Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-healing UI: Mechanically and Electrically Self-healing Materials for Sensing and Actuation Interfaces

Living things in nature have long been utilizing the ability to "heal" their wounds on the soft bodies to survive in the outer environment. In order to impart this self-healing property to our daily life interface, we propose Self-healing UI, a soft-bodied interface that can intrinsically self-heal damages without external stimuli or glue. The key material to achieving Self-healing UI is MWCNTs-PBS, a composite material of a self-healing polymer polyborosiloxane (PBS) and a filler material multi-walled carbon nanotubes (MWCNTs), which retains mechanical and electrical self-healability. We developed a hybrid model that combines PBS, MWCNTs-PBS, and other common soft materials including fabric and silicone to build interface devices with self-healing, sensing, and actuation capability. These devices were implemented by layer-by-layer stacking fabrication without glue or any post-processing, by leveraging the materials' inherent self-healing property between two layers. We then demonstrated sensing primitives and interactive applications that extend the design space of shape-changing interfaces with their ability to transform, conform, reconfigure, heal, and fuse, which we believe can enrich the toolbox of human-computer interaction (HCI).
Common Food Allergens in Iranian Children

Background: The prevalence of food allergy is different in different parts of the world. Identification of the most common food allergens is a priority in any population to provide effective preventive and curative measures. Objective: The aim of this study was to determine the most common food allergens in Iranian children. Methods: One hundred and ninety children with skin, respiratory or gastrointestinal symptoms, thought to be due to food allergy, were studied. The total serum IgE and eosinophil count tests were measured in all patients. Allergy to 25 food allergens was determined according to the patient’s history, skin prick tests, RASTs (Radioallergosorbent Test) and open food challenge tests. Findings: The most common food allergens were cow’s milk, tomato, egg white, egg yolk, beef and almond, respectively. Conclusion: The order of common food allergens in this study was different from other reports; this might be due to the different food habits and /or ethnic diversities.
How Cloud Traffic Goes Hiding: A Study of Amazon's Peering Fabric

The growing demand for an ever-increasing number of cloud services is profoundly transforming the Internet's interconnection or peering ecosystem, and one example is the emergence of "virtual private interconnections (VPIs)". However, due to the underlying technologies, these VPIs are not publicly visible and traffic traversing them remains largely hidden as it bypasses the public Internet. In particular, existing techniques for inferring Internet interconnections are unable to detect these VPIs and are also incapable of mapping them to the physical facility or geographic region where they are established. In this paper, we present a third-party measurement study aimed at revealing all the peerings between Amazon and the rest of the Internet. We describe our technique for inferring these peering links and pay special attention to inferring the VPIs associated with this largest cloud provider. We also present and evaluate a new method for pinning (i.e., geo-locating) each end of the inferred interconnections or peering links. Our study provides a first look at Amazon's peering fabric. In particular, by grouping Amazon's peerings based on their key features, we illustrate the specific role that each group plays in how Amazon peers with other networks.
HairBrush for Immersive Data-Driven Hair Modeling

While hair is an essential component of virtual humans, it is also one of the most challenging digital assets to create. Existing automatic techniques lack the generality and flexibility to create rich hair variations, while manual authoring interfaces often require considerable artistic skills and efforts, especially for intricate 3D hair structures that can be difficult to navigate. We propose an interactive hair modeling system that can help create complex hairstyles in minutes or hours that would otherwise take much longer with existing tools. Modelers, including novice users, can focus on the overall hairstyles and local hair deformations, as our system intelligently suggests the desired hair parts. Our method combines the flexibility of manual authoring and the convenience of data-driven automation. Since hair contains intricate 3D structures such as buns, knots, and strands, they are inherently challenging to create using traditional 2D interfaces. Our system provides a new 3D hair authoring interface for immersive interaction in virtual reality (VR). Users can draw high-level guide strips, from which our system predicts the most plausible hairstyles via a deep neural network trained from a professionally curated dataset. Each hairstyle in our dataset is composed of multiple variations, serving as blend-shapes to fit the user drawings via global blending and local deformation. The fitted hair models are visualized as interactive suggestions that the user can select, modify, or ignore. We conducted a user study to confirm that our system can significantly reduce manual labor while improve the output quality for modeling a variety of head and facial hairstyles that are challenging to create via existing techniques.
Taming Anycast in the Wild Internet

