Reviewerly/exHarmony huggingface dataset:
Integrating Activity Recognition and Nursing Care Records

In this paper, we introduce a system of integrating activity recognition and collecting nursing care records at nursing care facilities as well as activity labels and sensors through smartphones, and describe experiments at a nursing care facility for 4 months. A system designed to be used even by staff not familiar with smartphones could collected enough number of data without losing but improving their workload for recording. For collected data, we revealed the nature of the collected data as for activities, care details, and timestamps, and considering them, we show a reference accuracy of recognition of nursing activity which is durable to time skewness, overlaps, and class imbalances. Moreover, we demonstrate the near future prediction to predict the next day's activities from the previous day's records which could be useful for proactive care management. The dataset collected is to be opened to the research community, and can be the utilized for activity recognition and data mining in care facilities.
Multi-Source Deep Transfer Neural Network Algorithm

Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In this paper, we propose a new multi-source deep transfer neural network algorithm, MultiDTNN, based on convolutional neural network and multi-source transfer learning. In MultiDTNN, joint probability distribution adaptation (JPDA) is used for reducing the mismatching between source and target domains to enhance features transferability of the source domain in deep neural networks. Then, the convolutional neural network is trained by utilizing the datasets of each source and target domain to obtain a set of classifiers. Finally, the designed selection strategy selects classifier with the smallest classification error on the target domain from the set to assemble the MultiDTNN framework. The effectiveness of the proposed MultiDTNN is verified by comparing it with other state-of-the-art deep transfer learning on three datasets.
Human activity recognition using earable device

Wearable sensors are monumental for human activity recognition. Researchers are continuously inventing new technology to detect human activity properly. Earable opens up interesting possibilities of monitoring personal scale behavioral activities. In this paper, we explore earables device 'eSense' multisensory stereo device for personal scale behavior analysis. We propose an activity recognition framework by exploiting eSense based multi-sensory device. It has a microphone, 6-axis inertial measurement unit, and a dual-mode Bluetooth. We use eSense accelerometer sensor data for detecting head and mouth related behavioral activities. We develop a data collection framework from the eSense through our smartphone application via Bluetooth. Then from the collected data, a few statistical features are computed to classify six personal scale activities related to head and neck movement such as speaking, eating, headshaking and head nodding, as well as, stay and walk. We aggregate the time series data intzo different action labels that summarize the user activity over a time interval. After, we train the data to induce a predictive model for activity recognition. We explore both machine learning and deep learning approach for data classification. For classification, we use the Support Vector Machine, Random Forest, and K-Nearest Neighbor and Convolutional Neural Network and achieve satisfactory recognition accuracy. The findings provide promising prospect for eSense for personal scale activity recognition in healthcare monitoring service. Based on our study, this kid of work is done for the first time with satisfactory findings.
Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image

Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and multiple templates in a sliding-window manner, such techniques are inefficient and costly. Recently, convolutional neural networks (CNNs) have successfully been used for object detection, and they have demonstrated considerably superior performance than that of traditional detection methods; however, this success has not been expanded to aerial images. To overcome such problems, we propose a detection model based on two CNNs. One of the CNNs is designed to propose many object-like regions that are generated from the feature maps of multi scales and hierarchies with the orientation information. Based on such a design, the positioning of small size objects becomes more accurate, and the generated regions with orientation information are more suitable for the objects arranged with arbitrary orientations. Furthermore, another CNN is designed for object recognition; it first extracts the features of each generated region and subsequently makes the final decisions. The results of the extensive experiments performed on the vehicle detection in aerial imagery (VEDAI) and overhead imagery research data set (OIRDS) datasets indicate that the proposed model performs well in terms of not only the detection accuracy but also the detection speed.
Gaussian parsimonious clustering models with covariates and a noise component

We consider model-based clustering methods for continuous, correlated data that account for external information available in the presence of mixed-type fixed covariates by proposing the MoEClust suite of models. These models allow different subsets of covariates to influence the component weights and/or component densities by modelling the parameters of the mixture as functions of the covariates. A familiar range of constrained eigen-decomposition parameterisations of the component covariance matrices are also accommodated. This paper thus addresses the equivalent aims of including covariates in Gaussian parsimonious clustering models and incorporating parsimonious covariance structures into all special cases of the Gaussian mixture of experts framework. The MoEClust models demonstrate significant improvement from both perspectives in applications to both univariate and multivariate data sets. Novel extensions to include a uniform noise component for capturing outliers and to address initialisation of the EM algorithm, model selection, and the visualisation of results are also proposed.
Multiobjective optimization for recognition of isolated handwritten Indic scripts

