has specifically evaluated these approaches with focus on their classification accuracy and their computational complexity. For comprehensive evaluation purposes the study encompassed signals utilizing 8-ASK, 8-FSK, 32-PSK and 32-QAM modulation schemes, as representatives of the most prominent modulation schemes in current wireless systems. Our analysis shows that DT classifiers exhibit on average a 17.5% lower computational complexity than the investigated FB classifier, with about on average 12% higher classification accuracy in SNRs in the range of 0 to 10 dB. On the other hand, the DT approach classifiers show not only on average 31% higher computational complexity than the FB classifier, but it also has an approximately 10% lower classification accuracy for SNRs in the range of -10 to 0 dB.
A Gesture Recognition Algorithm for Hand-Assisted Laparoscopic Surgery

Minimally invasive surgery (MIS) techniques are growing in quantity and complexity to cover a wider range of interventions. More specifically, hand-assisted laparoscopic surgery (HALS) involves the use of one surgeon’s hand inside the patient whereas the other one manages a single laparoscopic tool. In this scenario, those surgical procedures performed with an additional tool require the aid of an assistant. Furthermore, in the case of a human–robot assistant pairing a fluid communication is mandatory. This human–machine interaction must combine both explicit orders and implicit information from the surgical gestures. In this context, this paper focuses on the development of a hand gesture recognition system for HALS. The recognition is based on a hidden Markov model (HMM) algorithm with an improved automated training step, which can also learn during the online surgical procedure by means of a reinforcement learning process.
Facial Recognition Using Deep Learning

Facial recognition is a fast-growing area used widely in identity verification, monitoring, and access control systems. High recognition rate and less training time are key factors in facial recognition problem. In our paper, we have compared artificial neural network (ANN) and convolutional neural network (CNN) for this specified problem. Our dataset contains more than 14,855 images out of which 1325 images with varied expressions and backgrounds are of the subject to be recognized. Results show the supremacy of CNN over ANN in terms of accuracy in facial recognition and less number of epochs, i.e. lesser training time.
SISTEM INFORMASI INVENTARIS BARANG BERBASIS WEB PADA SMP NEGERI 1 BUER

This study aims to design and build an Information System Web-based Inventory of Goods at Buer 1 Junior High School in order to help part of the inventory of goods in borrowing goods, information on the condition of the goods, numbering of goods and data collection of transactions from the purchase of goods. The research method used is a qualitative descriptive method and data collection methods used in this study, namely the method of observation and interviews and using the Waterfall method as a method of software development. Information Based Inventory Information System Web at 1 Buer Public Middle School was built using Sublime Text language PHP programming and MySql database. Inventory Information System Web Based At SMP Negeri 1 Buer makes it easy for Inventory Admin in item numbering, data collection, information on the condition of the goods, the process of borrowing and returning goods and data collection of goods from the purchase transaction
Vietnamese Sentiment Analysis for Hotel Review based on Overfitting Training and Ensemble Learning

In this study, we propose a machine learning model in analyzing customer opinions based on Vietnamese text: the case of hotel service, classifying a review as a positive or a negative. In particular, our solution focuses on improvement: preprocessing, standardization, training data relabelling with Error Analysis method. Besides, training data is enhanced with emotional dictionary; 5-Fold Cross Validation and Confusion Matrix are used to control overfitting and underfitting and to test the model; Hyperparameter Tuning method is used to optimize model parameters; Ensemble Methods are used to combine several machine learning techniques into the most efficient predictive model. Used data is collected from website booking.com.
Software Defect Prediction Using Bad Code Smells: A Systematic Literature Review

