Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Final Rinse Agitation with Ultrasonic or 808 nm Diode Laser on Coronal Microleakage of Root-canal Treated Teeth

Introduction: This in vitro study aimed at comparing the effect of agitating the final irrigant solutions of root canal by ultrasonic or using 808 nm Diode laser on the apical seal of canal. Methods and Materials: A total of 90 extracted human maxillary central incisors were prepared up to size #45 and were randomly assigned to 4 experimental groups (n=20) and two control groups (n=5) respectively, as follows: I): 3 mL of 5.25% NaOCl was agitated as final irrigant solution with ultrasonic for 30 sec. The ultrasonic tip was 1 mm shorter than the working length, II): 3 mL of 5.25% NaOCl was agitated as final irrigant with 808 nm Diode laser for 30 sec. Fiber tip, placed in 1 mm shorter from working length was spirally moved coronally, III): 3 mL of 17% EDTA was agitated as final irrigant with 808 nm Diode laser for 30 sec and was applied similar to group II, IV): 3 mL of 17% EDTA was stimulated as final irrigant with ultrasonic for 30 sec and was applied similar to I. Apical seal was assessed by Dual Chamber technique using Bovine Serum Albumin protein. Kruskal-Wallis and Mann Whitney tests were used with significance level lower than 0.05% for statistical analysis. Results: The average leakage in the negative control, positive control, and groups I, II, III, IV were: 0.00, 13.5±5.1, 1.72±2.9, 5.12±5.6, 3.36±3.7, 2.4±4.2, respectively. Statistical analysis showed significant difference between groups (P<0.05). There was a significant difference between groups 1 and 2 in terms of protein leakage. Conclusion: Agitating 5.25% sodium hypochlorite solution as the final irrigant with ultrasonic is more effective in apical leakage reduction compared to other groups.
Social Connection Aware Team Formation for Participatory Tasks

Performance of a collaborative task is mostly dependent on the collective effort from participants. To accomplish a participatory task effectively and efficiently, the team formation problem (TFP) outweighs all other considerations. It is even more complicated when social connections among candidates is taken into account. As we can imagine, a large number of tasks require members of the team to be socially close. On the contrary, a portion of tasks, e.g., proposal review, pay more attention to a multidimensional view, and team members should be selected from a variety of cliques. Due to the nature of tasks, it is challenging to find a subset that meets the skill requirement of the task as well as socially diversity demand of team members from a pool of candidates. In this paper, we explore the TFP in a social network. Based on different task objectives, we first formulate the TFP as TFP with strong ties (TFP-ST) and TFP with weak ties (TFP-WT), respectively. Both TFP-ST and TFP-WT are proven to be NP-hard, and we then design corresponding heuristic algorithms to solve the two problems. Through extensive simulations, we show that the solution to TFP-ST can achieve significant improvement in terms of collaboration cost, team size, as well as running time, and the solution to TFP-WT can provide better performance than existing approaches at the same time.
AdAM: Adaptive approximation management for the non-volatile memory hierarchies

Existing memory approximation techniques focus on employing approximations at an individual level of the memory hierarchy (e.g., cache, scratchpad, or main memory). However, to exploit the full potential of approximations, there is a need to manage different approximation knobs across the complete memory hierarchy. Towards this, we model a system including STT-RAM scratchpad and PCM main memory with different approximation knobs (e.g., read/write pulse magnitude/duration) in order to synergistically trade data accuracy for both STT-RAM access delay and PCM lifetime by means of an integer linear programming (ILP) problem at design-time. Furthermore, a runtime algorithm is proposed to adaptively tune the approximation knobs of both STT-RAM and PCM to obtain high energy savings while keeping error-per-second within acceptable ranges across the complete memory hierarchy. We evaluated our proposed technique (i.e., AdAM) in a baseline system consisting of 1–2MB STT-RAM scratchpad and 0.5–1GB PCM main memory. The experimental results demonstrate that AdAM improves the execution time and the lifetime of memory by up to 38.7% and 1.6X, respectively.
M3 Gesture Menu: Design and Experimental Analyses of Marking Menus for Touchscreen Mobile Interaction

Despite their learning advantages in theory, marking menus have faced adoption challenges in practice, even on today's touchscreen-based mobile devices. We address these challenges by designing, implementing, and evaluating multiple versions of M3 Gesture Menu (M3), a reimagination of marking menus targeted at mobile interfaces. M3 is defined on a grid rather than in a radial space, relies on gestural shapes rather than directional marks, and has constant and stationary space use. Our first controlled experiment on expert performance showed M3 was faster and less error-prone by a factor of two than traditional marking menus. A second experiment on learning demonstrated for the first time that users could successfully transition to recall-based execution of a dozen commands after three ten-minute practice sessions with both M3 and Multi-Stroke Marking Menu. Together, M3, with its demonstrated resolution, learning, and space use benefits, contributes to the design and understanding of menu selection in the mobile-first era of end-user computing.
Brain Computer Interface - some technical remarks

