Reviewerly/exHarmony huggingface dataset:
Chronic Pain Scales in Tangible Materials

For chronic pain patients, it is a challenge to communicate what their pain feels like - both to friends and relatives and to healthcare professionals. Traditionally doctors employ pain scales (numbers, standardised words, images of facial expressions), but pain scales can be challenging for patients, as chronic pain is experienced individually. They are also difficult to relate to for relatives and professionals, who do not have personal experience of chronic pain. In this pictorial, we present a series of design explorations with tangible materials to offer an alternative for chronic patients to express pain. In collaboration with six patients, we identify eight different types of pain experiences and the material metaphors that the patients may use to express them. We also develop three examples of tailored design artefacts, pain communicators, that can function as 'tangible pain scales' to express pain experiences.
Strategies to Improve Performance of Convolutional Neural Network on Histopathological Images Classification

Convolutional Neural Network (CNN) has been widely used in medical image processing. Histopathology is one of modality or images for a pathologist to analyze the status of cancer. The unstructured pattern of this image cause the problem, tend to miss identification or takes more time to analyze by the pathologist. Besides that, Deep learning training generally requires powerful hardware resources to improve performance during the training. Therefore, to address these problems, we propose two main activities in this study; to accelerate training time and to enhance the histopathology dataset. We train our CNN on three similar GPU specification (GTX-1080) as an alternative to become training time is faster. Mean-shift filter is one of the low-pass filter technique. We use this to handle unstructured pattern on histopathology images to enhance this dataset. The performance of all three GPUs is presented during the training process with 500 epochs measure by the speedup. Meanwhile, the performance of model testing is carried out with several batch-size selection scenarios from 32,64,128 and 256. The use of mean-shift can improve convergence during training in 128 batch-size become faster.
Chain of Antichains: An Efficient and Secure Distributed Ledger

Since the inception of blockchain and Bitcoin (Nakamoto, Bitcoin: A peer-to-peer electronic cash system (2008) [18]), a decentralized-distributed ledger system and its associated cryptocurrency, respectively, the world has witnessed a slew of newer adaptations and applications. Although the original distributed ledger technology of blockchain is deemed secure and decentralized, the confirmation of transactions is inefficient by design. Recently adopted, some distributed ledgers based on a directed acyclic graph validate transactions efficiently without the physically and environmentally costly building process of blocks (Lerner, Dagcoin: a crytocurrency without blocks (2015) [17]). However, centrally-controlled confirmation against the odds of multiple validation disqualifies that newer system as a decentralized-distributed ledger. In this regard, we introduce an innovative distributed ledger system by reconstructing a chain of antichains based on a given partially ordered pool of transactions. Each antichain contains distinct nodes whose approved transactions are recursively validated by subsequently augmenting nodes. The boxer node closes the box and keeps the hash of all transactions confirmed by the box-genesis node. Designation of boxers and box-geneses is conditionally randomized for decentralization. The boxes are serially concatenated with recursive confirmation without incurring the cost of box generation. Rewards are paid to the contributing nodes of the ecosystem whose trust is built on the doubly-secure protocol of confirmation. A value-preserving medium of payment is among numerous practical applications discussed herein.
An Improved Kernel Correlation Filter for Occlusion Target Tracking

In order to deal with the issues of stable tracking of moving targets under the occlusion situation, an improved kernel correlation filter is proposed in this paper. Color features can accommodate deformation, but can’t handle changes in illumination or the situation that the background is similar to the target. The local areas of the image are used to construct the target features in the histogram of oriented gradient (HOG), which is not easily affected by the illumination changes, but can’t adapt to the rapid deformation of the target. Therefore, the fusion features of HOG and color name (CN) are used in this paper. Due to the lack of occlusion discrimination mechanism, the cyclic structure in kernel correlation filter (KCF) is easy to introduce contaminated occluded samples, resulting in tracking failure. In this paper, the peak to sidelobe ratio (PSR) value of each frame is obtained and compared with the set threshold. When the occlusion is determined, a strategy for predicting the trajectory of the occluded target using a Kalman filter with the history frames is adopted. The proposed method is superior to KCF、 CSK、SCM、Struck、TLD and CT algorithms on the OTB-2013 benchmark dataset, especially under the occlusion situation.
An Efficient ASIC Implementation of Public Key Cryptography Algorithm SM2 Based on Module Arithmetic Logic Unit

