Miracle-dz/UniFAR-FTUs huggingface dataset:
Potassium Ion Selective Electrode Using Polyaniline and Matrix-Supported Ion-Selective PVC Membrane

Solid-state potassium ion selective electrode (K<sup>+</sup> ISE) has been the most studied chemical sensors due to its practical importance in biomedical applications. One of the major obstacles that prevented widespread use of solid-state K<sup>+</sup> ISE has been output potential drift problem. In this paper, we developed an electrochemical sensing unit in which working, counter, and reference electrodes are integrated in a single plane as all-solid-state form. In order to mitigate the output potential drift, a polyaniline intermediate layer and salt-saturated polyvinylebutyral top coating are introduced in the working and reference electrodes, respectively. Using cyclic voltammetry (CV), uniform layers of polyaniline are deposited on carbon electrode, as confirmed by scanning electron microscope observation. Potentiometry and electrochemical impedance spectroscopy measurement on our K<sup>+</sup> ISE show high sensitivity (60.5 mV/decade), low concentration for the limit of detection (<inline-formula> <tex-math notation="LaTeX">$10^{-5.8}$ </tex-math></inline-formula> M), and large range of linear detection (<inline-formula> <tex-math notation="LaTeX">$10^{-5}-1$ </tex-math></inline-formula> M), and superior selectivity of K<sup>+</sup> ISE against NH<sub>4</sub><sup>+</sup>, Na<sup>+</sup>, Mg<sup>2+</sup>, Ca<sup>2+</sup>, and Fe<sup>3+</sup>. With its high potential to be miniaturized, we foresee that our solid-state K<sup>+</sup> ISE will motivate the future applications in microdevices for clinical analysis, agricultural, and environmental applications.
Surge Withstand Capability of Parallel-connected Metal Oxide Varistors

Metal oxide varistors (MOVs) composed of multiple MOVs connected in parallel have benefits, including compactness and the potential to realize high surge withstand capability. On the other hand, it is absolutely necessary to control the V-I characteristics of each MOV connected in parallel to ensure uniform current flow between the component MOVs. If this current is not uniform, the component MOV having low voltage will puncture and degrade at a lower surge current than expected. In this paper, we have studied the distribution of electric field and current on the surface of a three-parallel MOV using the finitedifference time-domain (FDTD) method. Additionally, real impulse testing was conducted to confirm the surge withstand capability of three-parallel MOV. From the obtained results, we have concluded that the surge withstand capability of the three-parallel MOV is not decreased from the desired 3X individual surge withstand capability if the variation of V1mAvoltage between each MOV component is less than 4%.
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization

We focus on the multi-label categorization task for short texts and explore the use of a hierarchical structure (HS) of categories. In contrast to the existing work using non-hierarchical flat model, the method leverages the hierarchical relations between the pre-defined categories to tackle the data sparsity problem. The lower the HS level, the less the categorization performance. Because the number of training data per category in a lower level is much smaller than that in an upper level. We propose an approach which can effectively utilize the data in the upper levels to contribute the categorization in the lower levels by applying the Convolutional Neural Network (CNN) with a fine-tuning technique. The results using two benchmark datasets show that proposed method, Hierarchical Fine-Tuning based CNN (HFT-CNN) is competitive with the state-of-the-art CNN based methods.
Superradiant Cancer Hyperthermia Using a Buckyball Assembly of Quantum Dot Emitters

With the emergence of nanomedicine, targeted hyperthermia has a high potential of becoming a first-line cancer treatment modality. However, hyperthermia needs to be precisely controlled to avoid damaging adjacent healthy tissues. Due to the uncontrollable transfer of heat from the tumor boundary to healthy tissues, it is extremely difficult to control the temperature increase. As a solution, in this paper, we propose using a superradiant emitter assembly to deliver an ephemeral and powerful thermal pulse to enhance cancer hyperthermia by reducing damage to healthy tissues. Our assembly comprises quantum dot emitters arranged in the shape of a buckyball. We obtain the criteria for our assembly to be superradiant and prove that it is possible to control the superradiance using an external electric field. We analytically obtain expressions for the assembly dynamics and conduct thermal studies using a simple breast cancer model constructed using experimental parameters. Our results indicate that using a series of superradiant pulses can enhance cancer hyperthermia by minimizing the damage to adjacent healthy tissues.
Exploring the Management of Government Social Media - Studies from the Facebook Fan Pages in Taiwan

This study investigated government management of and public involvement in social media pages to summarize the internal management, daily routines, and perceptions of government agencies toward social media management. Furthermore, this study analyzed the driving factors of the intention to use, actual usage behaviors, and suggestions and expectations of the public regarding their involvement in the social media pages operated by government agencies. Subsequently, to identify the key factors in government social media management and directions for improvement, a comparison was made between the management practice of government agencies' social media and people's experiences in using them. Through participant observations, in-depth interviews, and other data collection methods, this study examined examples in Facebook-the social media platform most frequently used by Taiwanese people and government agencies-to explore these research questions. The government has invested considerable resources in social media management, attracting a large number of people to follow and participate in the official social media operated by various government agencies. However, whether the stickiness of these social media can be maintained, whether they continue to attract more participants, and whether they can evolve with social media platforms as people's needs and expectations change remain to be investigated.
Rainbow: Adaptive Layout Optimization for Wide Tables

