Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Previewing Gestures and the Gesture-and-Effect Model: Experimentation with Responsive Visual Feedback for New and Unlearned Interactions

Multi-touch gestures embedded in touch-based interfaces and devices present challenges. It can be difficult for users to discover different gestures and understand their effects. The study reported in this paper hypothesises that presenting automatic visual prompts, termed self-previewing gestures (SPGs) in this research, that depict touch and preview gesture execution will mitigate the problems that users encounter with unfamiliar gestural interfaces. A within-subjects experiment (n=45) is reported in which an iPad application with two alternative gestural designs, and five alternative user interface versions (one industry, two research baselines, and two SPGs) was created with the purpose of making the available gestures evident. A rating system that adapts Norman's Theory of Action to touch-based interactions by making use of known principles within interaction design (perceptible affordances, feedforward and feedback) is proposed. The system was used to assess participants' perceptions of and interactions with the SPGs, and the results revealed positive and negative aspects of designs and UI versions.
A decision support system based on rapid progression rules to enhance baseline evaluation of Parkinson's disease patients

Parkinson's Disease (PD) can express different rates of symptoms progression which in some patients has been found to be extremely aggressive. In this work, a set of rules are extracted using the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm to enable the prediction of such rapid disease progression cases from the baseline evaluation. After selecting the most effective of the initial 139 baseline features using the Wrapper algorithm, the best RIPPER rules are isolated based on the reported accuracy in assigning PD patients into rapid or normal progression groups. In total, about 120 unique rules that signal faster worsening of PD symptoms at baseline evaluation were extracted, assigning patients into the rapid progression group with a mean classification accuracy of 86.3%. The most informative of these rules are presented in this work, while all of them are used to develop a decision support system (DSS) to facilitate rapid progression risk assessment at the baseline evaluation. The rules were extracted using baseline evaluation data from the Parkinson's Progression Markers Initiative (PPMI) dataset. The DSS for prediction of rapid progression is included in the PD_Manager mHealth platform to offer better management of PD patients when rapid progression is expected.
Synthetic dataset generation for non-intrusive load monitoring in commercial buildings

In the recent years, there has been an increasing academic and industrial interest for analyzing the electrical consumption of commercial buildings. Whilst having similarities with the Non Intrusive Load Monitoring (NILM) tasks for residential buildings, the nature of the signals that are collected from large commercial buildings introduces additional difficulties to the NILM research. One of the main difficulties is that the amount of publicly available datasets collected from commercial buildings is very limited, which makes the NILM research even more challenging for this type of large buildings. In order to circumvent the issues caused by the lack of data available, we propose a model for generating realistic synthetic current waveforms by making use of both publicly available datasets and our private dataset that is collected from real commercial buildings. Our primarily experiments show that the generated data ressemble real datasets.
Deep Learning for Digital Text Analytics: Sentiment Analysis

In today's scenario, imagining a world without negativity is something very unrealistic, as bad NEWS spreads more virally than good ones. Though it seems impractical in real life, this could be implemented by building a system using Machine Learning and Natural Language Processing techniques in identifying the news datum with negative shade and filter them by taking only the news with positive shade (good news) to the end user. In this work, around two lakhs datum have been trained and tested using a combination of rule-based and data driven approaches. VADER along with a filtration method has been used as an annotating tool followed by statistical Machine Learning approach that have used Document Term Matrix (representation) and Support Vector Machine (classification). Deep Learning algorithms then came into picture to make this system reliable (Doc2Vec) which finally ended up with Convolutional Neural Network(CNN) that yielded better results than the other experimented modules. It showed up a training accuracy of 96%, while a test accuracy of (internal and external news datum) above 85% was obtained.
Multimodal Sparse Bayesian Dictionary Learning

This paper addresses the problem of learning dictionaries for multimodal datasets, i.e. datasets collected from multiple data sources. We present an algorithm called multimodal sparse Bayesian dictionary learning (MSBDL). MSBDL leverages information from all available data modalities through a joint sparsity constraint. The underlying framework offers a considerable amount of flexibility to practitioners and addresses many of the shortcomings of existing multimodal dictionary learning approaches. In particular, the procedure includes the automatic tuning of hyperparameters and is unique in that it allows the dictionaries for each data modality to have different cardinality, a significant feature in cases when the dimensionality of data differs across modalities. MSBDL is scalable and can be used in supervised learning settings. Theoretical results relating to the convergence of MSBDL are presented and the numerical results provide evidence of the superior performance of MSBDL on synthetic and real datasets compared to existing methods.
Preparation of BiFe1-xCoxO3 Ceramics via a Simple Solid Method and Enhanced Multiferroic Properties

