Miracle-dz/UniFAR-FTUs huggingface dataset:
Locality-preserving L1-graph and its application in clustering

Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. Recently, a nonparameteric graph construction method called L1-graph is proposed with claimed advantages on sparsity, robustness to data noise and datum-adaptive neighborhood. However, it suffers a lot from the loss of locality and the instability of performance. In this paper, we propose a Locality-Preserving L1- graph (LOP-L1), which preserves higher local-connections and at the same time maintains sparsity. Besides, compared with L1-graph and the succeeding regularization-based techniques, our LOP-L1 requires less amount of running time in the scalability test. We evaluate the effectiveness of LOP-L1 by applying it to clustering application, which confirms that the proposed algorithm outperforms related methods.
Hierarchical Reinforcement Learning for Real-Time Strategy Games

Real-Time Strategy (RTS) games can be abstracted to resource allocation applicable in many fields and industries. We consider a simplified custom RTS game focused on mid-level combat using reinforcement learning (RL) algorithms. There are a number of contributions to game playing with RL in this paper. First, we combine hierarchical RL with a multi-layer perceptron (MLP) that receives higher-order inputs for increased learning speed and performance. Second, we compare Q-learning against Monte Carlo learning as reinforcement learning algorithms. Third, because the teams in the RTS game are multi-agent systems, we examine two different methods for assigning rewards to agents. Experiments are performed against two different fixed opponents. The results show that the combination of Q-learning and individual rewards yields the highest win-rate against the different opponents, and is able to defeat the opponent within 26 training games.
The Rapidly Changing Landscape of Conversational Agents

Conversational agents have become ubiquitous, ranging from goal-oriented systems for helping with reservations to chit-chat models found in modern virtual assistants. In this survey paper, we explore this fascinating field. We look at some of the pioneering work that defined the field and gradually move to the current state-of-the-art models. We look at statistical, neural, generative adversarial network based and reinforcement learning based approaches and how they evolved. Along the way we discuss various challenges that the field faces, lack of context in utterances, not having a good quantitative metric to compare models, lack of trust in agents because they do not have a consistent persona etc. We structure this paper in a way that answers these pertinent questions and discusses competing approaches to solve them.
The Effect of Sokka Clay on the Tensile and Burning Properties of rPP/Clay Composite

Materials engineering, in order to achieve weight reduction and cost saving, was widely developed using plastic-based composite materials, like polypropylene (PP). As the large consumption of PP, the recycled polypropylene (rPP) was potential for producing rPP-based composites for car body panels. However, it had to have high mechanical and burning properties, and it can be achieved using clay particle as filler, like local clay from Sokka Kebumen Indonesia. The clay particle was made from rejected Sokka tile pocessed using a grinding machine and sieved for several sizes (≤74μm; 74-100 μm; 100-125 µm). The mixure of rPP/clay were pressed using a hotpress for producing the composite and its specimens were tested using Universal Testing Machine and horizontal burning test apparatus, and the fracture surfaces were observed using SEM. The enhancing of tensile and burning properties of rPP/clay composite can be performed by using the addition of Sokka-clay and smaller particle of clay. The rPP/clay composite was competitive with the commercial panel of car interior and potential to be applied as car panel material.
Name List Only? Target Entity Disambiguation in Short Texts

Target entity disambiguation (TED), the task of identifying target entities of the same domain, has been recognized as a critical step in various important applications. In this paper, we propose a graphbased model called TremenRank to collectively identify target entities in short texts given a name list only. TremenRank propagates trust within the graph, allowing for an arbitrary number of target entities and texts using inverted index technology. Furthermore, we design a multi-layer directed graph to assign different trust levels to short texts for better performance. The experimental results demonstrate that our model outperforms state-of-the-art methods with an average gain of 24.8% in accuracy and 15.2% in the F1-measure on three datasets in different domains.
Ferroelectric Switching and d33 Mapping of Micro-Patterned Piezoelectrics by Piezo Force Microscopy

