Miracle-dz/UniFAR-FTUs huggingface dataset:
Who decides what is allowed?: User Interactions and Permissions Use on Android

No paper or presentation is included, but the material is available via the following two published papers: " User Interactions and Permission Use on Android: https://dl.acm. org/citation.cfm? id=3025706 " User Comfort with Android Background Resource Accesses in Different Contexts: https://www.usenix. org/conference/soups2018/presentation/votipka
Increasing Student's Engagement towards Learning English Using Instant Messaging as a Teaching Tool in a Blended Learning Classroom

This study's objective was to ascertain if using instant messaging in a blended learning classroom, the engagement of Thai undergraduate students towards learning English would improve. The study used a quasi-experimental design, and included a control group who were taught in a traditional manner and compared it to an experimental group who were given discussion topics, assignments, reminders and organizational matters using instant messaging. Pre and post-test questionnaires were used to determine the student's engagement towards academic challenge, learning with peers and experience with faculty. The results were analyzed using MANOVA and follow-up ANOVAs. It was shown that the experimental group made significant improvements in engagement. This result suggests methods for teachers with large, mixed ability classes to improve engagement and for less proficient students to succeed.
H Space: Interactive Augmented Reality Art

This artwork exploits recent research into augmented reality systems, such as the HoloLens, for building creative interaction in augmented reality. The work is being conducted in the context of interactive art experiences. The first version of the audience experience of the artwork, "H Space", was informally tested in the SIGGRAPH 2018 Art Gallery context. Experiences with a later, improved, version was evaluated at Tsinghua University. The latest distributed version will be shown in Sydney. The paper describes the concept, the background in both the art and the technological domain and points to some of the key computer human interaction art research issues that the work highlights.
An Analysis of Phishing Blacklists: Google Safe Browsing, OpenPhish, and PhishTank

Blacklists play a vital role in protecting internet users against phishing attacks. The effectiveness of blacklists depends on their size, scope, update speed and frequency, and accuracy - among other characteristics. In this paper we present a measurement study that analyses 3 key phishing blacklists: Google Safe Browsing (GSB), OpenPhish (OP), and PhishTank (PT). We investigate the uptake, dropout, typical lifetimes, and overlap of URLs in these blacklists. During our 75-day measurement period we observe that GSB contains, on average, 1.6 million URLs, compared to 12,433 in PT and 3,861 in OP. We see that OP removes a significant proportion of its URLs after 5 and 7 days, with none remaining after 21 days - potentially limiting the blacklist’s effectiveness. We observe fewer URLs residing in all 3 blacklists as time-since-blacklisted increases – suggesting that phishing URLs are often short-lived. None of the 3 blacklists enforce a one-time-only URL policy - therefore protecting users against reoffending phishing websites. Across all 3 blacklists, we detect a significant number of URLs that reappear within 1 day of removal – perhaps suggesting premature removal or re-emerging threats. Finally, we discover 11,603 unique URLs residing in both PT and OP – a 12% overlap. Despite its smaller average size, OP detected over 90% of these overlapping URLs before PT did.
The Genomic and Transcriptomic Analysis of Stomach Cancer

The increasing number in cancer population necessitates the urgency for studying cancer. Cancer, the abnormal proliferation of cells arising from a particular organ and metastasizing to nearby areas, is a general category that contains more than 100 fatal diseases and is caused by alterations in oncogenes and tumor suppressor genes. This project adopted whole exome sequencing (DNA) data of 6 stomach cancer patients including 6 from tumor tissues and 3 from adjacent normal tissues. Genomic analysis of 6 stomach cancer patients revealed the number and frequency of mutated genes while transcriptomic analysis indicated the protein profiling of stomach cancer, which was performed by retrieving data from TCGA and TEGx database. For genomic analysis of common oncogene and tumor suppressor genes comparing the six patients' tumor tissue, the sequencing results identified 5 different types of mutations containing SNV (single nucleotide variation), MNV (multiple nucleotide variation), insertion, deletion and replacement. The common oncogene BCR was found in 5 of the 6 patients. In transcriptomic analysis of three patients' tumor gene subtract their normal gene, the shared mutated gene MUC3A was found significantly over-expressed in tumor tissues. Finally, the high p-value of the survival curve of MUC3A expression and predicted patients' survival conditions indicated that there's no statistically-significant correlation between MUC3A's over-expressions and patients' survival.
Heart Waves: A Heart Rate Feedback System Using Water Sounds