BiFe 1-x CoxO3(x= 0-0.3) polycrystalline samples were prepared in order to study the effect of Co substitution on BiFeO3 structural, magnetic and electric properties, which to the best of our knowledge, the related work has not been reported. The samples were synthesized by a simple solid-state method, which the process contains mortar grind, calcinate, press and sinter. The crystal structures were refined from XRD data in the acentric R3c space group; a second phase CoFe2O4 was identified, in increasing amounts with the increase of x. The magnetic moment is greatly improved and the magnetization for BiFe 0.96 Co 0.04 O3 was found to be 0.756 emu/g.
Design of a Compact Planar Crossover With Four Intersecting Channels

A compact planar crossover with four intersecting channels is proposed. By using two cascoded rings, the crossover with eight ports is constructed. The derived electrical lengths and selected impedance ratio between the interconnected lines and the outer ring are also used to achieve the desired central frequency. The measured and theoretically calculated results match well so that the feasibility of the proposed circuit is validated.
Promoting Mental Wellbeing in Young People Aged 12 - 18 Years: Opportunities for Design

Young people with poor mental health are more likely to have poor educational attainment, employment prospects and physical health, and mental health problems often persist into adulthood. Mental health promotion aims to elevate levels of mental wellbeing while protecting against its loss and reducing the prevalence of mental health problems. The delivery of and access to mental health promotion has evolved over recent years. This research explored the area of technology for promoting mental wellbeing in young people. The research was in two parts: focus groups with 34 young people aged 12--18 years, which identified a preference for digital sources of support; and a review of 14 apps and websites for supporting 12--18 year olds to promote their mental wellbeing, which identified significant shortfalls. The main contribution to HCI is new insights and opportunities for design to support young people aged 12--18 years to promote their mental wellbeing.
Dynamic Multi-Scale Semantic Segmentation based on Dilated Convolutional Networks

Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data. Towards such goal, Convolutional Networks can learn specific and adaptable features based on the data. However, these networks are not capable of processing a whole remote sensing image, given its huge size. To overcome such limitation, the image is processed using fixed size patches. The definition of the input patch size is usually performed empirically (evaluating several sizes) or imposed (by network constraint). Both strategies suffer from drawbacks and could not lead to the best patch size. To alleviate this problem, several works exploited multi-scale information by combining networks or layers. This process increases the number of parameters resulting in a more difficult model to train. In this work, we propose a novel technique to perform semantic segmentation of remote sensing images that exploits a multi-scale paradigm without increasing the number of parameters while defining, in training time, the best patch size. The main idea is to train a dilated network with distinct patch sizes, allowing it to capture multi-scale characteristics from heterogeneous contexts. While processing these varying patches, the network provides a score for each patch size, helping in the definition of the best size for the current scenario. A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing datasets with very distinct properties. Our results show that the proposed algorithm provides improvements in pixelwise classification accuracy when compared to state-of-the-art methods.
MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning

Mobile learning in massive open online course (MOOC) evidently differs from its traditional ways as it relies more on collaborations and becomes more fragmented. We present a cloud-based virtual learning environment (VLE) which can organize learners into a better teamwork context and customize micro learning resources in order to meet personal demands in real time. Particularly, a smart micro learning environment was built by a newly designed Software as a Service (SaaS), namely Micro Learning as a Service (MLaaS). It aims to provide adaptive micro learning contents as well as learning path identifications customized for each individual learner. To personalize the micro learning, a dynamic learner model is constructed with regards to the internal and external factors that can affect learning experience and outcomes. Educational data mining (EDM) techniques are employed as the main method to understand learners’ behaviors and recognize learning resource features. A solution of learning path optimization is also proposed towards assembling a complete MOOC learning experience.
Ransomware Payments in the Bitcoin Ecosystem

Ransomware can prevent a user from accessing a device and its files until a ransom is paid to the attacker, most frequently in Bitcoin. With over 500 known ransomware families, it has become one of the dominant cybercrime threats for law enforcement, security professionals and the public. However, a more comprehensive, evidence-based picture on the global direct financial impact of ransomware attacks is still missing. In this paper, we present a data-driven method for identifying and gathering information on Bitcoin transactions related to illicit activity based on footprints left on the public Bitcoin blockchain. We implement this method on-top-of the GraphSense open-source platform and apply it to empirically analyze transactions related to 35 ransomware families. We estimate the lower bound direct financial impact of each ransomware family and find that, from 2013 to mid-2017, the market for ransomware payments has a minimum worth of USD 12,768,536 (22,967.54 BTC). We also find that the market is highly skewed with only a few number of players responsible for the majority of the payments. Based on these research findings, policy-makers and law enforcement agencies can use the statistics provided to understand the size of the illicit market and make informed decisions on how best to address the threat.
Laplacian Black Box Variational Inference