The domain configurations and piezoelectric coefficient of ferroelectric materials such as PZT and PMN-PT thin films can have a significant effect on the optimization of future electronic devices. Piezo Force Microscopy (PFM) is an ideal tool based on Atomic Force Microscopy that allows unique investigations of such nanoscale effects, and can further be implemented to monitor domain switching dynamics. Utilizing PFM, the domain orientations as well as switching dynamics can be tracked in-situ. As the lateral dimension plays an important role in ferroelectric properties since it influences inplane strain, both normal and lateral domain orientations are uniquely mapped simultaneously. Leveraging a new method for fabricating ferroelectric mesas down to the nanoscale, coercive fields and switching kinetics have thereby been investigated for continuous strained films as well as geometrically strain-relieved samples. A piezoelectric enhancement at edges of engineered nano islands is directly observed, while switching activation energies have been calculated and related to the strain-relief behavior. Piezoelectric coefficients in the surface normal direction were precisely measured on 2 μm, 1 μm and 0.75 μm wide PMN-PT microfabricated structures. These revealed piezoelectric enhancements up to 500 nm from feature edges for 2 μm or wider structures, and an increasing overall enhancement throughout smaller 1 μm and 0.75 μm structures as the strain relief Linghan Ye – University of Connecticut, 2016 becomes more complete. These results are corroborated by X-RAY diffraction and dielectric measurements from collaborators. The enhancement of dielectric and piezoelectric properties for geometrically strain relieved structures should furthermore be applicable to in-plane piezoactuation. Accordingly, lateral piezoactuation for PMN-PT microstructures is recorded for 4 μm and 2 μm features revealing direction-dependent edge enhancement as hypothesized. Such insights are crucial for ferroelectric strain engineering efforts, the development of new device mechanisms, and their ultimate performance.
A Remote Home Monitoring System to Support Informal Caregivers of People with Dementia

Informal caregivers of people with dementia have a high risk of becoming overburdened. Health informatics for aging in place can provide them support by deploying unobtrusive remote home monitoring systems to assess real-time events and monitor changes in the behavior of the person with dementia (PwD). In this paper, we describe the concept, development, and evaluation of an intelligent remote Home Monitoring System (HMS) that provides support to informal caregivers by giving key information related to the health and independent living of the PwD. The HMS consists of a Sensor System that monitors low-level behaviors of the PwD, a Decision Support System that translates this into high-level behaviors, and a connected Smartphone Application that allows the caregiver to receive notifications, review behavioral information at a glance, and facilitates the collaborative care process between informal caregivers. The final HMS prototype was evaluated and scored high in terms of usability and quality of the Smartphone Application. The Sensor System showed no significant flaws during testing, and the Decision Support System is considered a viable proof of concept. The next step is to evaluate the HMS in a real-life setting in terms of offering peace of mind and reducing the burden of care.
Building IoT-Enabled Wearable Medical Devices: An Application to a Wearable, Multiparametric, Cardiorespiratory Sensor

Recent developments in personal and mobile healthcare have shown promising results in term of patients' quality of life and quality of care improvements. This can be achieved through continuous monitoring of patients' physiological functions using wearable non-invasive biomedical sensors. The remote collection and processing of such data can then be used to provide rapid medical response if a problem is detected or to offer preventive measures. However, the integration of wearable sensors into wider-scale framework is still a major challenge, as real-time data collection and remote configuration capabilities must be integrated to strongly constrained devices. Here, we show how such requirements can be integrated into a multiparameter, cardiorespiratory wearable sensor and how this sensor can be integrated into wide-scale Internet-based frameworks. We thus manufactured a biomedical-grade heart rate, instantaneous heart-rate variability and respiratory sensor. The sensor was tested in real life ambulatory condition, and we showed an Internet-based proof of concept exhibiting the integration of our sensor into wide-scale healthcare frameworks. Finally, we anticipate that wearable healthcare will greatly improve patients' quality-of-life by using IoT-based wearable devices similar to the sensor developed in this paper.
MECHANICAL PROPERTIES AND SURFACE CHARACTERIZATION OF SS ORTHODONTIC WIRE FOLLOWING FLUORIDATED AND NON- FLUORIDATED TREATMENT

Objective: To study the effect of prophylactic fluoride agents on the loading and unloading mechanical properties and surface quality of Stainless Steel orthodontic wires. Methods: SS wires were immersed in either an acidulated fluoride agent, Amine fluoride agent, Chlorhexidine or Artificial Saliva (control) for 1.5 hours at 37 0 c. After immersion, the loading and unloading elastic modulus and yield strength of the wires were measured using a 3-point bend test. A one-way analysis of variance and Turkey post hoc test were used to analyze the mechanical testing data. Scanning electron microscopy was also used to qualitatively evaluate the wire topography as a function of the fluoride treatments. Results: Unloading mechanical properties of SS wires were significantly decreased (P < .05) after exposure to both fluoride agents. Corrosive changes in surface topography were also observed after exposure to both the Amine and acidulated phosphate fluoride agents. Conclusions: The results suggest that using topical fluoride agents with SS wire could decrease the functional unloading mechanical properties of the wires and potentially contribute to prolonged orthodontic treatment.
Personalised PageRank for making recommendations in digital cultural heritage collections