Anycast is a popular tool for deploying global, widely available systems, including DNS infrastructure and content delivery networks (CDNs). The optimization of these networks often focuses on the deployment and management of anycast sites. However, such approaches fail to consider one of the primary configurations of a large anycast network: the set of networks that receive anycast announcements at each site (i.e., an announcement configuration). Altering these configurations, even without the deployment of additional sites, can have profound impacts on both anycast site selection and round-trip times. In this study, we explore the operation and optimization of any-cast networks through the lens of deployments that have a large number of upstream service providers. We demonstrate that these many-provider anycast networks exhibit fundamentally different properties when interacting with the Internet, having a greater number of single AS hop paths and reduced dependency on each provider, compared with few-provider networks. We further examine the impact of announcement configuration changes, demonstrating that in nearly 30% of vantage point groups, round-trip time performance can be improved by more than 25%, solely by manipulating which providers receive anycast announcements. Finally, we propose DailyCatch, an empirical measurement methodology for testing and validating announcement configuration changes, and demonstrate its ability to influence user-experienced performance on a global anycast CDN.
Audio Substituting Haptic System to Aware the Sound from Backwards

A person with hearing impairments is difficult to notice greets and calling attention from their back due to low ability of listening, and it leads the decline of the ability of sensing danger and the wrong guess at beginning communication. Therefore, we propose the system conveying the information of sound from backwards through haptic feedback. Through the experiment verifying recognition rate of DoA estimation, it is confirmed that the system can substitute the ability of rear sound image localization. Through the experiment verifying recognition rate of type of sounds, it is also confirmed that the system can aware the sound from backwards.
milliMorph -- Fluid-Driven Thin Film Shape-Change Materials for Interaction Design

This paper presents a design space, a fabrication system and applications of creating fluidic chambers and channels at millimeter scale for tangible actuated interfaces. The ability to design and fabricate millifluidic chambers allows one to create high frequency actuation, sequential control of flows and high resolution design on thin film materials. We propose a four dimensional design space of creating these fluidic chambers, a novel heat sealing system that enables easy and precise millifluidics fabrication, and application demonstrations of the fabricated materials for haptics, ambient devices and robotics. As shape-change materials are increasingly integrated in designing novel interfaces, milliMorph enriches the library of fluid-driven shape-change materials, and demonstrates new design opportunities that is unique at millimeter scale for product and interaction design.
Tip-Tap: Battery-free Discrete 2D Fingertip Input

We describe Tip-Tap, a wearable input technique that can be implemented without batteries using a custom RFID tag. It recognizes 2-dimensional discrete touch events by sensing the intersection between two arrays of contact points: one array along the index fingertip and the other along the thumb tip. A formative study identifies locations on the index finger that are reachable by different parts of the thumb tip, and the results determine the pattern of contacts points used for the technique. Using a reconfigurable 3x3 evaluation device, a second study shows eyes-free accuracy is 86% after a very short period, and adding bumpy or magnetic passive haptic feedback to contacts is not necessary. Finally, two battery-free prototypes using a new RFID tag design demonstrates how Tip-Tap can be implemented in a glove or tattoo form factor.
Errors, Misunderstandings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems

Ad-blocking systems such as Adblock Plus rely on crowdsourcing to build and maintain filter lists, which are the basis for determining which ads to block on web pages. In this work, we seek to advance our understanding of the ad-blocking community as well as the errors and pitfalls of the crowdsourcing process. To do so, we collected and analyzed a longitudinal dataset that covered the dynamic changes of popular filter-list EasyList for nine years and the error reports submitted by the crowd in the same period. Our study yielded a number of significant findings regarding the characteristics of FP and FN errors and their causes. For instances, we found that false positive errors (i.e., incorrectly blocking legitimate content) still took a long time before they could be discovered (50% of them took more than a month) despite the community effort. Both EasyList editors and website owners were to blame for the false positives. In addition, we found that a great number of false negative errors (i.e., failing to block real advertisements) were either incorrectly reported or simply ignored by the editors. Furthermore, we analyzed evasion attacks from ad publishers against ad-blockers. In total, our analysis covers 15 types of attack methods including 8 methods that have not been studied by the research community. We show how ad publishers have utilized them to circumvent ad-blockers and empirically measure the reactions of ad blockers. Through in-depth analysis, our findings are expected to help shed light on any future work to evolve ad blocking and optimize crowdsourcing mechanisms.
Assessments of myocardial perfusion in human using stress intravenous PESDA myocardial contrast echocardiography and Pulse Inversion Harmonic Imaging: A Comparison study with Tc-99m sestamibi SPECT