Wearable devices of today help people track and monitor their biometric data such as heart rate. While the tracked data can help inform people of their health, many find that it adds unnecessary anxieties in the way the feedback is provided. In the case of college students, they spend most of their time in a stressful environment, leading them to an increase in the risk of mental health issues. To help with this issue, we present Heart Waves, an experimental ambient feedback system that tracks heart rate and uses water sound to provide feedback in a stressful work environment. Heart Waves uses the sound of falling water to create a relaxing atmosphere to help ease any stress they are going through. As the user's heart rate goes up, the flow of water increases, and as their heart rate goes down, the flow rate of water decreases. The purpose of this project is to automatize the processing of heart rate data so that the user does not have to analyze the data and create an ambient feedback system that adjusts to their heart rate.
When Trusted Black Boxes Don't Agree: Incentivizing Iterative Improvement and Accountability in Critical Software Systems

Software increasingly plays a key role in regulated areas like housing, hiring, and credit, as well as major public functions such as criminal justice and elections. It is easy for there to be unintended defects with a large impact on the lives of individuals and society as a whole. Preventing, finding, and fixing software defects is a key focus of both industrial software development efforts as well as academic research in software engineering. In this paper, we discuss flaws in the larger socio-technical decision-making processes in which critical black-box software systems are developed, deployed, and trusted. We use criminal justice software, specifically probabilistic genotyping (PG) software, as a concrete example. We describe how PG software systems, designed to do the same job, produce different results. We highlight the under-appreciated impact of changes in key parameters and the disparate impact that one such parameter can have on different racial/ethnic groups. We propose concrete changes to the socio-technical decision-making processes surrounding the use of PG software that could be used to incentivize iterative improvements in the accuracy, fairness, reliability, and accountability of these systems.
Bridging the gap from AI ethics research to practice

The study of fairness in machine learning applications has seen significant academic inquiry, research and publication in recent years. Concurrently, technology companies have begun to instantiate nascent program in AI ethics and product ethics more broadly. As a result of these efforts, AI ethics practitioners have piloted new processes to evaluate and ensure fairness in their machine learning applications. In this session, six industry practitioners, hailing from LinkedIn, Yoti, Microsoft, Pymetrics, Facebook, and Salesforce share insights from the work they have undertaken in the area of fairness, what has worked and what has not, lessons learned and best practices instituted as a result. • Krishnaram Kenthapadi presents LinkedIn's fairness-aware reranking for talent search. • Julie Dawson shares how Yoti applies ML fairness research to age estimation in their digital identity platform. • Hanna Wallach contributes how Microsoft is applying fairness principles in practice. • Lewis Baker presents Pymetric's fairness mechanisms in their hiring algorithm. • Isabel Kloumann presents Facebook's fairness assessment framework through a case study of fairness in a content moderation system. • Sarah Aerni contributes how Salesforce is building fairness features into the Einstein AI platform. Building on those insights, we discuss insights and brainstorm modalities through which to build upon the practitioners' work. Opportunities for further research or collaboration are identified, with the goal of developing a shared understanding of experiences and needs of AI ethics practitioners. Ultimately, the aim is to develop a playbook for more ethical and fair AI product development and deployment.
Internet of things in designing tangible interfaces for children with special needs

The Internet of Things (IoT) is a technology that allows physical objects to be interconnected through the Internet. The growth in technology means that it now plays an important role in the transformation of environments so that they are better able to benefit children with special needs. Connectivity between different elements is therefore of great interest in developing prototypes oriented to storing information in the cloud or connecting physical objects. Historically children in the classroom have always interacted with physical objects, where they have used their objects in the classroom to learn some functionality. The design of tangible interfaces now allows children to interact with a digital as well as a physical environment.
Generating Pedestrian Training Dataset using DCGAN

Recently, as autonomous cars are developing very fast, it is the most crucial task to detect pedestrians for autonomous driving. Convolution neural network based on pedestrian detection models has gained enormous success in many applications. However, these models need a large amount of annotated and labeled datasets for training process which requires lots of time and human effort. For training samples, the diversity and quantity of datasets are very important. The proposed framework is based on Deep Convolutional Generative Adversarial Networks (DCGAN), able to generate realistic pedestrians. Experimental results show that DCGAN framework is able to synthesize real pedestrian images with diversity. The synthesized samples can be included in training data to improve the performance of pedestrian detectors. 24,770 images including PETA dataset, Inria dataset were used for the training process.
PotSense: Pothole Detection on Indian Roads using Smartphone Sensors