Black box variational inference (BBVI) is a recently proposed estimation method for parameters of statistical models. BBVI is an order of magnitude faster than Markov chain Monte Carlo (MCMC). The computation of BBVI is similar to maximum a posteriori estimation, but in addition to the point estimation given by the latter, BBVI also estimate another important statistic, i.e. the range of the parameters. Assuming normal distribution for the parameters, BBVI minimizes the KL divergence between the normal distribution and the statistical models. However if the parameters are not normal distributed, the estimation might not be precise. Similarly, if outliers are allowed then fat tail distributions are more suitable to model parameters. This paper discusses the problems of Cauchy and Student's t-distribution as a variational distribution. Inspired by Lasso, this paper replaces normal with Laplace distribution to implement BBVI. Experiment on linear regression shows, under non-ideal condition (e.g. with outlier data), BBSI based on Laplace provides more stable and correct estimation.
Comparison of Outcomes Following a Switch From a Brand to an Authorized Versus Independent Generic Drug

Authorized generics are identical in formulation to brand drugs, manufactured by the brand company but marketed as a generic. Generics, marketed by generic manufacturers, are required to demonstrate pharmaceutical and bioequivalence to the brand drug, but repetition of clinical trials is not required. This retrospective cohort study compared outcomes for generics and authorized generics, which serves as a generic vs. brand proxy that minimizes bias against generics. For the seven drugs studied between 1999 and 2014, 5,234 unique patients were on brand drugs prior to generic entry and 4,900 (93.6%) switched to a generic. During the 12 months following the brand‐to‐generic switch, patients using generics vs. authorized generics were similar in terms of outpatient visits, urgent care visits, hospitalizations, and medication discontinuation. The likelihood of emergency department (ED) visits was slightly higher for authorized generics compared with generics. These data suggest that generics were clinically no worse than their proxy brand comparators.
Accidents of Electrical and Mechanical Works for Public Sector Projects in Hong Kong

A study on electrical and mechanical (E&M) works-related accidents for public sector projects provided the opportunity to gain a better understanding of the causes of accidents by analyzing the circumstances of all E&M works accidents. The research aims to examine accidents of E&M works which happened in public sector projects. A total of 421 E&M works-related accidents in the “Public Works Programme Construction Site Safety and Environmental Statistics” (PCSES) system were extracted for analysis. Two-step cluster analysis was conducted to classify the E&M accidents into different groups. The results identified three E&M accidents groups: (1) electricians with over 15 years of experience were prone to ‘fall of person from height’; (2) electricians with zero to five years of experience were prone to ‘slip, trip or fall on same level’; (3) air-conditioning workers with zero to five years of experience were prone to multiple types of accidents. Practical measures were recommended for each specific cluster group to avoid recurrence of similar accidents. The accident analysis would be vital for industry practitioners to enhance the safety performance of public sector projects. This study contributes to filling the knowledge gap of how and why E&M accidents occur and promulgating preventive measures for E&M accidents which have been under researched.
Fault Analysis of Inverter-Interfaced Distributed Generators With Different Control Schemes

Diversification of control schemes adopted by inverter-interfaced distributed generators (IIDGs) leads to difficulties in fault current estimation in a microgrid, which might make preexisting protection systems invalid and threaten the safety of power electronic devices. It is therefore important to study fault characteristics of IIDGs. This paper investigates characteristics of fault current of IIDGs caused by both symmetrical and asymmetrical faults. Two kinds of widely used control modes, current control (constant current control and PQ control) and voltage control (V/F control and droop control), are under investigation to provide an intuitive comparison on fault current. In particular, a novel algorithm is proposed to calculate fault current of droop-controlled IIDGs. It is found that different limiters have great impacts on fault response of IIDGs and detailed research works are carried out to identify the effects in this paper. Simulation results based on PSCAD/EMTDC and calculation results based on MATLAB/Simulink verify the correctness of the proposed fault models.
Electrochemical Phosphate Sensors Using Silver Nanowires Treated Screen Printed Electrodes