SM2 public key cryptography, proposed by China, is widely used to ensure the security in communication. In this paper, based on the module arithmetic logic unit, SM3 unit, verify unit and XOR unit, we implemented a dual-field processor over SM2 public key cryptography, which can complete digital signature, verification, information encryption and decryption. In addition, the SM2 processor is described by Verilog HDL, and synthesized in CMOS 55nm process. Experimental results show that the SM2 processor runs at high frequency of 476 MHz with area of 245K gates. What's more, the processor requires only 0.56ms to compute a 256-bit point multiplication in GF(p), and 0.59ms in GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ).
Image Features Detection and Tracking for Image Based Target Augmented Reality Application

Image based target used in augmented reality application is not the same as traditional fiducial markers, data markers code and QR-codes in detection and tracking. The available features from the image detected and tracked are naturally found in the image itself. Image target commonly used in recognizing and augmented hard copy media and product for marketing campaigns, gaming, and visualizing products in the environment where the product was targeted to be used. HOwever, for mobile-based augmented reality application, image features detection and tracking still expose many challenges. This paper aims to review previous and current keypoint detection and tracking techniques for image target and address several issues face in mobile based augmented reality application. Most traditional interest-point detection and description methods are handcrafted. These methods focus on interest points in a generic setting and lack provision to adapt toward a specific dataset. Learning-based approaches can be a new solution to real-time application such mobile-based application. This project continuity work from our previous work done for mobile-based augmented reality coloring application [1].
Omnidirectional Video Quality Index Accounting for Judder

Human visual system (HVS) strongly responds to motion information. In particular, neurons in middle temporal (MT) area of the brain are sensitive to certain velocities. Thus distortions appeared on moving stimuli, as visually salient information, are visually very important and can significantly affect the quality of experience (QoE). Judder is a visual artifact that manifests as non-smooth motion when tracking a moving object on a digital display. In particular, the effect of judder becomes more significant in the wide field of view (FoV) displays where objects can be observed in a longer trajectory and duration. Head mounted display (HMD) maximizes FoV by enabling full 360° immersive experience and there has been rapid growth in the number of omnidirectional videos for HMDs. However, the impact of judder on visual QoE of the omnidirectional video has never been studied. In this paper, we first established a database of omnidirectional video sequences to study human responses on the visual effect of judder when watching videos on wide FoV HMDs. Two subjective tests were conducted to understand the impact of judder on video sequences compressed at different bit rates (i.e. different picture quality levels). Next, based on the subjective results, we proposed a novel QoE model considering the joint effect of judder, visual masking and picture quality. Experimental results indicate the effectiveness of the proposed model for accurate quality prediction of the videos with judder.
Feature-Aware Knowledge Tracing for Generation of Concept-Knowledge Reports in an Intelligent Tutoring System

In many Indian schools, a high student-teacher ratio makes it an uphill struggle for teachers to assess the knowledge of individual students and deficiencies in the students' understanding. Teachers should have a clear picture on what concepts each student has mastered, and which concepts the teacher needs to review in greater detail. This paper investigates the students' concept knowledge, based on the interaction of the students with an intelligent tutoring system. The Feature-Aware Student knowledge Tracing (FAST) algorithm was used, since the algorithm facilitates the separation of lesson-specific skills from concept knowledge. Data from 2400 first-grade students from 28 schools were used for the analysis. Findings include a moderate fit model and an easy interpretation of the model parameters.
Maximizing Robot Manipulability along Paths in Collision-free Motion Planning

A major task in motion planning is to find suitable movements with large manipulability, while collision-free operation must be guaranteed. This condition is increasingly important in the collaboration between humans and robots, as the capability of avoidance to humans or dynamic obstacles must be ensured anytime. For this purpose, paths in motion planning have to be optimized with respect to manipulability and distance to obstacles. Because with a large manipulability the robot has at any time, the possibility of evading due to the greater freedom of movement. Alternatively, the robot can be pushed away by using a Cartesian impedance control. To achieve this, we have developed a combined approach. First, we introduce a Rapidly-exploring Random Tree, which is extended and optimized by state costs for manipulability. Secondly, we perform an optimization using the STOMP method and Gaussian Mixture Models. With this dual approach we are able to find paths in narrow passages and simultaneously optimize the path in terms of manipulability.
Research Sharing-Oriented Functional Neuroimaging Named Entity Recognition

Named entity recognition is a basic and core task of information extraction on functional neuroimaging literatures. However, existing researches only focus on cognitive states and brain regions, and are far from effective research sharing. This paper proposes a research sharing-oriented approach for functional neuroimaging named entity recognition. The nine most representative entity categories were defined by analyzing the characteristics of task-based functional neuroimaging researches, and a multi-category named entity recognition method was designed based on BiLSTM-CNN. An experiment was performed on literatures obtained from the journal PLoS One. The experimental results show that the precision and recall rates of the proposed method can reach 94.50% and 95.56%, and are obviously superior to existing methods of functional neuroimaing named entity recognition.
PHT and KELM Based Face Recognition

