Reviewerly/exHarmony huggingface dataset:
A light weight smartphone based human activity recognition system with high accuracy

With the pervasive use of smartphones, which contain numerous sensors, data for modeling human activity is readily available. Human activity recognition is an important area of research because it can be used in context-aware applications. It has significant influence in many other research areas and applications including healthcare, assisted living, personal fitness, and entertainment. There has been a widespread use of machine learning techniques in wearable and smartphone based human activity recognition. Despite being an active area of research for more than a decade, most of the existing approaches require extensive computation to extract feature, train model, and recognize activities. This study presents a computationally efficient smartphone based human activity recognizer, based on dynamical systems and chaos theory. A reconstructed phase space is formed from the accelerometer sensor data using time-delay embedding. A single accelerometer axis is used to reduce memory and computational complexity. A Gaussian mixture model is learned on the reconstructed phase space. A maximum likelihood classifier uses the Gaussian mixture model to classify ten different human activities and a baseline. One public and one collected dataset were used to validate the proposed approach. Data was collected from ten subjects. The public dataset contains data from 30 subjects. Out-of-sample experimental results show that the proposed approach is able to recognize human activities from smartphones’ one-axis raw accelerometer sensor data. The proposed approach achieved 100% accuracy for individual models across all activities and datasets. The proposed research requires 3 to 7 times less amount of data than the existing approaches to classify activities. It also requires 3 to 4 times less amount of time to build reconstructed phase space compare to time and frequency domain features. A comparative evaluation is also presented to compare proposed approach with the state-of-the-art works.
Characterizing large earthquakes before rupture is complete

Whether earthquakes of different sizes are distinguishable early in their rupture process is a subject of debate. Studies have shown that the frequency content of radiated seismic energy in the first seconds of earthquakes scales with magnitude, implying determinism. Other studies have shown that recordings of ground displacement from small to moderate-sized earthquakes are indistinguishable, implying a universal early rupture process. Regardless of how earthquakes start, events of different sizes must be distinguishable at some point. If that difference occurs before the rupture duration of the smaller event, this implies some level of determinism. We show through analysis of a database of source time functions and near-source displacement records that, after an initiation phase, ruptures of M7 to M9 earthquakes organize into a slip pulse, the kinematic properties of which scale with magnitude. Hence, early in the rupture process-after about 10 s-large and very large earthquakes can be distinguished.
Analytics, bias, and evidence: the quest for rational decision making

Evidence-based decision making seems both desirable and rational. New analytical tools for investigating 'big data' promise to provide additional unbiased evidence. Concurrently, technological advances for improving decision making reopen issues related to facts, biases, and beliefs. For many years, decision support systems and technologies have had the goal of enhancing the effectiveness of human decision-making processes, fostering rational thinking, and avoiding biases and errors. Recently, cognitive neuroscience research has highlighted issues of implicit cognition, physiological and naturalistic processes, and the impact of social cues as elements of human thought. Decision support builders and data scientists must consider a broader range of issues, including issues of knowledge and belief, social factors, and technical capabilities when developing cognitive, analytical, and decision support systems.
Sequence-to-sequence Bangla Sentence Generation with LSTM Recurrent Neural Networks

Sequence to sequence text generation is the most efficient approach for automatically converting the script of a word from a source sequence to a target sequence. Text generation is the application of natural language generation which is useful in sequence modeling like the machine translation, speech recognition, image captioning, language identification, video captioning and much more. In this paper we have discussed about Bangla text generation, using deep learning approach, Long Short-term Memory (LSTM), a special kind of RNN (Recurrent Neural Network). LSTM networks are suitable for analyzing sequences of text data and predicting the next word. LSTM could be a respectable solution if you want to predict the very next point of a given time sequence. In this article we proposed a artificial Bangla Text Generator with LSTM, which is very early for this language and also this model is validated with satisfactory accuracy rate.
Visual and User-Defined Smart Contract Designing System Based on Automatic Coding