Popular column stores such as ORC and Parquet have been widely used in many Hadoop-oriented data analysis systems. With the effective column skipping and data compression functionalities provided by column stores, wide tables with hundreds or even thousands of columns are applied by many big data analysis applications to avoid the expensive distributed joins. We found that the performance of such systems can be further improved by optimizing the physical data layout to fit certain workloads and system settings. However, it is nontrivial to perform such optimization manually. In this demo, we present a data layout optimization tool called Rainbow, which leverages workload-driven layout optimization algorithms to adjust data layouts adaptively without intervening the previous data blocks that have been stored. We also provide a Web UI for users to interact with the layout optimization process. Furthermore, Rainbow is open sourced with an accompanying benchmark for performance evaluation of wide tables.
Variational Autoregressive Decoder for Neural Response Generation

Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation. However, existing CVAE-based models often generate responses from a single latent variable which may not be sufficient to model high variability in responses. To solve this problem, we propose a novel model that sequentially introduces a series of latent variables to condition the generation of each word in the response sequence. In addition, the approximate posteriors of these latent variables are augmented with a backward Recurrent Neural Network (RNN), which allows the latent variables to capture long-term dependencies of future tokens in generation. To facilitate training, we supplement our model with an auxiliary objective that predicts the subsequent bag of words. Empirical experiments conducted on Opensubtitle and Reddit datasets show that the proposed model leads to significant improvement on both relevance and diversity over state-of-the-art baselines.
Optimized Service Level Agreement Establishment in Cloud Computing

Nowadays, the access to a cloud computing environment is provided on-demand, offering transparent services to clients. Although the cloud allows an abstraction of the behavior of the infrastructure in the service providers (involving logical and physical resources), the Service Level Agreements (SLAs) fulfilment remains a challenge, because depending on the service demand and the system configuration, the providers may not be able to meet the clients requirements. In this way, mechanisms that take account of load balancing and resource provisioning algorithms to provide an efficient load distribution in the available resources are necessary. However, the studies in the literature do not effectively address the problem of the resource provisioning to meet clients requirements using optimization techniques, restricting the analysis to a limited set of objectives. This paper proposes algorithms to address the computational resource provisioning problem using optimization techniques on-the-fly. The techniques optimize the use of the resources available in the cloud infrastructure, aiming to fulfill the clients requirements defined in the SLAs, and ensuring the efficient use of resources.
Affordable Modular Autonomous Vehicle Development Platform

Road accidents are estimated to be the ninth leading cause of death across all age groups globally. 1.25 million people die annually from road accidents and Africa has the highest rate of road fatalities [1]. Research shows that three out of five road accidents are caused by driver-related behavioral factors [2]. Self-driving technology has the potential of saving lives lost to these preventable road accidents. Africa accounts for the majority of road fatalities andas such would benefit immensely from this technology. However, financial constraints prevent viable experimentation and research into self-driving technology in Africa. This paper describes the design of RollE, an affordable modular autonomous vehicle development platform. It is capable of driving via remote control for data collection and also capable of autonomous drivingusing a convolutional neural network. This system is aimed at providing students and researchers with an affordable autonomous vehicle to develop and test self-driving car technology.
Studying the dynamics of outdoor-indoor wireless links at 60 GHz.

In this article, we report exploratory results for typical first and second order statistics of the mm-wave channel in the presence of pedestrian traffic, when an outdoor base station serves an indoor user. We observe that the number of lobes at the subscriber unit allows exploiting the benefits of angular combiners. The classic theoretical models widely used to describe the average fade duration and level crossing rates in mobile links did not fit well to the data, as also observed at lower frequency bands. For the tested positions, the processing effort to align (automatically) the antenna beams should be no larger than 25 ms.
Bit-Level Disturbance-Aware Memory Partitioning for Parallel Data Access for MLC STT-RAM

Spin-transfer torque random access memory (STT-RAM) is one of the most promising emerging nonvolatile memory technologies suitable for substituting traditional memory due to its fascinating features, such as high density and low-leakage power. Multilevel cell (MLC) STT-RAM stores two or more bits in a single cell, which can boost data density. Memory partitioning is an efficient method to overcome the impediment of memory bandwidth restricting the speed of parallel data access. However, when applied to previous memory partitioning methods, MLC STT-RAM is still facing challenges. Since these methods have no respect for the issue of read disturbance, the computation performance is unsatisfactory. In this paper, we propose a bit-level disturbance-aware memory partitioning (BaMP) method for parallel data access on MLC STT-RAM. It can effectively eliminate the conflicts between read operations and check operations, which are caused by the read disturbance of STT-RAM, by scheduling the check operations to different time to avoid conflicts. Moreover, bit-level optimization strategies of check merging and check reducing are integrated into the BaMP flow to exploit the MLC STT-RAM structure. The evaluation results on an open data set show that our BaMP outperforms the state-of-the-art method in terms of bank number, storage overhead, performance, and searching speed.
Learning Better Internal Structure of Words for Sequence Labeling