The challenge of effective refactoring in the software development cycle brought forward the need to develop automated defect prediction models. Among many existing indicators of bad code, code smells have attracted particular interest of both the research community and practitioners in recent years. In this paper, we describe the current state-of-the-art in the field of bug prediction with the use of code smells and attempt to identify areas requiring further research. To achieve this goal, we conducted a systematic literature review of 27 research papers published between 2006 and 2019. For each paper, we (i) analysed the reported relationship between smelliness and bugginess, as well as (ii) evaluated the performance of code smell data used as a defect predictor in models developed using machine learning techniques. Our investigation confirms that code smells are both positively correlated with software defects and can positively influence the performance of fault detection models. However, not all types of smells and smell-related metrics are equally useful. God Class, God Method, Message Chains smells and Smell intensity metric stand out as particularly effective. Smells such as Inappropriate Intimacy, Variable Re-assign, Clones, Middle Man or Speculative Generality require further research to confirm their contribution. Metrics describing the introduction and evolution of anti-patterns in code present a promising opportunity for experimentation.
Privacy-Preserving Gaussian Process Regression – A Modular Approach to the Application of Homomorphic Encryption

Much of machine learning relies on the use of large amounts of data to train models to make predictions. When this data comes from multiple sources, for example when evaluation of data against a machine learning model is offered as a service, there can be privacy issues and legal concerns over the sharing of data. Fully homomorphic encryption (FHE) allows data to be computed on whilst encrypted, which can provide a solution to the problem of data privacy. However, FHE is both slow and restrictive, so existing algorithms must be manipulated to make them work efficiently under the FHE paradigm. Some commonly used machine learning algorithms, such as Gaussian process regression, are poorly suited to FHE and cannot be manipulated to work both efficiently and accurately. In this paper, we show that a modular approach, which applies FHE to only the sensitive steps of a workflow that need protection, allows one party to make predictions on their data using a Gaussian process regression model built from another party's data, without either party gaining access to the other's data, in a way which is both accurate and efficient. This construction is, to our knowledge, the first example of an effectively encrypted Gaussian process.
Two-Layer Reversible Data Hiding Based on AMBTC Image With (7, 4) Hamming Code

In Malik et al.'s method, each pixel can be embedded with log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 3 bits by being modified at most 1. Thus, their method achieves significant hiding capacity while maintaining good visual quality. However, in their method, the first lower and the first upper quantization levels must be excluded from data embedding in order to ensure reversibility. To this end, we propose a two-layer reversible data hiding (RDH) scheme in combination with (7,4) Hamming code. In the 1st-layer embedding, each block can be embedded with 16 bits. In the 2nd-layer embedding, each already-modified block can carry 6 bits or 12 bits by taking advantage of (7,4) Hamming code that hides three bits by modifying only one bit. At most 2-bit additional information is needed to help decoders to correctly extract the original lower and upper quantization levels. By means of two-layer embedding, our method achieves higher embedding capacity while maintaining almost the same visual quality, compared with Malik et al.'s method. Experimental results also demonstrate our effectivity.
A Novel Dynamic Hand Gesture and Movement Trajectory Recognition model for Non-Touch HRI Interface

Efficient Human Robot Interaction (HRI) interface is very much demandable for controlling the semi-autonomous robots. Hand gesture recognition is an effective form of non-touch instruction. Thus, human hand gesture recognition is mostly used technique for HRI. However, in most research, some sensor devices or marker are incorporate with the hand or a large number of hand image and hand gesture sequence is stored and process for gesture recognition in machine learning techniques, which are costly and demand complex computation. From this point of view, an efficient dynamic hand gesture and movement trajectory recognition system is proposed in this paper, which can be used in real-time fashion for effective HRI interface. In the proposed dynamic gesture recognition system, hand images and skeleton information are extracted for Kinect sensor. Hands are segmented from the video frame using a skin color segmentation model from the region of interest (ROI) around the palm position of both hands. The hand open and close states are identified by calculating the position of palm and extreme position of Figure for activating the instruction recognition. The trajectory of segmented hands and the hands open state are considered for formulating the model of gesture with respect to the selected index points of body skeleton. Finally, several gesture models are derived to recognize the instruction during temporal gesture movement. For validating the proposed model, an experimental environment is setup in experimental lab. Ten volunteers are considered and tested the proposed system for six gesture instructions. According to the experiment, the proposed system shows 94.5% average recognition accuracy for dynamic motion instruction identification.
Text Normalization for Indonesian Abbreviated Word Using Crowdsourcing Method