Smart contract applications based on Ethereum blockchain have been widely used in many fields. They are developed by professional developers using specialized programming languages like solidity. It requires high requirements on knowledge of the specialized field and the proficiency in contract programming. Thus, it is hard for normal users to design a usable smart contract based on their own demands. Most current studies about smart contracts focus on the security of coding while lack of friendly tools for users to design the specialized templates of contracts coding. This paper provides a visual and user-defined smart contract designing systems. It makes the development of domain-specific smart contracts simpler and visualization for contract users. The system implements the domain-specific features extraction about the crawled data sets of smart contract programs by TF-IDF and K-means++ clustering algorithm. Then, it achieves the automatic generation of unified basic function codes by Char-RNN (improved by LSTM) based on the domain-specific features. The system adopts Google Blockly and links the generated codes with UI controls. Finally, it provides a set of specialized templates of basic functions for users to design smart contracts by the friendly interface. It reduces the difficulty and costs of contract programming. The paper offers a case study to design contracts by users. The designed contracts were validated on the existing system to implement the food trading and traders' credit evaluation. The experimental results show that the designed smart contracts achieve good integration with the existing system and they can be deployed and compiled successfully.
Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

Human hand gestures are a widely accepted form of real-time input for devices providing a human-machine interface. However, hand gestures have limitations in terms of effectively conveying the complexity and diversity of human intentions. This study attempted to address these limitations by proposing a multi-modal input device, based on the observation that each application program requires different user intentions (and demanding functions) and the machine already acknowledges the running application. When the running application changes, the same gesture now offers a new function required in the new application, and thus, we can greatly reduce the number and complexity of required hand gestures. As a simple wearable sensor, we employ one miniature wireless three-axis gyroscope, the data of which are processed by correlation analysis with normalized covariance for continuous gesture recognition. Recognition accuracy is improved by considering both gesture patterns and signal strength and by incorporating a learning mode. In our system, six unit hand gestures successfully provide most functions offered by multiple input devices. The characteristics of our approach are automatically adjusted by acknowledging the application programs or learning user preferences. In three application programs, the approach shows good accuracy (90-96%), which is very promising in terms of designing a unified solution. Furthermore, the accuracy reaches 100% as the users become more familiar with the system.
LAM: Locality affine-invariant feature matching

False match removal is a crucial and fundamental task in photogrammetry and computer vision. This paper proposes a robust and efficient mismatch-removal algorithm based on the concepts of local barycentric coordinate (LBC) and matching coordinate matrices (MCMs), called locality affine-invariant matching (LAM). LAM is suitable for both rigid and nonrigid image matching problems. We define a novel LBC system based on area ratios, which is invariant to local affine transformations. We also present the MCMs based on the coordinates of matches, whose degeneracy is able to indicate the correctness of correspondences. Our LAM method first builds a mathematical model based on the LBCs to extract good matches that preserve local neighborhood structures. Then, LAM constructs local MCMs using the extracted reliable correspondences and identifies the correctness for the remaining matches via minimizing the rank of the MCMs. LAM has linear space and linearithmic time complexities. Extensive experiments on both rigid and nonrigid real datasets demonstrate the power of the proposed method; i.e., LAM is more robust to complex transformations compared to other methods and is two orders of magnitude faster than RANSAC under low inlier rates. The source code of the proposed LAM method will be publicly available in http://www.escience.cn/people/lijiayuan/index.html.
SAR Image Registration Using an Improved SAR-SIFT Algorithm and Delaunay-Triangulation-Based Local Matching

Synthetic aperture radar (SAR) image registration is a challenging task in remote sensing because of the presence of significant intensity as well as geometric differences between the images. Moreover, the influence of multiplicative speckle noise in SAR images adversely affects the registration result. The synthetic aperture radar-scale invariant feature transform (SAR-SIFT) algorithm, specially developed for the SAR images, has been used popularly for the automatic registration of SAR images based on global feature matching. However, the algorithm suffers from lack of controllability of the number of extracted features and uneven distribution of the features. Moreover, global matching of the features reduces the number of correct matches. To address these problems, we propose an improved SAR-SIFT (I-SAR-SIFT) algorithm, which extracts features from the SAR images by considering three important factors of feature-based image registration: stability, distinctiveness, and distribution. Moreover, a novel approach is adapted to improve the distinctiveness of the feature-descriptor in the presence of speckle noise. In addition, a Delaunay-triangulation-based local matching algorithm is introduced to significantly improve the matching performance in SAR images. Experiments on different pairs of SAR images show that the proposed method gives better performance than the existing SAR image registration methods.
Hierarchical human-like strategy for aspect-level sentiment classification with sentiment linguistic knowledge and reinforcement learning