Recognition of human face images is getting much attraction in pattern recognition since last few decades. Artificial intelligence and machine learning always tries to get more and more accurate for recognizing the face images. Only pixel based information of the face image can be helpful in recognizing the face images. This recognition rate can be increased if some feature of the face image is also added up with the pixel information of the face image. Based on this phenomenon, polar harmonic transform is utilized as the feature extraction technique for the feature based information. With this feature based information, kernel extreme learning machine (KELM) is utilized as the classification tool. It can be seen from the results obtained on the ORL, YALE and Georgia Tech face databases that more accurate results can be obtained using the feature based information.
Shearlet-Based Techniques for Histological Image Classification

In this paper, we propose a new feature representation for automatic classification of different tissue types in histopathological images. Our proposed approach is to combine different sets of features into a novel texture feature space. We compute all features in the Complex Shearlet domain, where we have not only examined the magnitude coefficients, but we also incorporate the relative phase (RP) coefficients to form the feature representation. Our Shearlet feature space consists of Haralick texture features, segmentation-based Fractal Texture Analysis, Local Binary patterns, and Local Oriented Statistic Information Booster. These descriptors are also used jointly to increase the classification accuracy of histopathological images when used with standard classifier. We demonstrate the effectiveness of our novel feature space with a support vector machine (SVM). The proposed features provide high classification performance competing with state-of-the-art systems equally on four publicly available datasets: Warwick-QU, Epistroma, BreaKHis, multi-class Kather.
Patient Activity Monitoring Based on Real-Time Location Data

We present our patient activity monitoring system with the aim of aiding informed clinical decision-making that encourages early and progressive ambulation (EPA). EPA has been shown to reduce the risk of many serious complications experienced by hospitalized patients. To achieve our goal, we employed a preexisting real-time location system (RTLS). We developed algorithms which leverage the data from an RTLS to determine the distance traveled, average velocity, time in zones-of-interest, and inactivity of patients to accurately inform clinicians of patient activities.
Development of a machine learning model for extracting image prominent colors

Abstract Although an image can consist of myriad different colors, due to the limitations of the human visual system and the mechanism of selective visual attention, only a limited number of them are prominent, that is, they stand out or are noticeable at first sight. In this article, a framework for building a model for extracting image prominent colors based on machine learning is presented. The model is learned on human‐extracted themes of prominent colors and uses numerous features, which were defined based on the properties of human visual system. For the purpose of this study, we constructed a database of images with their associated human‐extracted themes of prominent colors, which are open to the public and available to other researchers. The analysis of observers' data shows a high interobserver agreement on prominent color categories as well as high diversity of prominent colors. According to our results, the most influential factors on the perception of prominent colors are associated with color coverage (which should be adjusted with a saliency map), color properties—lightness and chroma, and diversity of colors. To the best of our knowledge, this is the first study on image prominent colors and first attempt to extract them with a model trained on the real data. The presented model has a high practical importance, since it can be used for extracting image colors in different scenarios, for example, for automatic color design, image categorization, as a descriptor in content‐based image retrieval, and image content analysis frameworks.
Twice fine‐tuning deep neural networks for paraphrase identification

In this Letter, the authors introduce a novel approach to learn representations for sentence-level paraphrase identification (PI) using BERT and ten natural language processing tasks. Their method trains an unsupervised model called BERT with two different tasks to detect whether two sentences are in paraphrase relation or not. Unlike conventional BERT, which fine tunes the target task such as PI to pre-trained BERT, twice fine-tuning deep neural networks first fine tune each task (e.g. general language understanding evaluation tasks, question answering, and paraphrase adversaries from word scrambling task) and second fine tune target PI task. As a result, the multi-fine-tuned BERT model outperformed the fine-tuned model only with Microsoft Research Paraphrase Corpus (MRPC), which is paraphrase data, except for one case of Stanford Sentiment Treebank - 2 (SST-2). Multi-task fine-tuning is a simple idea but experimentally powerful. Experiments show that fine-tuning just PI tasks to the BERT already gives enough performance, but additionally, fine-tuning similar tasks can affect performance (3.4% point absolute improvement) and be competitive with the state-of-the-art systems.
Using Ensembles for Class-Imbalance Problem to Predict Maintainability of Open Source Software