In this paper we describe the use of Personalised PageRank (PPR) to generate recommendations from a large collection of cultural heritage items. Various methods for computing item-to-item similarities are investigated, together with representing the collection as a network over which random walks can be taken. The network can represent similarity between item metadata, item co-occurrences in search logs, and the similarity of items based on linking them to Wikipedia articles and categories. To evaluate the use of PPR, search logs from Europeana are used to simulate user interactions. PPR on each information source is compared to a standard retrieval-based baseline, resulting in higher performance.
Doing it with a glass onion: Investigating affordances provided by a head-mounted augmented reality immersive device for the real-time online supervision of experimental learning

The focus of this work was to assess the affordances provided by a head-mounted augmented reality immersive device for the real-time online supervision of experimental learning for off-campus engineering student experimentation. The data collection involved recordings of first-year electronics laboratory classes where students, under real-time face-to-face and online supervision, carried out their experiments using real components and test instruments. The identification of kikan-shido events in the collected data was used to find the affordances that were present in both the face-to-face and the remotely supervised experimental sessions.
The medial temporal lobe functional connectivity patterns associated with forming different mental representations

The medial temporal lobes (MTL), and more specifically the hippocampus, are critical for forming mental representations of past experiences—autobiographical memories—and for forming other “nonexperienced” types of mental representations, such as imagined scenarios. How the MTL coordinate with other brain areas to create these different types of representations is not well understood. To address this issue, we performed a task‐based functional connectivity analysis on a previously published dataset in which fMRI data were collected as participants created different types of mental representations under three conditions. One condition required forming and relating together details from a past event (autobiographical task), another required forming and relating together details of a spatial context (spatial task) and another condition required relating together conceptual/perceptual features of an object (conceptual task). We contrasted the connectivity patterns associated with a functionally defined region in the parahippocampal cortex (PHC) and anatomically defined anterior and posterior hippocampal segments across these tasks. Examining PHC connectivity patterns revealed that the PHC seed was distinctly connected to other MTL structures during the autobiographical task, to posterior parietal regions during the spatial task and to a distributed network of regions for the conceptual task. Examining hippocampal connectivity patterns revealed that the anterior hippocampus was preferentially connected to regions of default mode network during the autobiographical task and to areas implicated in semantic processing for the conceptual task whereas the posterior hippocampus was preferentially connected to medial‐posterior regions of the brain during the spatial task. We interpret our findings as evidence that there are MTL‐guided networks for forming distinct types of mental representations that align with functional distinctions within the hippocampus.
Automatic Programming of VST Sound Synthesizers Using Deep Networks and Other Techniques

Programming sound synthesizers is a complex and time-consuming task. Automatic synthesizer programming involves finding parameters for sound synthesizers using algorithmic methods. Sound matching is one application of automatic programming, where the aim is to find the parameters for a synthesizer that cause it to emit as close a sound as possible to a target sound. We describe and compare several sound matching techniques that can be used to automatically program the Dexed synthesizer, which is a virtual model of a Yamaha DX7. The techniques are a hill climber, a genetic algorithm, and three deep neural networks that have not been applied to the problem before. We define a sound matching task based on six sets of sounds, which we derived from increasingly complex configurations of the Dexed synthesis algorithm. A bidirectional, long short-term memory network with highway layers performed better than any other technique and was able to match sounds closely in 25% of the test cases. This network was also able to match sounds in near real time, once trained, which provides a significant speed advantage over previously reported techniques that are based on search heuristics. We also describe our open source framework, which makes it possible to repeat our study, and to adapt it to different synthesizers and algorithmic programming techniques.
Pool boiling on nano-finned surfaces

Pool Boiling on Nano-Finned Surfaces. (December 2007) Sharan Ram Sriraman, B.E. , Hindustan College of Engineering, India Chair of Advisory Committee: Dr. Debjyoti Banerjee The effect of nano-structured surfaces on pool boiling heat transfer is explored in this study. Experiments are conducted in a cubical test chamber containing fluoroinert coolant (PF5060, Manufacturer: 3M Co.) as the working fluid. Pool boiling experiments are conducted for saturation and subcooled conditions. Three different types of ordered nano-structured surfaces are fabricated using Step and flash imprint lithography on silicon substrates followed by Reactive Ion Etching (RIE) or Deep Reactive Ion Etching (DRIE). These nano-structures consist of a square array of cylindrical nanofins with a longitudinal pitch of 1 mm, transverse pitch of 0.9 mm and fixed (uniform) heights ranging from 15 nm – 650 nm for each substrate. The contact angle of de-ionized water on the substrates is measured before and after the boiling experiments. The contact-angle is observed to increase with the height of the nano-fins. Contact angle variation is also observed before and after the pool boiling experiments. The pool boiling curves for the nano-structured silicon surfaces are compared with that of atomically smooth single-crystal silicon (bare) surfaces. Data processing is performed to estimate the heat flux through the projected area (plan area) for the nano-patterned zone as well as the heat flux through the total nano-patterned area, which includes the
Effect of the duration of hospice and palliative care on the quality of dying and death in patients with terminal cancer: A nationwide multicentre study