We consider some grafic and computational aspects of BCI's - Brain Computer Interfaces, which could be of importance in the training of users and exploitation by uesrs, in the future system learning and acquisition and mapping of the important events and knowledge. The current systems transfer rates at the BCI are estimated, proposing potential accelerations by use of inner tones and melodies.
A cross-layer adaptive approach for performance and power optimization in STT-MRAM

Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) is a promising candidate as a universal on-chip memory technology due to non-volatility, high density and scalability. However, high write energy and latency are major challenges in this memory technology due to the asymmetry and stochastic nature of the write operation. Typically, the write current is set for the minimum energy point, which can further impact the write latency. To mitigate these issues, we propose an adaptive write current scaling technique that adjusts the write current, and hence the write latency and energy based on the performance needs at run-time. Using this technique, optimal energy and performance points for write current are obtained using detailed device and system level analysis. Furthermore, we use runtime adaptation of write current by predicting the write access rate for the next execution phase. We evaluate the efficiency of the proposed approach on SPEC2000 applications for STT-MRAM-based L1 and L2-cache levels. The results show that the effective write latency of L1 and L2 is reduced by 52.4% and 55.7% with 7.6% and 1.4% area overheads, respectively, corresponding to the overall system performance optimization of 15.5% while the total memory energy consumption is increasing by only 3.2%.
EDITalk: Towards Designing Eyes-free Interactions for Mobile Word Processing

We present EDITalk, a novel voice-based, eyes-free word processing interface. We used a Wizard-of-Oz elicitation study to investigate the viability of eyes-free word processing in the mobile context and to elicit user requirements for such scenarios. Results showed that meta-level operations like highlight and comment, and core operations like insert, delete and replace are desired by users. However, users were challenged by the lack of visual feedback and the cognitive load of remembering text while editing it. We then studied a commercial-grade dictation application and discovered serious limitations that preclude comfortable speak-to-edit interactions. We address these limitations through EDITalk's closed-loop interaction design, enabling eyes-free operation of both meta-level and core word processing operations in the mobile context. Finally, we discuss implications for the design of future mobile, voice-based, eyes-free word processing interface.
“Suddenly, we got to become therapists for each other”: Designing Peer Support Chats for Mental Health

Talk therapy is a common, effective, and desirable form of mental health treatment. Yet, it is inaccessible to many people. Enabling peers to chat online using effective principles of talk therapy could help scale this form of mental health care. To understand how such chats could be designed, we conducted a two-week field experiment with 40 people experiencing mental illnesses comparing two types of online chats-chats guided by prompts, and unguided chats. Results show that anxiety was significantly reduced from pre-test to post-test. User feedback revealed that guided chats provided solutions to problems and new perspectives, and were perceived as "deep," while unguided chats offered personal connection on shared experiences and were experienced as "smooth. " We contribute the design of an online guided chat tool and insights into the design of peer support chat systems that guide users to initiate, maintain, and reciprocate emotional support.
Scaling Classroom IT Skill Tutoring: A Case Study from India