To facilitate software maintenance and save the maintenance cost, numerous machine learning (ML) techniques have been studied to predict the maintainability of software modules or classes. An abundant amount of effort has been put by the research community to develop software maintainability prediction (SMP) models by relating software metrics to the maintainability of modules or classes. When software classes demanding the high maintainability effort (HME) are less as compared to the low maintainability effort (LME) classes, the situation leads to imbalanced datasets for training the SMP models. The imbalanced class distribution in SMP datasets could be a dilemma for various ML techniques because, in the case of an imbalanced dataset, minority class instances are either misclassified by the ML techniques or get discarded as noise. The recent development in predictive modeling has ascertained that ensemble techniques can boost the performance of ML techniques by collating their predictions. Ensembles themselves do not solve the class-imbalance problem much. However, aggregation of ensemble techniques with the certain techniques to handle class-imbalance problem (e.g., data resampling) has led to several proposals in research. This paper evaluates the performance of ensembles for the class-imbalance in the domain of SMP. The ensembles for class-imbalance problem (ECIP) are the modification of ensembles which pre-process the imbalanced data using data resampling before the learning process. This study experimentally compares the performance of several ECIP using performance metrics Balance and g-Mean over eight Apache software datasets. The results of the study advocate that for imbalanced datasets, ECIP improves the performance of SMP models as compared to classic ensembles.
Multitarget Tracking Using One Time Step Lagged Delta-Generalized Labeled Multi-Bernoulli Smoothing

Aiming at improving the tracking performance of the delta-generalized labeled multi-Bernoulli (δ-GLMB) filter, we present a one time step lagged δ-GLMB smoother in this work, which also inherently outputs targets trajectories and differs from the Probability hypothesis density (PHD), Multi-Bernoulli (MB), and Cardinalized probability hypothesis density (CPHD) smoothers that are incapable of generating target trajectories directly. Under the standard multitarget measurement likelihood and state transition kernel, we show that a δ-GLMB distributed multitarget filtering density would result in a same distributed one time step lagged multitarget smoothing density. An efficient implementation of the proposed smoothing algorithm using the standard ranked assignment technique is also given. Numerical results show that the proposed smoother is capable of tracking a time-varying number of targets, in the presence of measurement origin uncertainty, target detection uncertainty, and clutter, and show that the proposed smoother outperforms the δ-GLMB filter, and the PHD, MB, and CPHD smoothers of the same time lag on both the estimates of target number and state and it also outperforms the LMB and the approximated δ-GLMB smoothers of the same time lag on target number estimate.
Generating attributed variability models for transfer learning

Modern software systems often provide configuration options for customizing of the system's functional and non-functional properties, such as response time and energy consumption. The valid configurations of a software system are commonly documented in a variability model. Supporting the optimization of a system's non-functional properties, variability models have been extended with attributes that represent the influence of one or multiple options on a property. The concrete values of attributes are typically determined only in a single environment (e.g., for a specific software version, a certain workload, and a specific hardware setup) and are applicable only for this context. Changing the environment, attribute values need to be updated. Instead of determining all attributes from scratch with new measurements, recent approaches rely on transfer learning to reduce the effort of obtaining new attribute values. However, the development and evaluation of new transfer-learning techniques requires extensive measurements by themselves, which often is prohibitively costly. To support research in this area, we propose an approach to synthesize realistic attributed variability models from a base model. This way, we can support research and validation of novel transfer-learning techniques for configurable software systems. We use a genetic algorithm to vary attribute values. Combined with a declarative objective function, we search a changed attributed variability model that keeps some key characteristics while mimicking realistic changes of individual attribute values. We demonstrate the applicability of our approach by replicating the evaluation of an existing transfer-learning technique.
Comparison Between Fuzzy Kernel C-Means, Fuzzy Kernel Possibilistic C-Means and Support Vector Machines in Soft Tissue Tumor Classification

Soft Tissue Tumor (STT) are cell growths, whose existence are not limited to the presence of tumors in soft tissues. Furthermore, they are classified into soft tissue and non-soft tissue tumor and early detection is important to determine the right course of treatment. This research, therefore, aims to compare fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines on Soft Tissue Tumor dataset, obtained from Nur Hidayah Hospital, Yogyakarta, Indonesia, consisting of 50 STT and 25 non-STT samples. The results conclude that fuzzy kernel c-means provides a better running time when using the parameter $$ \sigma = 0.05 $$. However, support vector machines, with the parameter $$ \sigma = 0.0001 $$ performs better than other methods in terms of accuracy, sensitivity, precision, and F1-Score.