Reviewerly/exHarmony huggingface dataset:
Hierarchical Convolutional Neural Networks Information Fusion for Activity Source Detection in Smart Buildings

Detecting the source of activities in buildings is of interest to enable the potentials for Smart Buildings in industrial facilities, public infrastructure, hospitals, and commercial or residential buildings. This problem has been addressed using intrusive techniques, including vision-based methods. Recent studies have instead demonstrated that unobtrusive approaches could provide comparable results to the intrusive methods. These methods have the capability to identify and classify atypical floor vibration signals to specific sources, e.g., human fall detection or rotary machines fault detection. Most successful applications of vibration-based monitoring use traditional feature learning methods. These methods extract the temporal and spectral features from the vibration signals, and human experts are involved in selecting the features for classification. However, they typically require a labor-intensive process, which may add uncertainty and bias to the results. Several promising alternatives to these approaches have recently been proposed which take advantage of the time-dependent frequency spectrum of the vibration signals (i.e., spectogram). These representations are then fed into a deep convolutional neural network (CNN) architecture for image-like classification, training an accurate model. However, activty source detection in buildings requires the identification of numerous possible sources (classes), which may lead to low accuracy and confidence in the results. Thus, in this study, we incorporate prior knowledge of a target application, called a hierarchical structure of activity sources (classes). We develop a hierarchical CNN-based technique, which has a relatively easy setup, is modifiable, and has an extendable structure, to use floor vibration signatures to detect the source of activities in a building. We validate the method on a recently released benchmark dataset for human activity identification by implementing a flat numerous-class CNN-based approach, and the proposed hierarchical CNN-based approach. The results indicate that the hierarchical CNN-based approach outperforms other methods.
On Azimuth Ambiguities Suppression for Short-Baseline Along-Track Interferometry: the Stereoid Case

Ambiguities in short-baseline ATI interferometry need to be treated not as noise that lowers the coherence, but as a source of bias. A mathematical formulation of the interferometric ambiguity model is given, and an approach to correct ambiguities is proposed and illustrated with simulation results.
A Proposed Gradient Tree Boosting with Different Loss Function in Crime Forecasting and Analysis

Gradient tree boosting (GTB) is a newly emerging artificial intelligence technique in crime forecasting. GTB is a stage-wise additive framework that adopts numerical optimisation methods to minimise the loss function of the predictive model which later enhances it predictive capabilities. The applied loss function plays critical roles that determine GTB predictive capabilities and performance. GTB uses the least square function as its standard loss function. Motivated by this limitation, the study is conducted to observe and identify a potential replacement for the current loss function in GTB by applying a different existing standard mathematical function. In this study, the crime models are developed based on GTB with a different loss function to compare its forecasting performance. From this case study, it is found that among the tested loss functions, the least absolute deviation function outperforms other loss functions including the GTB standard least square loss function in all developed crime models.
A hybrid approach to inferring the Internet of Things for complex activity recognition

Abstract With the rapid development and large-scale uptake of the Internet of Things, smart home is evolving from a vision towards a realistically viable solution for assisted living. Activity recognition is one of the fundamental tasks in order to provide accurate and timely assistance and service. As daily living scenarios are full of similar activities, missing data, and noise, inferring complex activities using knowledge-driven reasoning algorithms suffers from several drawbacks, e.g., real-time raw sensor data segmentation, poor generalization, higher computational complexity, and scalability. To address these problems, this paper proposes a hybrid approach to complex daily activity recognition by merging the first-order logic and probability graphic modeling. Specifically, we develop a novel “Markov logic network” combining data-driven multi-feature and simplified rule-based modeling and inference, thus enabling and supporting the applicability and robustness of daily activity recognition. To evaluate the approach and associated methods, we design a testing scenario with a number of similar activity groups, missing data, or disturbance test datasets in a multi-modeling sensor scene. Initial results show our approach outperforms the traditional approach with a better accuracy in the situations of similar activities with missing data and noise disturbance. Experiments are also conducted to compare the Gibbs sampling and MC-SAT sampling algorithms for Markov logic network, and the results show that the Gibbs is better in our experimental settings.
The Development of Digital Board Game to Introduce Indonesian Wildlife using AR Technology and NFC

The low level of understanding of the community about the diversity of animals in Indonesia has led to rampant poaching. This is due to a lack of ownership and love for Indonesian animals. An interactive media is needed to provide an understanding of the diversity of Indonesian special animals. Digital board games are one of the most effective interactive media for influencing the community. This is because digital board games have high interactivity and visualization of imagination so that the information they want to convey can be conveyed effectively. This research is about making a digital board game to introduce Indonesian wildlife by utilizing Near Field Communication (NFC) and Augmented Reality (AR) technology.
Cyber-Analytics: Identifying Discriminants of Data Breaches.

