Reviewerly/exHarmony huggingface dataset:
Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data

Exponential growth of large scale data industrial internet of things is evident due to the enormous deployment of IoT data acquisition devices. Detection of unusual patterns from large scale IoT data is important though challenging task. Recently, one-class support vector machines is extensively being used for anomaly detection. It tries to find an optimal hyperplane in high dimensional data that best separates the data from anomalies with maximum margin. However, the hinge loss of traditional one-class support vector machines is unbounded, which results in larger loss caused by outliers affecting its performance for anomaly detection. Furthermore, existing methods are computationally complex for larger data. In this paper, we present novel anomaly detection for large scale data by using randomized nonlinear features in support vector machines with bounded loss function rather than finding optimized support vectors with unbounded loss function. Extensive experimental evaluation on ten benchmark datasets shows the robustness of the proposed approach against outliers such as 0.8239, 0.7921 , 0.7501, 0.6711 , 0.6692, 0.4789 , 0.6462 , 0.6812 , 0.7271 and 0.7873 accuracy for Gas Sensor Array, Human Activity Recognition, Parkinson’s, Hepatitis, Breast Cancer, Blood Transfusion , Heart, ILPD and Wholesale Customers datasets respectively. In addition to this, introduction of randomized nonlinear feature helps to considerably decrease the computational complexity and space complexity from O(N3) to O(Bkn) and O(N2) to O(Bkn). Thus, very attractive for larger datasets.
mHealth Authentication Approach Based 3D Touchscreen and Microphone Sensors for Real-Time Remote Healthcare Monitoring System: Comprehensive Review, Open Issues and Methodological Aspects

Patient authentication acquires superior attention as a necessary security requirement in a remote health monitoring system. From a usability perspective, credential-based authentication techniques that use sensors, such as orientation, finger and camera, are not exceedingly well-convenient for medical applications. Moreover, patients could entrust credentials to someone else, thus violating the privacy policy. Therefore, further investigations are required to highlight the challenges and discuss the comprehensiveness of solutions. This study aims to discuss the open issues for mHealth authentication approaches derived from multilayer systematic reviews. Moreover, the study intends to discuss the design and develop and evaluate patient usability through a new sensor-based smartphone authentication technique. A new authentication methodology based on 3D touch and microphone sensors is proposed for real-time mHealth monitoring systems. The methodological aspects of the proposed approach are discussed, and three distinct and successive phases are presented. In the first phase, the adoption of two of the unexplored sensors as a new authentication technique is proposed. In the second phase, the development and data collection of the newly developed sensor are debated to put the methodology into perspective. Two techniques, namely 'microphone sensor (inaudible acoustic signal recorder)' and 'Android versions', are designed to represent the new mHealth authentication technique based on 3D touch and microphone sensors, respectively. In the third phase, the usability testing demonstration is discussed, and the main factor in the proposed usability (i.e. Remember) for test users is identified. The proposed methodology will be simulated and implemented in the future to serve as a guide for privacy preservation within authentication techniques, resulting in persistent and high-level usability within mHealth applications.
Effects of virtual reality on creative design performance and creative experiential learning

This study investigated the effects of teaching via virtual reality (VR) on creative design performance and creative experiential learning processes by using a pretest–post-test nonequivalent group design to sample 138 seventh graders from a junior high school in Taipei City. The main conclusions from this study are: (1) VR has significant positive effects on creative design processes, particularly in the design and planning, testing and revision and thoughts and appreciation stages. (2) VR has significant positive effects on creative design outcomes, with high- and low-level effects on creative design and functional appropriateness, respectively. (3) The learning process for VR and creative experiential learning shifts from concrete experiences to reflective observations, abstract conceptualization and, finally, innovative applications. Behaviors of reflective observation and innovative application are active and vital to the performance of creative processes and outcomes.
A regression unsupervised incremental learning algorithm for solar irradiance prediction

Intensity of solar irradiance directly affects solar power generation and this makes solar irradiance forecasting a vital process in energy management systems. Existing forecasting systems show positive solar irradiance forecasting performance, but most of them are not accurate in real-life especially when there are fast-moving clouds, causing highly fluctuating solar irradiance profile, which is difficult to predict. Moreover, the requirement to pre-train Artificial Intelligence-based forecasting system has made solar irradiance forecasting impractical as long-hour weather profiles need to be collected prior to deployment. This paper proposes a new artificial intelligent algorithm namely the Regression Enhanced Incremental Self-organising Neural Network (RE-SOINN) for accurate (even for highly fluctuating profile) and adaptive solar irradiance forecasting. This algorithm works by learning the time-series solar irradiance data incrementally and predicting it in real-time. It is novel in terms of enabling the learning of data from discrete (as in the conventional) to continuous using the regression method. The proposed algorithm further improves the prediction accuracy by decomposing the input data into two components (low and high frequency components) before feeding into the RE-SOINNs. Results showed that the proposed algorithm achieves higher accuracy compared to the Persistence model, Exponential Smoothing Model and Artificial Neural Networks.
Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping

In computational pathology, automated tissue phenotyping in cancer histology images is a fundamental tool for profiling tumor microenvironments. Current tissue phenotyping methods use features derived from image patches which may not carry biological significance. In this work, we propose a novel multiplex cellular community-based algorithm for tissue phenotyping integrating cell-level features within a graph-based hierarchical framework. We demonstrate that such integration offers better performance compared to prior deep learning and texture-based methods as well as to cellular community based methods using uniplex networks. To this end, we construct celllevel graphs using texture, alpha diversity and multi-resolution deep features. Using these graphs, we compute cellular connectivity features which are then employed for the construction of a patch-level multiplex network. Over this network, we compute multiplex cellular communities using a novel objective function. The proposed objective function computes a low-dimensional subspace from each cellular network and subsequently seeks a common low-dimensional subspace using the Grassmann manifold. We evaluate our proposed algorithm on three publicly available datasets for tissue phenotyping, demonstrating a significant improvement over existing state-of-the-art methods.
Group reduced kernel extreme learning machine for fault diagnosis of aircraft engine

The original kernel extreme learning machine (KELM) employs all training samples to construct hidden layer, thus avoiding the performance fluctuations caused by the ELM randomly assigning weights. However, excessive nodes will inevitably lead to structural redundancy, which hinders its application in systems with high real-time performance requirements but limited onboard storage and computing capacity. Considering the well interpretability of sparse learning, this study introduces the group sparse structure for KELM to resolve its limitation of structural redundancy. Specifically, the proposed novel method introduces a special norm to reformulate the dual optimization problem of KELM to realize group sparse structure in output weights. As a result, nodes with large weights can be selected as the significant nodes, while nodes with small weights will be regarded as the redundant nodes and neglected directly. In addition, we have also devised an alternating iterative optimization algorithm and deduced the complete proof of convergence to solve the non-smoothness optimization problem in proposed method. Then, the validity and feasibility of the proposed method are verified by extensive experiments on benchmark datasets. More importantly, tests of fault diagnosis for an aircraft engine show that the proposed approach can maintain the competitive recognition performance with much faster testing speed.
RoadText-1K: Text Detection &amp; Recognition Dataset for Driving Videos

Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new "RoadText-1K" dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection, recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/projects/cvit-projects/roadtext-1k.
Understanding Uncertainty in Self-adaptive Systems

Ensuring that systems achieve their goals under uncertainty is a key driver for self-adaptation. Nevertheless, the concept of uncertainty in self-adaptive systems (SAS) is still insufficiently understood. Although several taxonomies of uncertainty have been proposed, taxonomies alone cannot convey the SAS research community’s perception of uncertainty. To explore and to learn from this perception, we conducted a survey focused on the SAS ability to deal with unanticipated change and to model uncertainty, and on the major challenges that limit this ability. In this paper, we analyse the responses provided by the 51 participants in our survey. The insights gained from this analysis include the view—held by 71% of our participants—that SAS can be engineered to cope with unanticipated change, e.g., through evolving their actions, synthesising new actions, or using default actions to deal with such changes. To handle uncertainties that affect SAS models, the participants recommended the use of confidence intervals and probabilities for parametric uncertainty, and the use of multiple models with model averaging or selection for structural uncertainty. Notwithstanding this positive outlook, the provision of assurances for safety-critical SAS continues to pose major challenges according to our respondents. We detail these findings in the paper, in the hope that they will inspire valuable future research on self-adaptive systems.
Revisiting the Asymptotic Optimality of RRT

RRT* is one of the most widely used sampling-based algorithms for asymptotically-optimal motion planning. RRT* laid the foundations for optimality in motion planning as a whole, and inspired the development of numerous new algorithms in the field, many of which build upon RRT* itself. In this paper, we first identify a logical gap in the optimality proof of RRT*, which was developed by Karaman and Frazzoli (2011). Then, we present an alternative and mathematically-rigorous proof for asymptotic optimality. Our proof suggests that the connection radius used by RRT* should be increased from γ (log n/n) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/d</sup> to γ' (log n/n) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/(d+1)</sup> in order to account n n for the additional dimension of time that dictates the samples' ordering. Here γ, γ' are constants, and n, d are the number of samples and the dimension of the problem, respectively.
Mapping Research Trends of Blockchain Technology in Healthcare

Disruptive technology, blockchain is propelling a technological intervention in healthcare due to its unique features and advantages. The healthcare industry is migrating to Health 4.0. Therefore, peer-to-peer (P2P) transactions in a decentralized and distributed manner makes blockchain more lucrative to serve the needs of the healthcare industry of today. The revolutionary system, blockchain has been discussed in the field of healthcare over the past five years. Hence, a systematic investigation of the existing body of knowledge concerning blockchain research in healthcare is essential. The motivation of this study is to support further studies based on the current research trend analysis via graphical visualization and bibliographic material analysis. Therefore, this study maps the expansion of scientific and academic research conducted concerning blockchain that is relevant to healthcare by utilizing a bibliometric analytic method to understand the state of the art. Bibliometric statistics were utilized to analyze current scientific articles published in the Scopus database from 2016 to 2019. In addition, an overview of the publication trends over the first three months of 2020 was undertaken to understand the research trend for the current year so far. The study serves the purpose of mapping research development trends in healthcare. The outcome discovered some beneficial insights such as the yearly trend of publications, top listed authors, institutes, countries, and publishers from around the world. Moreover, this article assists scholars in developing a theoretical framework to provide a primary source of reference for further studies regarding blockchain technology in the healthcare domain.
Deep-Reinforcement-Learning-Based User Profile Perturbation for Privacy-Aware Recommendation