Essential biomolecules of the human body and plant growth depend upon the proper amount of phosphate ions. Phosphorus has critical values in both agricultural and biomedical applications. There is a need for inexpensive, portable, repeatable, highly sensitive, and field deployable sensors with wide detection range to monitor the health of water system and field soil. This paper aims to develop an electrochemical phosphate sensor using novel ammonium molybdate tetrahydrate/silver nanowires (AMT/AgNWs) modified screen printed electrode (SPE) for phosphate detection to achieve simplicity, high sensitivity, wide detection range, and high repeatability and portability. The cyclic voltammetry measurements exhibited the sensitivities of AMT modified SPE without and with AgNWs are <inline-formula> <tex-math notation="LaTeX">$0.1~\mu \text{A}/\mu \text{M}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$0.71~\mu \text{A}/\mu \text{M}$ </tex-math></inline-formula>, respectively. The use of highly conductive AgNWs significantly increased the sensitivities of the AMT/SPE. Besides, AgNWs and AMT modified SPE (AMT/AgNWs/SPE) showed a very wide detection range of <inline-formula> <tex-math notation="LaTeX">$5~\mu \text{M}$ </tex-math></inline-formula>–1 mM. The maximum relative standard deviation of around 5% confirms the repeatability of the proposed sensor. This paper suggests that AMT/AgNWs/SPE is promising for simple, low-cost, and portable phosphate ion detection.
Traceability in the Wild: Automatically Augmenting Incomplete Trace Links

Software and systems traceability is widely accepted as an essential element for supporting many software development tasks. Today's version control systems provide inbuilt features that allow developers to tag each commit with one or more issue ID, thereby providing the building blocks from which project-wide traceability can be established between feature requests, bug fixes, commits, source code, and specific developers. However, our analysis of six open source projects showed that on average only 60% of the commits were linked to specific issues. Without these fundamental links the entire set of project-wide links will be incomplete, and therefore not trustworthy. In this paper we address the fundamental problem of missing links between commits and issues. Our approach leverages a combination of process and text-related features characterizing issues and code changes to train a classifier to identify missing issue tags in commit messages, thereby generating the missing links. We conducted a series of experiments to evaluate our approach against six open source projects and showed that it was able to effectively recommend links for tagging issues at an average of 96% recall and 33% precision. In a related task for augmenting a set of existing trace links, the classifier returned precision at levels greater than 89% in all projects and recall of 50%.
Deep Learning Based Pilot Allocation Scheme (DL-PAS) for 5G Massive MIMO System

This letter proposes a deep learning-based pilot assignment scheme (DL-PAS) for a massive multiple-input multiple-output (massive MIMO) system that utilizes a large number of antennas for multiple users. The proposed DL-PAS improves the performance in cellular networks with severe pilot contamination by learning the relationship between pilot assignment and the users’ location pattern. In this letter, we design a novel supervised learning method, where input features and output labels are users’ locations in all cells and pilot assignments, respectively. Specifically, pretrained optimal pilot assignments with given users’ locations are provided through an exhaustive search method as the training data. Then, the proposed DL-PAS provides a near-optimal pilot assignment from the produced inferred function by analyzing the training data. We implement the proposed scheme using a commercial deep multilayer perceptron system. Simulation-based experiments show that the proposed scheme achieves almost 99.38% theoretical upper-bound performance with low complexity, requiring only 0.92-ms computational time.
Exposure to Suicide in High Schools: Impact on Serious Suicidal Ideation/Behavior, Depression, Maladaptive Coping Strategies, and Attitudes toward Help-Seeking

Adolescents’ exposure to a peer’s suicide has been found to be associated with, as well as to predict, suicidal ideation and behavior. Although postvention efforts tend to be school-based, little is known about the impact of a schoolmate’s suicide on the school’s student population overall. The present study seeks to determine whether there is excess psychological morbidity among students in a school where a schoolmate has died by suicide, and whether students’ attitudes about coping and help-seeking strategies are more or less problematic in such schools. Students in twelve high schools in Suffolk and Westchester counties in New York State—2865 students at six schools where a student had died by suicide within the past six months, and 2419 students at six schools where no suicide had occurred within the current students’ tenure—completed an assessment of their suicidal ideation and behavior, depressive symptoms, coping and help-seeking attitudes, stressful life events, and friendship with suicide decedent (if applicable). No excess morbidity (i.e., serious suicidal ideation/behavior and depression) was evident among the general student population after a schoolmate’s death by suicide; however, the risk of serious suicidal ideation/behavior was elevated among students at exposed schools who had concomitant negative life events. There was a significant relationship between friendship with the decedent and morbidity, in that students who were friends, but not close friends, of the decedents had the greatest odds of serious suicidal ideation/behavior. Overall, students in exposed schools had more adaptive attitudes toward help-seeking; but this was not true of the decedents’ friends or students with concomitant negative life events. The implications of the findings for postvention strategies are discussed.