India is home to the largest under-25 demographic profile in the world, but lacks a job-ready educational system. It requires a wide-spread, skill-oriented educational model, equipping youth to thrive in highly dynamic job markets. As a response to the huge demand for technical education, a large private skill-tutoring ecosystem has sprung up in In-dia but remains geographically limited. This paper, drawn from a three-month ethnographic research conducted in Ameerpet (arguably India's largest IT skilling hub), probes the pedagogic style and characteristics of tutoring, and of-fers reasons why learners prefer to enroll into a physical model of classroom teaching over online courses. We make design suggestions for online learning platforms to attract students who are marginalized in the more formal and com-petitive education system, and opt for Ameerpet-like skill-hubs. Our primary offering is to suggest a shift in perspec-tive of online education platforms to include job readiness and accompanying changes in course content and delivery.
Effectiveness of Trigger Point Manual Treatment on the Frequency, Intensity, and Duration of Attacks in Primary Headaches: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background A variety of interventions has been proposed for symptomatology relief in primary headaches. Among these, manual trigger points (TrPs) treatment gains popularity, but its effects have not been investigated yet. Objective The aim was to establish the effectiveness of manual TrP compared to minimal active or no active interventions in terms of frequency, intensity, and duration of attacks in adult people with primary headaches. Methods We searched MEDLINE, COCHRANE, Web Of Science, and PEDro databases up to November 2017 for randomized controlled trials (RCTs). Two independent reviewers appraised the risk-of-bias (RoB) and the grading of recommendations, assessment, development, and evaluation (GRADE) to evaluate the overall quality of evidence. Results Seven RCTs that compared manual treatment vs minimal active intervention were included: 5 focused on tension-type headache (TTH) and 2 on Migraine (MH); 3 out of 7 RCTs had high RoB. Combined TTH and MH results show statistically significant reduction for all outcomes after treatment compared to controls, but the level of evidence was very low. Subgroup analysis showed a statistically significant reduction in attack frequency (no. of attacks per month) after treatment in TTH (MD −3.50; 95% CI from −4.91 to −2.09; 4 RCTs) and in MH (MD −1.92; 95% CI from −3.03 to −0.80; 2 RCTs). Pain intensity (0–100 scale) was reduced in TTH (MD −12.83; 95% CI from −19.49 to −6.17; 4 RCTs) and in MH (MD −13.60; 95% CI from −19.54 to −7.66; 2RCTs). Duration of attacks (hours) was reduced in TTH (MD −0.51; 95% CI from −0.97 to −0.04; 2 RCTs) and in MH (MD −10.68; 95% CI from −14.41 to −6.95; 1 RCT). Conclusion Manual TrPs treatment of head and neck muscles may reduce frequency, intensity, and duration of attacks in TTH and MH, but the quality of evidence according to GRADE approach was very low for the presence of few studies, high RoB, and imprecision of results.
How to Design a Digital Storytelling Authoring Tool for Developing Pre-Reading and Pre-Writing Skills

In the paper we describe an exploration into the design of an authoring tool to support the creation of multimedia stories. We explicitly targeted children with no reading or writing skills and their educators. Children in this age group often enjoy reading and creating stories together with adults and in so doing develop important pre-literacy skills. Literature suggests that when children play an active role in these activities, with a high level of engagement and interaction, there is a significant increase in their vocabulary acquisition and an improvement in their communication skills. Thus, we investigated these issues by conducting an explorative study in a pre-school class with fifteen children and three teachers. Here, we describe the emerging challenges and provide design directions for an authoring system to support the co-creation of stories for pre-literate children.
Ambient: Facial Thermal Feedback in Remotely Operated Applications

Facial thermoreception plays an important role in mediating surrounding ambient and direct feeling of temperature and touch, enhancing our subjective experience of presence. Such sensory modality has not been well explored in the field of Telepresence and Telexistence. We present Ambient, an enhanced experience of remote presence that consists of a fully facial thermal feedback system combined with the first person view of Telexistence systems. Here, we present an overview of the design and implementation of Ambient, along with scenarios envisioned for social and intimate applications.
Predicting Missing Values in Spatio-Temporal Remote Sensing Data

Continuous, consistent, and long time-series from remote sensing are essential to monitoring changes on Earth’s surface. However, analyzing such data sets is often challenging due to missing values introduced by cloud cover, missing orbits, sensor geometry artifacts, and so on. We propose a new and accurate spatio-temporal prediction method to replace missing values in remote sensing data sets. The method exploits the spatial coherence and temporal seasonal regularity that are inherent in many data sets. The key parts of the method are: 1) the adaptively chosen spatio-temporal subsets around missing values; 2) the ranking of images within the subsets based on a scoring algorithm; 3) the estimation of empirical quantiles characterizing the missing values; and 4) the prediction of missing values through quantile regression. One advantage of quantile regression is the robustness to outliers, which enables more accurate parameter retrieval in the analysis of remote sensing data sets. In addition, we provide bootstrap-based quantification of prediction uncertainties. The proposed prediction method was applied to a Normalized Difference Vegetation Index data set from the Moderate Resolution Imaging Spectroradiometer and assessed with realistic test data sets featuring between 20% and 50% missing values. Validation against established methods showed that the proposed method has a good performance in terms of the root-mean-squared prediction error and significantly outperforms its competitors. This paper is accompanied by the open-source R package gapfill, which provides a flexible, fast, and ready-to-use implementation of the method.
TopicOnTiles: Tile-Based Spatio-Temporal Event Analytics via Exclusive Topic Modeling on Social Media

Detecting anomalous events of a particular area in a timely manner is an important task. Geo-tagged social media data are useful resource for this task; however, the abundance of everyday language in them makes this task still challenging. To address such challenges, we present TopicOnTiles, a visual analytics system that can reveal information relevant to anomalous events in a multi-level tile-based map interface by using social media data. To this end, we adopt and improve a recently proposed topic modeling method that can extract spatio-temporally exclusive topics corresponding to a particular region and a time point. Furthermore, we utilize a tile-based map interface to efficiently handle large-scale data in parallel. Our user interface effectively highlights anomalous tiles using our novel glyph visualization that encodes the degree of anomaly computed by our exclusive topic modeling processes. To show the effectiveness of our system, we present several usage scenarios using real-world datasets as well as comprehensive user study results.
Low switching frequency model predictive control of PMSM drives for traction applications