Identifying the most informative regions of character images is important for a robust handwritten character recognition system. If it is to be done in an efficient and cost-effective way, this task becomes more challenging. In this work, we have proposed a new multi-objective optimization framework to identify those local regions cost-effective way. We define three objective functions to be taken into account for this purpose: 1) recognition accuracy. 2) average recognition time per character image, and 3) redundancy of the local regions. More specifically, in this work, a modified opposition-based multiobjective Harmony Search algorithm has been proposed to select the local regions from handwritten character images based on their rankings in a three-dimensional pareto-front. Our work has been evaluated on four datasets - 1) isolated handwritten Bangla Basic characters, 2) isolated handwritten Bangla numerals, 3) English numerals, and 4) isolated handwritten Devanagari characters. The results show a significant decrease in the recognition costs and redundancy and an increase in the recognition accuracy obtained on all the datasets. Thus, our system provides a cost-effective approach towards isolated handwritten character recognition.
InCarAR

Advances in vehicle automation and the resulting change of the interior of cars lead to new challenges for user interface concepts. Augmented reality (AR) is a promising solution for the emerging design needs due to its diverse opportunities for user interaction and presenting information. This paper supports the development of novel AR applications. We describe a corresponding use case set consisting of 98 examples from a literature review and two focus groups. Based on these samples we present a design space for in-car AR applications. To demonstrate the benefit thereof, we show a fictional design process including our proposed design space to derive a custom AR system. This work supports designers and engineers by providing a systematic approach for integrating 3D AR interfaces in a vehicle, excluding windshields and windows.
Attention-based Sentiment Reasoner for aspect-based sentiment analysis

Abstract Aspect-based sentiment analysis (ABSA) is a powerful way of predicting the sentiment polarity of text in natural language processing. However, understanding human emotions and reasoning from text like a human continues to be a challenge. In this paper, we propose a model, named Attention-based Sentiment Reasoner (AS-Reasoner), to alleviate the problem of how to capture precise sentiment expressions in ABSA for reasoning. AS-Reasoner assigns importance degrees to different words in a sentence to capture key sentiment expressions towards a specific aspect, and transfers them into a sentiment sentence representation for reasoning in the next layer. To obtain appropriate importance degree values for different words in a sentence, two attention mechanisms we designed: intra attention and global attention. Specifically, intra attention captures the sentiment similarity between any two words in a sentence to compute weights and global attention computes weights by a global perspective. Experiments on all four English and four Chinese datasets show that the proposed model achieves state-of-the-art accuracy and macro-F1 results for aspect term level sentiment analysis and obtains the best accuracy for aspect category level sentiment analysis. The experimental results also indicate that AS-Reasoner is language-independent.
Formal Verification of Workflow Policies for Smart Contracts in Azure Blockchain

Ensuring correctness of smart contracts is paramount to ensuring trust in blockchain-based systems. This paper studies the safety and security of smart contracts in the Azure Blockchain Workbench, an enterprise Blockchain-as-a-Service offering from Microsoft. In particular, we formalize semantic conformance of smart contracts against a state machine workflow with access-control policy and propose an approach to reducing semantic conformance checking to safety verification using program instrumentation. We develop a new Solidity program verifier VeriSol that is based on translation to Boogie, and have applied it to analyze all application contracts shipped with the Azure Blockchain Workbench and found previously unknown bugs in these published smart contracts. After fixing these bugs, VeriSol was able to successfully perform full verification for all of these contracts.
An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation

The ACM Recommender Systems Challenge 2018 focused on the task of automatic music playlist continuation, which is a form of the more general task of sequential recommendation. Given a playlist of arbitrary length with some additional meta-data, the task was to recommend up to 500 tracks that fit the target characteristics of the original playlist. For the RecSys Challenge, Spotify released a dataset of one million user-generated playlists. Participants could compete in two tracks, i.e., main and creative tracks. Participants in the main track were only allowed to use the provided training set, however, in the creative track, the use of external public sources was permitted. In total, 113 teams submitted 1,228 runs to the main track; 33 teams submitted 239 runs to the creative track. The highest performing team in the main track achieved an R-precision of 0.2241, an NDCG of 0.3946, and an average number of recommended songs clicks of 1.784. In the creative track, an R-precision of 0.2233, an NDCG of 0.3939, and a click rate of 1.785 was obtained by the best team. This article provides an overview of the challenge, including motivation, task definition, dataset description, and evaluation. We further report and analyze the results obtained by the top-performing teams in each track and explore the approaches taken by the winners. We finally summarize our key findings, discuss generalizability of approaches and results to domains other than music, and list the open avenues and possible future directions in the area of automatic playlist continuation.
Smartphone Adoption in Mobile Learning Scenario