There are many languages in the world, each one of them has its own characteristic. This makes languages a very interesting research, especially in text mining field. Written language need to be preprocessed first to get its normalized form. Abbreviated words are one of the problems in text mining. System cannot process the text optimally due to the different meaning the abbreviated words may have. This research objective is to develop Indonesian abbreviated words dataset, which can then be used to normalize any abbreviated words in Indonesian. Crowdsourcing was selected as a method to develop the dataset, because only human is able to translate abbreviated words into normal form of the words. From 1170 sentences that were tested, 1063 sentences were answered correctly while 107 sentences were answered incorrectly by the respondents. This research accuracy is about 90.85%. However, there is still a problem which occurred when the abbreviated word has more than one meaning/normal form, thus unique keywords are needed to determine which meaning is most accurate.
Hybrid Strategies for Choosing Suitable Cryptosystem Based on Game and Information Theories

This paper provides a new approach for enhancing the security of a computer. This enhancement can be achieved by treatment with the best strategy for executing some chosen crypto-systems on a computer. The enhancement process is done in two stages; the first stage depends on the result of the security evaluation of the crypto-system. The second stage compares several crypto-systems which are evaluated in the first stage. This research is a complementary to the previous published papers and focused on comparing different crypto-systems which are strongly different in their securities, and propose the best strategies for their use. The proposed approach used information theory and triangular game as a basic rule in order to provide accurate indicators for the security of crypto-systems, and also used diagonal game to suggest the best strategy to use the cryptosystems.
The lower bound of quantum memory-assisted entropic uncertainty and secret rate for two topological qubits under environments

The uncertainty principle sets a bound on our ability to predict the measurement outcome of two incompatible observables precisely. The entropic uncertainty lower bound can be improved by considering an additional particle as the quantum memory B which has correlation with the measured particle A. Here, we consider the quantum memory-assisted entropic uncertainty for the case in which the quantum memory and the measured particle are topological qubits. In our scenario the topological quantum memory B interacts with environment. We will study Ohmic-like fermionic and bosonic environments. We also investigate the effect of the fermionic and bosonic environments on the lower bounds of the amount of the key which can be extracted per state by Alice and Bob for quantum key distribution protocols.
Real time noisy dataset implementation of optical character identification using CNN

Optical character recognition (OCR) is one of the major research problem in real time applications and it is used to recognise all the characters in an image. As English is a universal language, character recognition in English is a challenging task. Deep learning approach is one of the solution for the recognition of optical characters. Aim of this research work is to perform character recognition using convolutional neural network with LeNET architecture. Dataset used in this work is scanned passport dataset for generating all the characters and digits using tesseract. The dataset has training set of 60,795 and testing set of 7,767. Total samples used are 68,562 which is separated by 62 labels. Till now there is no research on predicting all 52 characters and ten digits. The algorithm used in this work is based on deep learning with appropriate some layer which shows significant improvement in accuracy and reduced the error rate. The developed model was experimented with test dataset for prediction and can produce 93.4% accuracy on training, and 86.5% accuracy on the test dataset.
A Small Object Detection Solution by Using Super- Resolution Recovery

Small object detection is the key challenge to aerial image object detection. Small objects in aerial images are usually too fuzzy to detect. This paper proposes a solution by using Generative Adversarial Network to recover small objects' low-resolution image to high-resolution to get better detection performance. Experiment result shows this solution achieves mAP 68.38% and missing rate 14.23%.
Triptych: Logarithmic-Sized Linkable Ring Signatures with Applications

Ring signatures are a common construction used to provide signer ambiguity among a non-interactive set of public keys specified at the time of signing. Unlike early approaches where signature size is linear in the size of the signer anonymity set, current optimal solutions either require centralized trusted setups or produce signatures logarithmic in size. However, few also provide linkability, a property used to determine whether the signer of a message has signed any previous message, possibly with restrictions on the anonymity set choice. Here we introduce Triptych, a family of linkable ring signatures without trusted setup that is based on generalizations of zero-knowledge proofs of knowledge of commitment openings to zero. We demonstrate applications of Triptych in signer-ambiguous transaction protocols by extending the construction to openings of parallel commitments in independent anonymity sets. Signatures are logarithmic in the anonymity set size and, while verification complexity is linear, collections of proofs can be efficiently verified in batches. We show that for anonymity set sizes practical for use in distributed protocols, Triptych offers competitive performance with a straightforward construction.
D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