Early referral to hospice and palliative care (HPC) has significant benefits, but little is known about the appropriate time for referral. The purpose of this study of terminal cancer patients was to identify the most appropriate time for referral to HPC. Cross-sectional correlation study design was used. Participants were the bereaved relatives, who were the adult primary caregivers of the 1,829 terminal cancer patients who died 2-6 months previously in nationwide centres that provide HPC in Korea. A post-bereavement survey (Good Death Inventory, GDI) of family caregivers was used to assess patients' quality of dying and death. Relative to patients who were in HPC for 3-7 days and HPC for 8-21 days, those in HPC for 22-84 days had significantly higher quality of dying. Propensity score matched comparison between the group hospitalised for 22-84 days (n = 65) and the group hospitalised for 85 days or longer (n = 65) showed no significant differences in all the items on quality of dying and death. Our results suggest that terminal cancer patients who stay in HPC at least for 22 days have improved quality of dying and death.
Predicting severity of bug report by mining bug repository with concept profile

Recently, for large scale software projects, developers rely on bug reports for corrective software maintenance. The severity of a reported bug is an important feature to decide how fast it needs to be fixed. Therefore, to arrange a new submitted bug to an appropriate fixer, it is necessary to recognize the severity of each bug report. Unfortunately, reporters need to decide the severity of bugs manually. Even if there are guidelines on how to verify the severity of a bug, it is still a time-consuming work. Utilizing the concept profiles by mining bug repositories is a good way to resolve this problem. In this paper, we propose a concept profile-based prediction technique to assign the severity of a given bug. In detail, we analyze historical bug reports in the bug repositories and build the concept profiles from them. We evaluate the performance of our method on the bug reports from the bug repositories of popular open-source projects that include Eclipse and Mozilla Firefox, the result shows that the proposed technique can effectively predict the severity of a given bug.
The Implications of Experimental Design for Biodiversity Manipulations

I report a simulation study that tested the ability of a variety of experimental designs to achieve two fundamental goals: (1) to determine the association between loss of biological diversity and responses such as ecosystem functioning and (2) to determine which components of biodiversity, such as number of species, functional diversity, or a keystone species, were most responsible for that association. For the goal of reliably detecting an overall association, all designs I tested performed well and were unlikely to misidentify predominant patterns. Thus, this study affirms the common conclusion of many published biodiversity experiments that loss of biological diversity is often associated with a reduction in ecosystem functioning. However, for the goal of identifying the components of biodiversity that are most responsible for the effects, designs differed markedly. Some designs performed well in detecting number‐of‐species effects but poorly in detecting effects of unique species or functional groups. No design tested was able to discriminate both numeric effects and compositional effects in all situations. Thus, this study demonstrates that interpreting results about mechanisms from biodiversity experiments will be critically dependent on an experiment's design.
GeoMantis: Inferring the Geographic Focus of Text using Knowledge Bases

We consider the problem of identifying the geographic focus of a document. Unlike some previous work on this problem, we do not expect the document to explicitly mention the target region, making our problem one of inference or prediction, rather than one of identification. Further, we seek to tackle the problem without appealing to specialized geographic information resources like gazetteers or atlases, but employ general-purpose knowledge bases and ontologies like ConceptNet and YAGO. We propose certain natural strategies towards addressing the problem, and show that the GeoMantis system that implements these strategies outperforms an existing state-of-the-art system, when compared on documents whose target region (country, in particular) is not explicitly mentioned or is obscured. Our results give evidence that using general-purpose knowledge bases and ontologies can, in certain cases, outperform even specialized tools.
Attenuating the impact of limb position on surface EMG pattern recognition using a mixed-LDA classifier

Surface electromyography (sEMG) signals have been applied in the control of prosthetic hand over the past decades. With many control schemes and algorithms being proposed, pattern recognition (PR) based control strategies have become a mainstream approach for the manipulation of dexterous prostheses. However, there is still a huge gap between practical usage and controlled laboratory conditions due to electrode shift, consistence during contraction, impedance variation and so on. Among these challenges, the variation of limb position between training session and test session is an important issue which will have bad impact on the motion classification performance. In this paper, we investigated the impact of limb position variation on the PR based motion classification with a linear discrimination analysis (LDA) classifier. Experiments were carried out in five different limb positions and seven classes of hand motion in each position. In order to reduce the impact, a mixed-LDA classifier which fused the LDA parameters of other positions was implemented. The results revealed that mixed-LDA classifier performed better than single-set classifier with a 93.6% classification accuracy over the five limb positions. The outcome of this study suggests that mixed-LDA method may be a promising way to attenuate the adverse impact of limb position variation.