This paper attempts to explain the opportunities and risks of smartphones usage for students particularly in relation to mobile learning activities. This study deployed mixed methodology for achieving the purpose of the research. The quantitative method was used to examine the smartphone habits and mobile learning experience among students. It gathered 206 students at high school students that own smartphone. Then it was followed by Focus Group Discussion from the selected respondents to designed proposed recommendation of experiential learning through smartphone. The findings indicate that the smartphone activities of students keen in using it for social networking, instant messaging, and entertaining activities. Therefore, it is important to introduce a paradigm of student from smartphone for fun activities to fun based learning activities through experiential mobile learning.
Towards Federated Learning Approach to Determine Data Relevance in Big Data

In the past few years, data has proliferated to astronomical proportions; as a result, big data has become the driving force behind the growth of many machine learning innovations. However, the incessant generation of data in the information age poses a needle in the haystack problem, where it has become challenging to determine useful data from a heap of irrelevant ones. This has resulted in a quality over quantity issue in data science where a lot of data is being generated, but the majority of it is irrelevant. Furthermore, most of the data and the resources needed to effectively train machine learning models are owned by major tech companies, resulting in a centralization problem. As such, federated learning seeks to transform how machine learning models are trained by adopting a distributed machine learning approach. Another promising technology is the blockchain, whose immutable nature ensures data integrity. By combining the blockchain's trust mechanism and federated learning's ability to disrupt data centralization, we propose an approach that determines relevant data and stores the data in a decentralized manner.
Visualization of Deep Reinforcement Learning using Grad-CAM: How AI Plays Atari Games?

Deep Reinforcement Learning (DRL) allows agents to learn strategies to solve complex tasks. It has been applied to solve various problems such as natural language processing, games, etc. However, it is still difficult to apply DRL to certain real-world problems because each action is not predictable, and we cannot know why the results are coming out. For this reason, a technology called eXplainable Artificial Intelligence (XAI) has been recently developed. As this technology shows a visualization of the AI process, people can easily understand the results of AI. In this paper, we proposed to use Grad-CAM, one of the XAI techniques, when we visualize the behaviors of AI players trained by DRL. Our experimental results show which part of the input state is focused on when one well-trained agent takes action.
Multi-agent architecture for information retrieval and intelligent monitoring by UAVs in known environments affected by catastrophes

The consequences of natural or man-made catastrophes can be devastating. To minimize its impact, it is crucial to carry out a rapid analysis of the affected environment in the moments after they occur, especially from the perspective of alert notification or crisis management. In this context, the use of UAVs, understood as the technological basis on which intelligent systems capable of providing support to rescue teams is built, has positively contributed to face this challenge. In this article the design of a multi-agent architecture which enables the deployment of systems made up of intelligent agents that can monitor environments affected by a catastrophe and provide support to human staff in the decision-making process is proposed. These environments, known in advance, are characterized through a set of points of interests that are critical from the point of view of aerial surveillance and monitoring. To conduct an intelligent information analysis, a formal model of normality analysis is employed, which makes possible the definition of surveillance components. These represent the knowledge bases of the agents responsible for monitoring environments. Likewise, the architecture envisages communication and cooperation mechanisms between the different agents, as the basis for fusing information to assess the overall level of risk of the monitored environment. A case study is presented in which the spread of toxic smoke in an industrial complex which has just suffered a hypothetical earthquake is monitored.
Estimation of soil pH using PXRF spectrometry and Vis-NIR spectroscopy for rapid environmental risk assessment of soil heavy metals