An Intelligent Transport System is an essential facet of today's world. The Indian traffic scenario has many distinct challenges that are not common in other countries. These embody less-disciplined/chaotic lane traffic, varied vehicle types plying at the same time and poor road conditions such as potholes. As per MoRTH (Ministry of Road Transport and Highways) India, around a million accidents and nearly 10,000 road accident-based deaths happen every year solely owing to potholes. Given the humongous road network in India, combined with limitations of Government Departments, typically only a few roads are well-maintained. In this regard, this work proposes an economical yet efficient system called PotSense. PotSense uses smartphone-based sensors, particularly accelerometer and camera sensors, to crowd-source information of potholes on public roads. Furthermore, it is broadcasted to all road users to ensure their safety. PotSense also investigates the utilization of different neural network techniques for processing the data obtained from the camera. Moreover, the collected data is analyzed to perceive substantial insight on road quality and potholes. Notably, it is observed that irrespective of the dimension, depth, and other characteristics of the pothole, the proposed solution, "PotSense" accurately detects the pothole in more than 60\% of the typical Indian road scenarios.
Assist and Architecture Requirements for SRAM using a 28nm Silicon Test Vehicle

Planar devices are getting smaller, variations are increasing at both at the global and local levels and work functions and parasitic are dominating designer thoughts. However, with the advent of multigate and non-planar devices, designers are being presented with notable improvements beyond the regular device scaling. These new devices present new challenges in both logic and memory design and layout. This paper will address several of those challenges and present novel solutions for bitcell implementations as well as assisted row & column circuits for bitcell read/write operation to improve yield and reduce disturb.
Promoting Student Engagement Using Flipped Classroom in Large Introductory Financial Accounting Class

Flipped classroom has become an instructional trend used by many universities especially in Science courses. The use of flipped instructional classroom encourages student to be actively engaged in their learning. However, the implementation of flipped classroom in Accounting education at the university level particularly in large classroom has been limited so far. Therefore, the focus of this study is to understand student engagement in a series of flipped classrooms in a large first-year introductory Financial Accounting subject. The study employed a survey questionnaire which generated open and closed responses, classroom observation, and analyses of students' responses on Slido pages and their scores in formative tasks. The study found that the flipped classroom has a positive impact on student engagement particularly in improving students' understanding of subject content and during class discussion between students and student-instructor. Recommendations for the use of flipped classroom for large Accounting classroom are also provided.
A Comprehensive Assessment Method of Asset Importance for Key Asset Protection

In a given system network, an important prerequisite for security risk control is how to accurately calculate the impact of different host assets in the topology on the spread of attack risk. In this regard, we propose a comprehensive asset importance assessment model for critical asset protection. First, the importance of local static assets in the topology was evaluated using three methods: degree centrality, mediation centrality, and closeness centrality. At the same time, PageRank was used to assess the importance of local dynamic assets for asset level changes. By combining the above two methods, and using K-CORE to evaluate the core position of the host in the network topology as the global weight indicator of the importance of assets. The comprehensive evaluation results of the importance of topology-oriented assets was finally obtained, and the validity of the model was verified through the experiment of the simulation ER random network.
Undῠla: Embodied Mediated Communication via Coordinated Movement

This work is the part of my ongoing Practice-based PhD that explores ways of making people feel more connected across geographical distance. My research proposes to reconsider the view of mediated communication as disembodied sending-receiving of information that is missing contextual and bodily aspects. Through my experimental practice I aim to reinforce characteristics of our embodied existence and allow dyads (two people) to communicate over distance in a dynamic, co-regulated and ambiguous way without the use of explicit written language or speech. This paper describes the first experimental work - "Undula". It aims to test the hypothesis that: connectedness can arise from a jointly attentive dynamic body movement coordination. The experimental work features two identical custom-made rocking chairs with a sonified movement feedback (ocean waves). The chairs will be placed in separate locations allowing participants to coordinate their movement through a mutual internal self-paced rhythm over distance with no visual feedback. Explore this work in action: https://brazauskayte.com/undula.
Identifying Online Profiles of Distance Learning Students Using Data Mining Techniques

Educational data has grown over the years with the increased use of technology within educational environments. This has led to a huge amount of data being stored in various data sources representing the student, his/her activities, and other aspects relevant to the learning process. To meet this analytical need, Educational Data Mining (EDM) has emerged to assist educational institutions in identifying key benefits such as students at risk, the level of student engagement or predicting student performance. The aim of this research was to explore the various aspects of student interaction data using data mining techniques to identify relevant patterns of behaviors and possible key attributes that have higher degrees of influence on distance learning students. The main findings identified several patterns of user online profiles based on a set of adopted learning strategies, the research proposed a framework for analyzing such interaction data based on R and Hadoop platforms to correlate online profiles with student performance.
Flexible Multi-modal Hashing for Scalable Multimedia Retrieval