Aspect-level sentiment analysis is a crucial problem in fine-grained sentiment analysis, which aims to automatically predict the sentiment polarity of the specific aspect in its context. Although remarkable progress has been made by deep learning based methods, aspect-level sentiment classification in real-world remains a challenging task. The human reading cognition is rarely explored in sentiment classification, which however is able to improve the effectiveness of the sentiment classification by considering the process of reading comprehension and logical thinking. Motivated by the process of the human reading cognition that follows a hierarchical routine, we propose a novel Hierarchical Human-like strategy for Aspect-level Sentiment classification (HHAS). The model contains three major components, a sentiment-aware mutual attention module, an aspect-specific knowledge distillation module, and a reinforcement learning based re-reading module, which are consistent with the stages of the human reading cognitive process (i.e., pre-reading, active reading, and post-reading). To measure the effectiveness of HHAS, extensive experiments are conducted on three widely used datasets. Experimental results demonstrate that HHAS achieves impressive results and yields state-of-the-art results on the three datasets.
A Visual Dialog Augmented Interactive Recommender System

Traditional recommender systems rely on user feedback such as ratings or clicks to the items, to analyze the user interest and provide personalized recommendations. However, rating or click feedback are limited in that they do not exactly tell why users like or dislike an item. If a user does not like the recommendations and can not effectively express the reasons via rating and clicking, the feedback from the user may be very sparse. These limitations lead to inefficient model learning of the recommender system. To address these limitations, more effective user feedback to the recommendations should be designed, so that the system can effectively understand a user's preference and improve the recommendations over time. In this paper, we propose a novel dialog-based recommender system to interactively recommend a list of items with visual appearance. At each time, the user receives a list of recommended items with visual appearance. The user can point to some items and describe their feedback, such as the desired features in the items they want in natural language. With this natural language based feedback, the recommender system updates and provides another list of items. To model the user behaviors of viewing, commenting and clicking on a list of items, we propose a visual dialog augmented cascade model. To efficiently understand the user preference and learn the model, exploration should be encouraged to provide more diverse recommendations to quickly collect user feedback on more attributes of the items. We propose a variant of the cascading bandits, where the neural representations of the item images and user feedback in natural language are utilized. In a task of recommending a list of footwear, we show that our visual dialog augmented interactive recommender needs around 41.03% rounds of recommendations, compared to the traditional interactive recommender only relying on the user click behavior.
Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset

service level agreement (SLA) management is one of the key issues in cloud computing.The primary goal of a service provider is to minimize the risk of service violations, as these results in penalties in terms of both money and a decrease in trustworthiness.To avoid SLA violations, the service provider needs to predict the likelihood of violation for each SLO and its measurable characteristics (QoS parameters) and take immediate action to avoid violations occurring.There are several approaches discussed in the literature to predict service violation; however, none of these explores how a change in control parameters and the freshness of data impact prediction accuracy and result in the effective management of an SLA of the cloud service provider.The contribution of this paper is two-fold.First, we analyzed the accuracy of six widely used prediction algorithms-simple exponential smoothing, simple moving average, weighted moving average, Holt-Winter double exponential smoothing, extrapolation, and the autoregressive integrated moving average-by varying their individual control parameters.Each of the approaches is compared to 10 different datasets at different time intervals between 5 min and 4 weeks.Second, we analyzed the prediction accuracy of the simple exponential smoothing method by considering the freshness of a data; i.e., how the accuracy varies in the initial time period of prediction compared to later ones.To achieve this, we divided the cloud QoS dataset into sets of input values that range from 100 to 500 intervals in sets of 1-100, 1-200, 1-300, 1-400, and 1-500.From the analysis, we observed that different prediction methods behave differently based on the control parameter and the nature of the dataset.The analysis helps service providers choose a suitable prediction method with optimal control parameters so that they can obtain accurate prediction results to manage SLA intelligently and avoid violation penalties.
Evolutionary feature subsets selection based on interaction information for high dimensional imbalanced data classification