In this study, the relationship between data breach characteristics and the number of individuals affected by these violations was considered. Data were acquired from the Department of Health and Human Services breach reporting database and analyzed using SPSS. Regression analyses revealed that the hacking/IT incident breach type and network server breach location were the most significant predictors of the number of individuals affected; however, they were not predictive when combined. Moreover, network server location and unauthorized access/disclosure breach type were predictive when combined. Additional analyses of variance revealed that covered entity type and business associate presence were significant predictors, while the geographic region of a breach occurrence was insignificant. The results of this study revealed several associations between healthcare breach characteristics and the number of individuals affected, suggesting that more individuals are affected in hacking/IT incidents and network server breaches independently and that network server breach location and unauthorized access/disclosure breach type were predictive in combination.
A Hybrid Advanced PSO-Neural Network System

In this paper, a combination of Advanced Particle Swarm Optimization (APSO) and Neural Network are presented to compensate the drawbacks of both the techniques and utilize the strong attributes to form a hybrid system called Hybrid Advance Particle Swarm Optimization-Neural Network System (HAPSONNS). APSO is used for the training of the neural network. In the initial phases of the search, PSO has swift convergence for global optimum, but later it suffers from slow convergence around the global optimum position. On the contrary, the gradient method attains prior to convergence around the global optimum point, therefore, attaining better accuracy in terms of convergence. This paper elucidates the usage of APSO applied to feedforward neural network to improve the classification accuracy of the network and also decreases the network training time.
MICE-DA: A MICE method with Data Augmentation for missing data imputation in IEEE ICHI 2019 DACMI Challenge

The widespread use of Electronic health records (EHR) systems in clinical practice offers valuable data sources for health care and population research in recent years. EHR data are often presented with long follow-up, and wide coverage on types of diseases, population groups, laboratory tests and other detailed clinical inputs. They provide great opportunities for researchers to answer many clinical questions in real world settings which cannot be easily addressed in traditional clinical trials [1]. However, EHR system is not created primarily for research purpose, and some important clinical information can be missing or collected irregularly due to administrative reasons or the nature of patient conditions [2]. ICHI Data Analytics Challenge on Missing data Imputation (DACMI) presented such a real-world example derived from an intensive care unit (ICU) EHR database in US [3]. The dataset consists of irregularly collected test results for 13 commonly measured analytes (clinical laboratory tests), participants are asked to impute missing data points which are either masked by the organizer for evaluation or missing from the original data.
The Influence of Interactive and Non-interactive E-Book on the Learning Effectiveness of High and Low Achievement Nursing Students

This study aims to explore the influence of interactive and non-interactive e-books used in nursing education courses on the achievement of high achievement and low achievement students. This study used a quasi-experimental study design with two classes of students as experimental and control groups. Both two groups use the same learning materials. The results of the study show that both e-books have a positive impact on learning outcomes, but interactive e-books are more effective, and learning through interactive e-books is more effective for low-achieving students than for high-achieving students. It also means that this learning method can effectively narrow the gap between high and low students.
A Proposal of a Students’ Voluntary Growth System of Generic Skills by Objective Evaluation Methods

A continuous survey of students’ Generic Skills (GSs) has been conducted since academic year of 2014 at National Institute of Technology, Sendai College (Sendai KOSEN). Five years have passed since the survey started, and the survey from students’ admission to their graduation at Sendai KOSEN has been completed. Our students’ growth characteristics of GSs in the educational curriculum at Sendai KOSEN became evident from this five-year continuous survey.. Based on the survey results, we propose a “students’ voluntary growth system of GSs.” Using this proposed method, students can be expected to enter a cycle of improving their own generic skills voluntarily and effectively.
Comparative Study between Web Services Technologies: REST and WSDL

The field of web services became one of the hottest points of discussion during the last decade, along with the evolving web technology. The possibility of having diverse systems connected to each other has been enormously and successfully achieved with the aid of web services. Despite the fact that web services are built over the concept of web programming, developers must have in mind that there are substantial comparison aspects relevant to creating and hosting a web service. Knowing the procedure of constructing a web service, as well as its architecture and standards being used is highly required and important. This paper is intended to provide web programmers some of the basic concepts regarding two of the most commonly used web services nowadays. These two web services are WSDL (Web Service Description Language) and REST (Representational State Transition). It firstly provides the architecture, standards, and steps on how to create them. The paper then concludes with some findings and observations.
Copula Multi-label Learning