In this paper, direct torque control model predictive algorithm is applied to an interior permanent magnet synchronous motor for traction applications. Models for the electric machine and a 2-level voltage source inverter are presented and their relevant equations are expressed. Model predictive control scheme comprises of using a model of the system to predict the future behavior of the plant variables until a predefined horizon in time, and finally selection of the proper control action which minimizes a cost function. This cost function includes several terms, each representing a specific control objective, which in case of controlling a traction permanent magnet motor drive are torque command tracking, maximum torque per ampere, and complying with system ratings limitations. Also the issue of computation delay, an intrinsic characteristic of model predictive control theory when implemented digitally, is studied and a solution is proposed. Finally, an alternative method to meet the rating limitations and a novel switching frequency reduction technique are introduced. The control strategy is evaluated under standard maneuver scenario of motor starting and a speed reference step followed by load torque application and the results are illustrated.
Designing Pronunciation Learning Tools: The Case for Interactivity against Over-Engineering

Paired role-play is a common collaborative activity in language learning classrooms, adding meaning and cultural context to the learning process. This is complemented by teachers' immediate and explicit feedback. Interactive tools that provide explicit feedback during collaborative learning are scarce, however. More commonly, supporting dialogue practice takes the form of computer-aided single-student read-and-record activities. This limitation is partly due to the complexity of processing language learners' speech in unconstrained tasks. In this paper, we assess the value of pronunciation error detection algorithms within a realistic, software-aided, paired role-playing task with beginning learners of French. We found that students' pronunciations improve regardless of the type of error detector employed -- even for those using simple heuristics. We suggest that speech technologies for language learning have been too focused on engineering goals. Instead, new interactive designs supporting collaboration may be used to overcome engineering limitations and properly support students' engagement.
D-SWIME: A Design Space for Smartwatch Interaction Techniques Supporting Mobility and Encumbrance

Smartwatches enable rapid access to information anytime and anywhere. However, current smartwatch content navigation techniques, for panning and zooming, were directly adopted from those used on smartphones. These techniques are cumbersome when performed on small smartwatch screens and have not been evaluated for their support in mobility and encumbrance contexts (when the user's hands are busy). We studied the effect of mobility and encumbrance on common content navigation techniques and found a significant decrease in performance as the pace of mobility increases or when the user was encumbered with busy hands. Based on these initial findings, we proposed a design space which would improve efficiency when navigation techniques, such as panning and zooming, are employed in mobility contexts. Our results reveal that our design space can effectively be used to create novel interaction techniques that improve smartwatch content navigation in mobility and encumbrance contexts.
Admixed Portrait: Design to Understand Facebook Portrayals in New Parenthood

We report on a design-led study of the photographic representation of self and family on Facebook during and after becoming parents for the first time. Our experience-centered, research-through-design study engaged eight participants across five UK homes, in a month-long deployment of a prototype technology -- a design research artifact, Admixed Portrait, that served to prompt participant reflection on first-time parenthood. In addition to pre- and post-deployment interviews, participants kept diaries capturing personal reflections during the deployment, on daily social media use and interactions with Admixed. Our qualitative insights on social media representations of transitional experience and identity for new parents, reveal how their online 'photowork' related to self-expression and social functioning. We contribute design considerations for developing tools to support photographic expression in social media use, and methodological insights about design-led inquiry for understanding transitional experiences.
A machine vision based pistachio sorting using transferred mid-level image representation of Convolutional Neural Network

Convolutional neural networks have proved to be prominent in various fields of machine vision and image classification. Although it necessitates a large-scale dataset for promising performance, the mid-level representation of these networks can be exploited for specified tasks with smaller annotated image dataset. To this end, by evaluating the generality-specificity of the desired layer as a feature extractor layer, the parameters of Convolutional Neural Networks learned on massive-size dataset like ImageNet can be transferred to a new model. In this study, the images of different sort of pistachios including trashes have been acquired to feed into a new model using a support vector classifier. The ultimate goal of our machine vision system is to separate the desired open-shell pistachios from other defected pistachios as well as trashes. For image segmentation, we use active contour method to detect objects and form both new images of each object type and their augmented images. Since our dataset is not large-scale compared to ImageNet classes, a feature reduction method is performed after the feature extractor layer of pre-trained Convolutional Neural Network. The results show the better performance of the proposed approach in detection of desired-formed pistachio facing unseen test set of images compared to basic approaches.