With the advent of technology in various scientific fields, high dimensional data are becoming abundant. A general approach to tackle the resulting challenges is to reduce data dimensionality through feature selection. Traditional feature selection approaches concentrate on selecting relevant features and ignoring irrelevant or redundant ones. However, most of these approaches neglect feature interactions. On the other hand, some datasets have imbalanced classes, which may result in biases towards the majority class. The main goal of this paper is to propose a novel feature selection method based on the interaction information (II) to provide higher level interaction analysis and improve the search procedure in the feature space. In this regard, an evolutionary feature subset selection algorithm based on interaction information is proposed, which consists of three stages. At the first stage, candidate features and candidate feature pairs are identified using traditional feature weighting approaches such as symmetric uncertainty (SU) and bivariate interaction information. In the second phase, candidate feature subsets are formed and evaluated using multivariate interaction information. Finally, the best candidate feature subsets are selected using dominant/dominated relationships. The proposed algorithm is compared with some other feature selection algorithms including mRMR, WJMI, IWFS, IGFS, DCSF, IWFS, K_OFSD, WFLNS, Information Gain and ReliefF in terms of the number of selected features, classification accuracy, F-measure and algorithm stability using three different classifiers, namely KNN, NB, and CART. The results justify the improvement of classification accuracy and the robustness of the proposed method in comparison with the other approaches.
A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&amp;ID)

In the Fourth Industrial Revolution, artificial intelligence technology and big data science are emerging rapidly. To apply these informational technologies to the engineering industries, it is essential to digitize the data that are currently archived in image or hard-copy format. For previously created design drawings, the consistency between the design products is reduced in the digitization process, and the accuracy and reliability of estimates of the equipment and materials by the digitized drawings are remarkably low. In this paper, we propose a method and system of automatically recognizing and extracting design information from imaged piping and instrumentation diagram (P&amp;ID) drawings and automatically generating digitized drawings based on the extracted data by using digital image processing techniques such as template matching and sliding window method. First, the symbols are recognized by template matching and extracted from the imaged P&amp;ID drawing and registered automatically in the database. Then, lines and text are recognized and extracted from in the imaged P&amp;ID drawing using the sliding window method and aspect ratio calculation, respectively. The extracted symbols for equipment and lines are associated with the attributes of the closest text and are stored in the database in neutral format. It is mapped with the predefined intelligent P&amp;ID information and transformed to commercial P&amp;ID tool formats with the associated information stored. As illustrated through the validation case studies, the intelligent digitized drawings generated by the above automatic conversion system, the consistency of the design product is maintained, and the problems experienced with the traditional and manual P&amp;ID input method by engineering companies, such as time consumption, missing items, and misspellings, are solved through the final fine-tune validation process.
Combining Simulation and Augmented Reality Methods for Enhanced Worker Assistance in Manual Assembly

Due to mass customization product variety increased steeply in the automotive industry, entailing the increment of worker’s cognitive load during manual assembly tasks. Although worker assistance methods for cognitive automation already exist, they proof insufficient in terms of usability and achieved time saving. Given the rising importance of simulation towards autonomous production planning, a novel approach is proposed using human simulation data in context of worker assistance methods to alleviate cognitive load during manual assembly tasks. Within this paper, a new concept for augmented reality-based worker assistance is presented. Additionally, a comparative user study (N=24) was conducted with conventional worker assistance methods to evaluate a prototypical implementation of the concept. The results illustrate the enhancing opportunity of the novel approach to save cognitive abilities and to induce performance improvements. The implementation provided stable information presentation during the entire experiment. However, with regard to the recentness, there has to be carried out further developments and research, concerning performance adaptions and investigations of the effectiveness.
Wireless MapReduce Distributed Computing

Motivated by mobile edge computing and wireless data centers, we study a wireless distributed computing framework where the distributed nodes exchange information over a wireless interference network. Our framework follows the structure of MapReduce. This framework consists of Map, Shuffle, and Reduce phases, where Map and Reduce are computation phases and Shuffle is a data transmission phase. In our setting, we assume that the transmission is operated over a wireless interference network. We demonstrate that, by duplicating the computation work at a cluster of distributed nodes in the Map phase, one can reduce the amount of transmission load required for the Shuffle phase. In this work, we characterize the fundamental tradeoff between computation load and communication load, under the assumption of one-shot linear schemes. The proposed scheme is based on side information cancellation and zero-forcing, and we prove that it is optimal in terms of computation-communication tradeoff. The proposed scheme outperforms the naive TDMA scheme with single node transmission at a time, as well as the coded TDMA scheme that allows coding across data, in terms of the computation-communication tradeoff.