User profile perturbation protects privacy in the release of user profiles to receive recommendation services, in which the privacy budget as a privacy parameter can be controlled to effect a tradeoff between the recommendation quality and privacy protection against inference attacks. In this article, we propose a deep reinforcement learning (RL)-based user profile perturbation scheme for recommendation systems. This scheme applies differential privacy to protect user privacy and uses deep RL to choose the privacy budget against inference attackers. Based on an evaluated neural network (NN) and a target NN, this scheme enables a user device to optimize the privacy budget over time based on the sensitivity level of the clicked item, the similarities among the recommended items, and the estimated privacy loss. We provide an upper bound on the privacy protection performance of this scheme in the recommendation game and evaluate its computational complexity. Simulation results for a movie recommendation system show that this scheme increases the user privacy protection level for a given recommendation quality compared with benchmark schemes.
Efficient sparse collective communication and its application to accelerate distributed deep learning

Efficient collective communication is crucial to parallel-computing applications such as distributed training of large-scale recommendation systems and natural language processing models. Existing collective communication libraries focus on optimizing operations for dense inputs, resulting in transmissions of many zeros when inputs are sparse. This counters current trends that see increasing data sparsity in large models.
Understanding the automated parameter optimization on transfer learning for cross-project defect prediction

Data-driven defect prediction has become increasingly important in software engineering process. Since it is not uncommon that data from a software project is insufficient for training a reliable defect prediction model, transfer learning that borrows data/konwledge from other projects to facilitate the model building at the current project, namely cross-project defect prediction (CPDP), is naturally plausible. Most CPDP techniques involve two major steps, i.e., transfer learning and classification, each of which has at least one parameter to be tuned to achieve their optimal performance. This practice fits well with the purpose of automated parameter optimization. However, there is a lack of thorough understanding about what are the impacts of automated parameter optimization on various CPDP techniques. In this paper, we present the first empirical study that looks into such impacts on 62 CPDP techniques, 13 of which are chosen from the existing CPDP literature while the other 49 ones have not been explored before. We build defect prediction models over 20 real-world software projects that are of different scales and characteristics. Our findings demonstrate that: (1) Automated parameter optimization substantially improves the defect prediction performance of 77% CPDP techniques with a manageable computational cost. Thus more efforts on this aspect are required in future CPDP studies. (2) Transfer learning is of ultimate importance in CPDP. Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is 'not difficult' to find a better alternative by making a combination of existing transfer learning and classification techniques. This finding provides important insights about the future design of CPDP techniques.
KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition

This paper introduces a large-scale spontaneous speech corpus of Korean, named KsponSpeech. This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. This paper also presents the baseline performance of an end-to-end speech recognition model trained with KsponSpeech. In addition, we investigated the performance of standard end-to-end architectures and the number of sub-word units suitable for Korean. We investigated issues that should be considered in spontaneous speech recognition in Korean. KsponSpeech is publicly available on an open data hub site of the Korea government.
Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. To this aim, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue.
Overview of Machine Learning: Part 2

Deep learning has contributed to solving complex problems in science and engineering. This article provides the fundamental background required to understand and develop deep learning models for medical imaging applications. The authors review the main deep learning architectures such as multilayer perceptron, convolutional neural networks, autoencoders, recurrent neural networks, and generative adversarial neural networks. They also discuss the strategies for training deep learning models when the available datasets are imbalanced or of limited size and conclude with a discussion of the obstacles and challenges hindering the deployment of deep learning solutions in clinical settings.
Automated bank cheque verification using image processing and deep learning methods

Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate methodology for the processing of bank cheques with minimal human intervention. When it comes to the clearance of the bank cheques and monetary transactions, this should not only be reliable and robust but also save time which is one of the major factor for the countries having large population. In order to perform the task of cheque verification, we developed a tool which acquires the cheque leaflet key components, essential for the task of cheque clearance using image processing and deep learning methods. These components include the bank branch code, cheque number, legal as well as courtesy amount, account number, and signature patterns. our innovation aims at benefiting the banking system by re-innovating the other competent cheque-based monetary transaction system which requires automated system intervention. For this research, we used institute of development and research in banking technology (IDRBT) cheque dataset and deep learning based convolutional neural networks (CNN) which gave us an accuracy of 99.14% for handwritten numeric character recognition. It resulted in improved accuracy and precise assessment of the handwritten components of bank cheque. For machine printed script, we used MATLAB in-built OCR method and the accuracy achieved is satisfactory (97.7%) also for verification of Signature we have used Scale Invariant Feature Transform (SIFT) for extraction of features and Support Vector Machine (SVM) as classifier, the accuracy achieved for signature verification is 98.10%.