A formidable challenge in multi-label learning is to model the interdependencies between labels and features. Unfortunately, the statistical properties of existing multi-label dependency modelings are still not well understood. Copulas are a powerful tool for modeling dependence of multivariate data, and achieve great success in a wide range of applications, such as finance, econometrics and systems neuroscience. This inspires us to develop a novel copula multi-label learning paradigm for modeling label and feature dependencies. The copula based paradigm enables to reveal new statistical insights in multi-label learning. In particular, the paper first leverages the kernel trick to construct continuous distribution in the output space, and then estimates our proposed model semiparametrically where the copula is modeled parametrically, while the marginal distributions are modeled nonparametrically. Theoretically, we show that our estimator is an unbiased and consistent estimator and follows asymptotically a normal distribution. Moreover, we bound the mean squared error of estimator. The experimental results from various domains validate the superiority of our proposed approach.
On degree-d zero-sum sets of full rank

A set $S \subseteq {{\mathbb {F}}_{2}^{n}}$ is called degree-d zero-sum if the sum ${\sum }_{s \in S} f(s)$ vanishes for all n-bit Boolean functions of algebraic degree at most d. Those sets correspond to the supports of the n-bit Boolean functions of degree at most n − d − 1. We prove some results on the existence of degree-d zero-sum sets of full rank, i.e., those that contain n linearly independent elements, and show relations to degree-1 annihilator spaces of Boolean functions and semi-orthogonal matrices. We are particularly interested in the smallest of such sets and prove bounds on the minimum number of elements in a degree-d zero-sum set of rank n. The motivation for studying those objects comes from the fact that degree-d zero-sum sets of full rank can be used to build linear mappings that preserve special kinds of nonlinear invariants, similar to those obtained from orthogonal matrices and exploited by Todo, Leander and Sasaki for breaking the block ciphers Midori, Scream and iScream.
Efficient, Effective, and Realistic Website Fingerprinting Mitigation

Website fingerprinting attacks have been shown to be able to predict the website visited even if the network connection is encrypted and anonymized.These attacks have achieved accuracies as high as 92%.Mitigations to these attacks are using cover/decoy network traffic to add noise, padding to ensure all the network packets are the same size, and introducing network delays to confuse an adversary.Although these mitigations have been shown to be effective, reducing the accuracy to 10%, the overhead is high.The latency overhead is above 100% and the bandwidth overhead is at least 30%.We introduce a new realistic cover traffic algorithm, based on a user's previous network traffic, to mitigate website fingerprinting attacks.In simulations, our algorithm reduces the accuracy of attacks to 14% with zero latency overhead and about 20% bandwidth overhead.In real-world experiments, our algorithms reduces the accuracy of attacks to 16% with only 20% bandwidth overhead.
Imbalanced Dataset Problem in Sentiment Analysis

In this study, the problems caused by unbalanced data sets on sentiment analysis are discussed and the situation of balancing the data sets of the methods based on sample incrementation and sample reduction analysed to reach more reliable classification results and the positive and negative effects of these methods are revealed. For this purpose, the effects of ROS, SMOTE, RUS and NM algorithms on the logistic regression classifier were analyzed by using word-based N-gram structures on three different sentiment analysis datasets containing both Turkish and English texts. As a result, it has been found out that the sample increase methods (ROS, SMOTE) increase the classifier performance values and the sample reduction methods (RUS and NM) decrease the performance values on the data sets and the results are explained in detail.
Decision support systems for scheduling tasks in Biosystems Engineering

Modern decision support systems have many applications, including assistance in scheduling tasks. Biosystems engineering combines engineering sciences and physical sciences in order to understand and improve biological systems in agriculture, food production, environment, etc. The work reviews the decision support systems in the aspect of scheduling tasks in the field of biosystems engineering. The analysis was based on documents (articles and proceedings paper) indexed in the Web of Science Core Collection (WoS-CC) database from 1945-2018. The search has been limited to the category of WoS-CC related to agriculture, water resources, food processing, horticulture and forestry. The main research topics, areas of application and methods used were determined. In the analyzed documents, task scheduling was mainly used in irrigation and harvest scheduling. Simple and advanced optimization tools were used.
Smart Bag Prototype with Apriori Algorithm