Multi-modal hashing methods could support efficient multimedia retrieval by combining multi-modal features for binary hash learning at the both offline training and online query stages. However, existing multi-modal methods cannot binarize the queries, when only one or part of modalities are provided. In this article, we propose a novel Flexible Multi-modal Hashing (FMH) method to address this problem. FMH learns multiple modality-specific hash codes and multi-modal collaborative hash codes simultaneously within a single model. The hash codes are flexibly generated according to the newly coming queries, which provide any one or combination of modality features. Besides, the hashing learning procedure is efficiently supervised by the pair-wise semantic matrix to enhance the discriminative capability. It could successfully avoid the challenging symmetric semantic matrix factorization and O(n2) storage cost of semantic matrix. Finally, we design a fast discrete optimization to learn hash codes directly with simple operations. Experiments validate the superiority of the proposed approach.
An AR Puzzle Application for Improving Emotion Recognition for AS Children

Affecting to around 1% of the population, Autism is sometimes described as a different approach to interacting with the world. Adapting the surrounding objects and systems can improve their experience and their relative's. This project is based on previous research where it has been shown that toys can influence positively in a child's development. Also, new technologies as Augmented Reality (AR) can be beneficial for these children in attracting and keeping their attention. The proposed game would engage the player by first creating a customized monster with the help of different AR markers. In a second stage, the player would try to guess the emotion of different monsters or virtual humans. The game will be tested in further stages to check its suitability for the AS children and the effect on their emotion recognition skills.
AfibPred: A Novel Atrial Fibrillation Prediction Approach based on Short Single-Lead ECG Using Deep Transfer Knowledge

According to the World Health Organization, cardiovascular disease (CVD) is the leading cause of death worldwide. One of the most common heart diseases that lead to stroke and some other heart problems is an Atrial Fibrillation (AFib). To cope with this dangerous risk, an early diagnosis of CVD is a global emergency helping patients to get timely treatment, and decreasing the prevalence of mortality. The early diagnosis of CVD can be conducted by an efficient and accurate analysis of a heart electrocardiogram (ECG) signal. In this paper, we propose a novel atrial fibrillation prediction approach called (AfibPred). AfibPred is based on the recent deep transfer learning technology to detect AFib disease through reusing knowledge gained from classifying some various CVD diseases. This proposal is consisted of three main processes, the first one is to discover pretrained models trained on the large multi-leads dataset, whereas the second process exploits the pretrained models for classifying Atrial Fibrillation (AFib), Noisy (P), Normal (N) and Other rhythms (O) by analyzing short single-lead ECG signal. Finally, we are stacking different models gained using Extreme Gradient Boosting (XG-boost) method. After a set of validations, the best trained AfibPred achieved an overall F1-score of 97%, and AFib F1-score of 99% proving the accuracy of AFib prediction.
Is Satellite Image Target Camouflage Still Valid Under Deep Learning Target Detection?

Satellite images can be used to observe a wild range of ground in a bird's eye view. In the past, researchers used hand-craft features to detect targets in satellite images. With the rapid growth of deep learning, neural networks such as Faster R-CNN, YOLO, SSD and RetinaNet can detect targets precisely and quickly. Traditional camouflage is designed to make important targets hard to identify. So whether the satellite image target camouflage is still effective with deep learning target detection? In this paper, we use YOLO v3 and RetinaNet to verify the effectiveness of camouflage and propose an improved YOLO v3 to enhance detection efficiency, which raise the detection speed from 34.5fps to 55.3fps in an image of 416*416. The experimental results show that the target camouflage in the satellite image has no effect under deep learning target detection methods. At the end of the paper, suggestions on how to improve the camouflage effect to resist deep learning detection are proposed.
A Survey of Hybrid Deep Learning Methods for Traffic Flow Prediction

Traffic flow prediction using big data and deep learning attracts great attentions in recent years. Researchers show that DNN models can provide better traffic prediction accuracy than the traditional shallow models. Since the traffic flow reveals both spatial and temporal dependency characteristics, and may be impacted by weather, social event data etc., therefore, a set of hybrid DNN models have been presented recently in literature for further improving the traffic flow prediction performances. The hybrid models can capture dependency in multi-dimension and show better prediction performances than simple DNN models. This paper presents a thorough review and comparison of hybrid deep learning models for traffic flow prediction. We review the data sources used in hybrid deep learning and the various hybrid deep learning models built for trafficc flow prediction. The benefits of using hybrid models are summarized.
Structural Style in the Peel Region, NWT and Yukon

The frontal ranges of the northern Mackenzie Mountains, Northwest Territories, exhibit an abrupt change in structural trend from predominantly N-S in the Mackenzie Plain to E-W in the Peel Plateau area, and N-S again in the Richardson Mountains, Yukon Territory, resulting in a very pronounced arcuate shape. Across the Mackenzie Plain Basin, the Franklin Mountains mirror the changes in the structural trend and parallel the Mackenzie Mountains for a distance of 600 km.