Environmental risk of heavy metals (HMs) in soil is commonly assessed by the different risk screening values of HMs under different pH based on soil environmental quality standards. To explore and establish a reliable, rapid and cost-effective method for detailed soil environmental quality survey with high-density sampling in the large-scale area is of significance for theoretical and practical research. In present study, using data from Yunnan Province, China, rapid analysis of soil HMs were conducted via portable X-ray fluorescence (PXRF) spectrometry, and that of soil pH was estimated by applying PXRF and visible near-infrared reflectance (Vis-NIR) spectroscopy data to partial least-squares regression (PLSR) and support vector machine regression (SVMR). Then we compared soil HM contamination grades calculated by conventional laboratory analysis data with those of rapid analysis data. It was found that soil HMs (i.e., As, Pb, Cu, and Zn) were successfully estimated by PXRF with high coefficient of determination (R2) above 0.97 (P < 0.001). SVMR with fused sensor dataset (here PXRF and Vis-NIR) provided the best predictive model for soil pH estimation (R2 = 0.86; the ratio of performance to deviation (RPD) = 2.21; the ratio of performance to interquartile distance (RPIQ) = 3.09). The Kappa coefficient of the classification was 0.91, a very high-level consistency between the assessment of soil HM contamination grades calculated by rapid analysis data and that of conventional laboratory analysis data. Therefore, our study suggested a promising method to rapidly detect soil HM contamination under different pH intervals, which would considerably reduce the financial burden of detailed soil HM survey great sampling number in large-scale areas.
The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English

In this paper we present datasets of Facebook comment threads to mainstream media posts in Slovene and English developed inside the Slovene national project FRENK (the acronym FRENK stands for "FRENK - Raziskave Elektronske Nespodobne Komunikacije" (engl. "Research on Electronic Inappropriate Communication")) which cover two topics, migrants and LGBT, and are manually annotated for different types of socially unacceptable discourse (SUD). The main advantages of these datasets compared to the existing ones are identical sampling procedures, producing comparable data across languages and an annotation schema that takes into account six types of SUD and five targets at which SUD is directed. We describe the sampling and annotation procedures, and analyze the annotation distributions and inter-annotator agreements. We consider this dataset to be an important milestone in understanding and combating SUD for both languages.
sEnDec: An Improved Image to Image CNN for Foreground Localization

Although it is not immediately intuitive that Deep Convolutional Neural Networks (DCNNs) can yield adequate feature representation for a Foreground Localization (FGL) task, recent architectural and algorithmic advancements in Deep Learning (DL) have shown that the DCNNs have become forefront methodology for this pixel-level classification problem. In FGL, the DCNNs face an inherent trade-off between moving objects, i.e., the foreground (FG) and the non-static background (BG) scenes, through learning from local- and global-level features. Driven by the latest success of the innovative structures for image classification and semantic segmentation, this work introduces a novel architecture, called Slow Encoder-Decoder (sEnDec) that aims to improve the learning capacity of a traditional image-to-image DCNN. The proposed model subsumes two subnets for contraction (encoding) and expansion (decoding), wherein both phases, it employs an intermediate feature map up-sampling and residual connections. In this way, the lost structural details due to spatial subsampling are recovered. It helps to get a more delineated FG region. The experimental study is carried out with two variants of the proposed model: one with strided convolution (conv) and the other with max pooling for spatial subsampling. A comparative analysis on sixteen benchmark video sequences, including baseline, dynamic background, camera jitter, shadow effects, intermittent object motion, night videos, and bad weather show that the proposed sEnDec model performs very competitively against the prior- and state-of-the-art approaches.
How persuasive is a phishing email? A phishing game for phishing awareness

Context:In the current era of digital technology, social engineers are using various tactics to undermine human weaknesses. Social Engineers target human psychology to achieve their target(s) which are in the form of data, account details, or IT devices etc. According to our research, one of the fi rst methods social engineers used to target victims is Phishing/Spear Phishing. Objective:The objective of this study is to utilize serious game to: i) educate players regarding phishing and spear-phishing attacks; ii) make aware and educate players regarding dangers associated with excessive online information disclosure. Method:In order to address the objectives we have: i) performed an in-depth literature review to extract insights related to social engineering, phishing, game design, learning functions, human interaction, and game-based learning etc; ii) proposed and aligned the game design with social engineering ontology concepts; iii) performed an empirical evaluation to evaluate the effectiveness of the designed board game. Conclusion:From this research study, we conclude that: i) PhishI game is useful in educating players regarding excessive online information disclosure and phishing awareness; ii) game-based learning is an effective method for inculcating and general cyber-related awareness in players.