Today's technological developments can help all human work, which can initially be done in the usual way, if added a technology in it, then the work can be easier. One of them is when we travel and carry bags, we have to prepare whatever items we have to carry, and carry the items in the bag. Sometimes we forget the things we have to carry and from that problem, we have to go home first to get the items that must be taken and that can take a lot of time. To overcome this problem, the author has built a prototype smart bag. The prototype can minimize lags to carry their carry-on items. This smart bag is built with microcontroller, RFID, firebase, and android applications. The a priori algorithm determines the combination of the luggage on the smart bag, if any luggage is left behind, the android application notifies.
Research on Multi-Channel Semantic Fusion Classification Model

In this work, we propose a multi-channel semantic fusion convolutional neural network (SFCNN) to solve the problem of emotional ambiguity caused by the change of contextual order in sentiment classification task. Firstly, the emotional tendency weights are evaluated on the text word vector through the improved emotional tendency attention mechanism. Secondly, the multi-channel semantic fusion layer is leveraged to combine deep semantic fusion of sentences with contextual order to generate deep semantic vectors, which are learned by CNN to extract high-level semantic features. Finally, the improved adaptive learning rate gradient descent algorithm is employed to optimize the model parameters, and completes the sentiment classification task. Three datasets are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the SFCNN model has the high steady-state precision and generalization performance.
Left-corner parsing of minimalist grammars

Much recent research in experimental psycholinguistics revolves around the resolution of long-distance dependencies, and the manner in which the human sentence processor “retrieves’” elements from earlier in a sentence that must be related in some way to the material currently being processed. At present there is no obvious way for the issues raised by this research to be framed in terms of an MG parser. Stabler’s 2013 top-down MG parser does not involve any corresponding notion of “retrieval’”: it requires that a phrase’s position in the derivation tree be completely identified before the phrase can be scanned, which means that a filler cannot be scanned without committing to a particular location for its corresponding gap. This chapter attempts to develop a parsing algorithm that is inspired by Stabler, but which allows a sentence-initial filler to be scanned immediately while delaying the choice of corresponding gap position.
Human Performance in Google’s Two-factor Authentication Setup Process

Due to the proliferation of online services such as social networking, online banking, and cloud computing, more personal data are potentially exposed than ever before. Efforts such as two factor authentication (2FA) aim to make these services more secure; however, existing research efforts suggest this may come at the expense of usability. We conducted a usability evaluation of Google's 2FA setup process that confirms this concern, and extends previous efforts by identifying several problem areas and specific usability issues that affect human performance in 2FA setup processes. Future research should include more diverse populations but also continue efforts in improving the usability of 2FA setup processes. This will hopefully lead to increased adoption of 2FA systems.
Research on Application of Reptilian Technology in Tax Management Achievements

Abstract In the era of big data and internet, huge network data increases the information asymmetry of tax collection and management, such as the aggravation of information asymmetry caused by new tax sources, single access to information, imperfect tax information sharing platform, and tax risk deficiencies, which brings about the problem of tax collection and management. But web crawler technology can automatically crawl on the Internet and search tax-related data, which greatly improves the ability of tax inspectors, the efficiency of tax management and the quality of tax service. There are some shortcomings in reptilian technology to improve.
Sentiment Polarity Detection in Azerbaijani Social News Articles

Text classification field of natural language processing has been experiencing remarkable growth in recent years.Especially, sentiment analysis has received a considerable attention from both industry and research community.However, only a few research examples exist for Azerbaijani language.The main objective of this research is to apply various machine learning algorithms for determining the sentiment of news articles in Azerbaijani language.Approximately, 30.000 social news articles have been collected from online news sites and labeled manually as negative or positive according to their sentiment categories.Initially, text preprocessing was implemented to data in order to eliminate the noise.Secondly, to convert text to a more machine-readable form, BOW (bag of words) model has been applied.More specifically, two methodologies of BOW model, which are tf-idf and frequency-based model have been used as vectorization methods.Additionally, SVM, Random Forest, and Naive Bayes algorithms have been applied as the classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed.Experimental results indicate that SVM outperforms other classification algorithms.
Improving EDBTC Image Quality Using Stationary and Decimated Wavelet Transform

This paper presents a new technique for improving the quality of Error Difussion Block Truncation Coding (EDBTC) decoded image. The proposed method exploits the usability of wavelet transform on decomposing the EDBTC decoded image into low and high frequency subbands. This new technique employs the two dimensional Discrete Wavelet Transform (DWT) and two dimensional Stationary Wavelet Transform (SWT). This scheme considers the EDBTC decoded image as noisy image in which the occurence of noise can be minimized by modifying the wavelet high frequency. As documented in experimental results, the proposed method yields promising result for the EDBTC image reconstruction.