Objective:The object of this study was to assess the accuracy of dipyridamole stress intravenous (IV) myocardial contrast echocardiography (MCE) using pulse inversion harmonic imaging and PESDA in the detection of perfusion defect in the patients with coronary artery disease in comparison with dipyridamole stress Tc99m sestamibi SPECT. Methods:Total 46 patients (29 males, mean age 64 years old) were consecutively enrolled. Patients with prior myocardial infarction were excluded. MCE and Tc-99m sestamibi SPECT were performed at the same day during rest and after 0.56 or 0.84mg/Kg dipyridamole infusion. Continuous IV infusion of PESDA (2-5 mL/min) was administered while obtaining triggered (1:1) end-systolic apical 2, 4 chamber and long axis views. Tc-99m sestamibi was injected 3 minutes after dipyridamole. Tc-99m sestamibi SPECT images were obtained one hour later. Coronary angiography was followed within two days in all patients. Tc-99m sestamibi SPECT images were matched to the sixteen segments of left ventricle according to American Society of Echocardiography for segmental comparison. Both images were analyzed visually. Results:Using coronary angiography as the standard, MCE showed overall sensitivity of 70.7%, specificity of 95.8%, positive predictive value (PPV) of 87.8% and negative predictive value (NPV) of 88.5% in the detection of coronary atherosclerosis (≥70% stenosis). Tc-99m sestamibi SPECT showed sensitivity of 75.6%, specificity of 98.9%, PPV of 96.8% and NPV of 90.6%. The overall concordance rate between MCE and Tc99m sestamibi SPECT for the detection of perfusion defects was 86.9% (Cohen’s kappa value 0.63) according 논문접수일:2000년 7월 30일 심사완료일:2000년 8월 10일 교신저자:정남식, 120-752 서울 서대문구 신촌동 134 연세대학교 의과대학 심장혈관센터 심장내과 심혈관연구소 전화:(02) 361-7071·전송:(02) 393-2041 E-mail:namsikc@yumc.yonsei. ac.kr
Towards achieving optimal performance using stacked generalization algorithm: a case study of clinical diagnosis of malaria fever

: The birth of data mining has been a blessing to all fields of endeavours and there are numerous data mining algorithms available today. One of the major problems of mining data is the selection of the appropriate algorithm or model for a job at hand; this has led to different comparison experiments by researchers. Stacked Generalization is one of the methods of combining multiple models to give a better accuracy. The method has been investigated to be effective by many researchers over the years. This study investigates how optimal performance could be achieved using Stacked Generalization algorithm. Six different data mining algorithms (PART, REP Tree, J48, Random Tree, RIDOR and JRIP) arranged in two different orders were used as base learners to two different Meta Learners (Random Forest and NNGE) independently and the results obtained were compared in terms of classification accuracy. The study shows that the order of arrangement of the base learners and the choice of Meta Learner could affect the accuracy of the Stacked Generalization method; NNGE outperforms Random Forest as a Meta-Learner and its performance is independent of the order of arrangement of the base learners as against Random Forest. Malaria fever datasets collected from reputable hospitals in Ado-Ekiti, Ekiti State, Nigeria were purposefully used for this study because malaria is one of the major diseases killing almost a million people yearly in the tropical region of Africa, so a more accurate malaria fever diagnosis model is as well proposed as a result of this study.
The inflection point hypothesis: a principled debugging approach for locating the root cause of a failure

The end goal of failure diagnosis is to locate the root cause. Prior root cause localization approaches almost all rely on statistical analysis. This paper proposes taking a different approach based on the observation that if we model an execution as a totally ordered sequence of instructions, then the root cause can be identified by the first instruction where the failure execution deviates from the non-failure execution that has the longest instruction sequence prefix in common with that of the failure execution. Thus, root cause analysis is transformed into a principled search problem to identify the non-failure execution with the longest common prefix. We present Kairux, a tool that does just that. It is, in most cases, capable of pinpointing the root cause of a failure in a distributed system, in a fully automated way. Kairux uses tests from the system's rich unit test suite as building blocks to construct the non-failure execution that has the longest common prefix with the failure execution in order to locate the root cause. By evaluating Kairux on some of the most complex, real-world failures from HBase, HDFS, and ZooKeeper, we show that Kairux can accurately pinpoint each failure's respective root cause.
Signature detection as a way to recognise historical parish register structure

This article deals with the analysis of pages of French parish registers from 16th to 18th century. These documents are structured in paragraphs called acts. Each act contains valuable demographic information that can be useful to genealogists willing to find information about their ancestors. The first step toward parish register analysis consists of delimiting each act. But these documents are so poorly-structured that the visual separation between the acts is not always clearly visible. One of the main visual indication of separation is the signature of the priest at the end of each act. In this work, we propose to train and compare several u-shaped neural networks for signature detection. We also propose a rule-based system for segmentation into acts and evaluate the impact of signature detection at act level.
Booting the Booters: Evaluating the Effects of Police Interventions in the Market for Denial-of-Service Attacks