Sparseness is the distinctive aspect of big data generated by numerous applications at present. Furthermore, several similar records exist in real-world sparse datasets. Based on Iterative Trimmed Transaction Lattice (ITTL), the recently proposed TRICE algorithm learns frequent itemsets efficiently from sparse datasets. TRICE stores alike transactions once, and eliminates the infrequent part of each distinct transaction afterward. However, removing the infrequent part of two or more distinct transactions may result in similar trimmed transactions. TRICE repeatedly generates ITTLs of similar trimmed transactions that induce redundant computations and eventually, affects the runtime efficiency. This paper presents D-GENE, a technique that optimizes TRICE by introducing a deferred ITTL generation mechanism. D-GENE suspends the process of ITTL generation till the completion of transaction pruning phase. The deferral strategy enables D-GENE to generate ITTLs of similar trimmed transactions once. Experimental results show that by avoiding the redundant computations, D-GENE gets better runtime efficiency. D-GENE beats TRICE, FP-growth, and optimized versions of SaM and RElim algorithms comprehensively, especially when the difference between distinct transactions and distinct trimmed transactions is high.
Hierarchical Question-Aware Context Learning with Augmented Data for Biomedical Question Answering

This paper is concerned with the task of biomedical Question Answering (QA) which refers to extracting an answer to the given question from a biomedical context. Current works have made progress on this task, but they are still severely restricted by the insufficient training data due to the domain-specific nature, which motivates us to further explore a powerful way to solve this problem. We propose a Hierarchical Question-Aware Context Learning (HQACL) model for the biomedical QA task constituted by multi-level attention. The interaction between the question and the context can be captured layer by layer, with multi-grained embeddings to strengthen the ability of the language representation. A special training method called DA, including two parts namely domain adaptation and data augmentation, is also introduced to enhance the model performance. Domain adaptation can be defined as pre-training on a large-scale open-domain dataset and fine-tuning on the small training set of the target domain. As for the data augmentation, the Round-trip translation method is adopted to create new data with various expressions, which almost doubles the training set. The public biomedical dataset collected from PubMed provided by BioASQ is used to evaluate our model. The results show that our approach is superior to the best recent solution and achieves a new state of the art.
Supporting the deployment of ISO-based project management processes with agile metrics

Agile approaches are well perceived in software development companies. These approaches are not faced with traditional process models. This research analyses how by defining and monitoring a set of Agile metrics, Agile mature companies can be also conformant with the best practices proposed by the targeted ISO process reference models. Conformance has been identified with five Technical Management processes of the ISO/IEC/IEEE 12207 standard and with two activities of the Project Management process of the ISO/IEC TR 29110-5-1-2 standard. The findings may be of the interest of those Agile settings that need to work according to the process model established in the company.
A Non-Touchscreen Tactile Wearable Interface as an Alternative to Touchscreen-Based Wearable Devices

Current consumer wearable devices such as smartwatches mostly rely on touchscreen-based user interfaces. Even though touch-based user interfaces help smartphone users quickly adapt to wearable devices with touchscreens, there exist several limitations. In this paper, we propose a non-touchscreen tactile wearable interface as an alternative to touchscreens on wearable devices. We designed and implemented a joystick-integrated smartwatch prototype to demonstrate our non-touchscreen tactile wearable interface. We iteratively improved and updated our prototype to improve and polish interaction ideas and prototype integration. To show feasibility of our approach, we compared and contrasted form factors of our prototype against the latest nine commercial smartwatches in terms of their dimensions. We also show response time and accuracy of our wearable interface to discuss our rationale for an alternative and usable wearable UI. With the proposed tactile wearable user interface, we believe our approach may serve as a cohesive single interaction device to enable various cross-device interaction scenarios and applications.