Illegal booter services offer denial of service (DoS) attacks for a fee of a few tens of dollars a month. Internationally, police have implemented a range of different types of intervention aimed at those using and offering booter services, including arrests and website takedown. In order to measure the impact of these interventions we look at the usage reports that booters themselves provide and at measurements of reflected UDP DoS attacks, leveraging a five year measurement dataset that has been statistically demonstrated to have very high coverage. We analysed time series data (using a negative binomial regression model) to show that several interventions have had a statistically significant impact on the number of attacks. We show that, while there is no consistent effect of highly-publicised court cases, takedowns of individual booters precede significant, but short-lived, reductions in recorded attack numbers. However, more wide-ranging disruptions have much longer effects. The closure of HackForums' booter market reduced attacks for 13 weeks globally (and for longer in particular countries) and the FBI's coordinated operation in December 2018, which involved both takedowns and arrests, reduced attacks by a third for at least 10 weeks and resulted in lasting change to the structure of the booter market.
RFTouchPads: Batteryless and Wireless Modular Touch Sensor Pads Based on RFID

This paper presents RFTouchPads, a system of batteryless and wireless modular hardware designs of two-dimensional (2D) touch sensor pads based on the ultra-high frequency (UHF) radio-frequency identification (RFID) technology. In this system, multiple RFID IC chips are connected to an antenna in parallel. Each chip connects only one of its endpoints to the antenna; hence, the module normally turns off when it gets insufficient energy to operate. When a finger touches the circuit trace attached to another endpoint of the chip, the finger functions as part of the antenna that turns the connected chip on, while the finger touch location is determined according to the chip's ID. Based on this principle, we propose two hardware designs, namely, StickerPad and TilePad. StickerPad is a flexible 3×3 touch-sensing pad suitable for applications on curved surfaces such as the human body. TilePad is a modular 3×3 touch-sensing pad that supports the modular area expansion by tiling and provides a more flexible deployment because its antenna is folded. Our implementation allows 2D touch inputs to be reliability detected 2 m away from a remote antenna of an RFID reader. The proposed batteryless, wireless, and modular hardware design enables fine-grained and less-constrained 2D touch inputs in various ubiquitous computing applications.
Rapid Prototyping of Pneumatically Actuated Inflatable Structures

Fabricating and actuating inflatables for shape-changing interfaces and soft robotics is challenging and time-consuming, requiring knowledge in diverse domains such as pneumatics, manufacturing processes for elastomers, and embedded systems. We propose in this poster a scheme for rapid prototyping and pneumatically actuating piecewise multi-chambered inflatables, using balloons as our building blocks. We provide a construction kit containing pneumatic control boards, pneumatic components, and balloons for constructing simple actuated balloon models. We also provide various primitives of actuation and locomotion to help the user put together their desired actuator, along with an Android app and software API for controlling it via Bluetooth. Finally, we demonstrate the construction and actuation of these inflatable structures using three sample applications.
Convolutional Neural Networks for Food Image Recognition: An Experimental Study

The global rising trend of consumer health awareness has increasingly drawn attention towards the development of nutrition tracking applications. A key component to enable large scale, low cost and personalized nutrition analysis is a food image recognition engine based on convolutional neural network (CNN). While there is a strong demand for many variants of such applications to serve different localized needs around the world, it remains an ongoing challenge to piece together a complete solution involving (i) dataset preparation (2) choice of CNN architecture (3) CNN optimization strategies in the context of food classification. In this work, we address this problem by conducting a series of experimental study to aid engineers in quickly preparing datasets, deploying and optimizing CNN based solutions for food image recognition tasks. Based on our results, we recommend that (i) larger networks such as Xception shall be used; (ii) minimum of 300 images per category need to be available to kick-start training for optimal performance; (iii) image augmentation techniques that do not alter shapes are more beneficial; various augmentation techniques can also be combined for better results; (iv) dataset balancing strategy might not be required to address class imbalance in food datasets below an imbalance ratio of 7; (v) higher native image resolution during training is beneficial to classifier performance, especially for networks requiring larger input size.
Classifying sentiment of dialectal arabic reviews: a semi-supervised approach

: Arab Internet users tend to use dialectical words to express how they feel about products, services, and places. Although, dialects in Arabic derived from the formal Arabic language, it differs in several aspects. In general, Arabic sentiment analysis recently attracted lots of researchers’ attention. A considerable amount of research has been conducted in Modern Standard Arabic (MSA), but little work has focused on dialectal Arabic. The presence of the dialect in the Arabic texts made Arabic sentiment analysis is a challenging issue, due to it usually does not follow specific rules in writing or speaking system. In this paper, we implement a semi-supervised approach for sentiment polarity classification of dialectal reviews with the presence of Modern Standard Arabic (MSA). We combined dialectal sentiment lexicon with four classifying learning algorithm to perform the polarity classification, namely Support Vector Machines (SVM), Naïve Bayes (NB), Random Forest, and K-Nearest Neighbor (K-NN). To select the features with which the classifiers can perform the best, we used three feature evaluation methods, namely, Correlation-based Feature Selection, Principal Components Analysis, and SVM Feature Evaluation. In the experiment, we applied the approach to a data set which was manually collected. The experimental results show that the approach yielded the highest classification accuracy using SVM algorithm with 92.3 %.