Character-based neural models have recently proven very useful for many NLP tasks. However, there is a gap of sophistication between methods for learning representations of sentences and words. While, most character models for learning representations of sentences are deep and complex, models for learning representations of words are shallow and simple. Also, in spite of considerable research on learning character embeddings, it is still not clear which kind of architecture is the best for capturing character-to-word representations. To address these questions, we first investigate the gaps between methods for learning word and sentence representations. We conduct detailed experiments and comparisons on different state-of-the-art convolutional models, and also investigate the advantages and disadvantages of their constituents. Furthermore, we propose IntNet, a funnel-shaped wide convolutional neural architecture with no down-sampling for learning representations of the internal structure of words by composing their characters from limited, supervised training corpora. We evaluate our proposed model on six sequence labeling datasets, including named entity recognition, part-of-speech tagging, and syntactic chunking. Our in-depth analysis shows that IntNet significantly outperforms other character embedding models and obtains new state-of-the-art performance without relying on any external knowledge or resources.
Supporting Remote Real-Time Expert Help: Opportunities and Challenges for Novice 3D Modelers

We investigate how novice 3D modelers can remotely leverage real-time expert help to aid their learning tasks. We first carried out an observational study of remote novice-expert pairs of 3D modelers to understand traditional chat-based assistance in the context of learning 3D modeling. Next, we designed MarmalAid, a web-based 3D modeling tool with a novel real-time, in-context help feature that allows users to embed real-time chat conversations at any location within the 3D geometry of their models. Our user study with 12 novices who used both MarmalAid's real-time, in-context chat and an external chat tool to seek help, showed that novices found the real-time, in-context chat to be more useful and easier to use, and that experts asked for fewer clarifications, allowing the novices to ask more task-related questions. Our findings suggest to several design opportunities to utilize and extend the real-time, in-con-text heln concept in 3D modeline aoolications and bevond.
Large-Scale Gesture Recognition With a Fusion of RGB-D Data Based on Saliency Theory and C3D Model

Gesture recognition has raised wide attention in computer vision owing to its many applications. However, the task of video-based large-scale gesture recognition yet faces many challenges, since many gesture-irrelevant factors like the background may disturb the recognition accuracy. To better recognize gestures with large-scale videos, we propose a method based on RGB-D data in this paper, where the “RGB-D” means RGB and depth data captured simultaneously by specific devices like Kinect. To learn gesture details better, we first use an adaptive frame unification strategy to unify the frame number of inputs, and then the RGB and depth data are sent to the C3D model to extract spatiotemporal features, respectively. In order to alleviate the interference of gesture-irrelevant factors, the saliency theory is also employed to generate auxiliary data. Next the features of these data are combined to boost the performance, which can also avoid unreasonable synthetic data, since the dimension of C3D features is uniform. Finally the performances of several classifiers are tested and the best one of SVM classifier is selected to output the ultimate accuracy. Our approach achieves 52.04% and 59.43% accuracy on the validation and testing subset of the Chalearn LAP IsoGD, respectively, both of which outperform our results in the chalearn LAP Large-scale Gesture Recognition Challenge as reported in ICPR 2016.
Modelling an Interior Permanent Magnet Traction Motor Based on Current Signals Produced in a Space Vector Modulation

This paper addresses the development and application of real-life inverter borne three phase current waveforms into a motor design simulation software such as Motor Cad. The proposed current signals are calculated in Matlab simulation based on the space vector modulation (SVM) technique in a field oriented control (FOC) algorithm. The created pulse width modulation signals are applied to a three phase conventional inverter to control the Nissan Leaf traction motor, where the magnetic non-linearity has been taken into account. The performance of the motor based on the implemented control input signals is evaluated over three torque-speed operating points and based on the machine efficiency map. The results suggest that the simulated torque, current and voltage waveforms as a result of the proposed control model are close to the practical environment where the voltage constraints and switching harmonics of the power inverter must all be considered.
Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN

Rapid growth of multi-modal documents on the Internet makes multi-modal summarization research necessary. Most previous research summarizes texts or images separately. Recent neural summarization research shows the strength of the Encoder-Decoder model in text summarization. This paper proposes an abstractive text-image summarization model using the attentional hierarchical Encoder-Decoder model to summarize a text document and its accompanying images simultaneously, and then to align the sentences and images in summaries. A multi-modal attentional mechanism is proposed to attend original sentences, images, and captions when decoding. The DailyMail dataset is extended by collecting images and captions from the Web. Experiments show our model outperforms the neural abstractive and extractive text summarization methods that do not consider images. In addition, our model can generate informative summaries of images.
Unsupervised Bilingual Lexicon Induction via Latent Variable Models

Bilingual lexicon extraction has been studied for decades and most previous methods have relied on parallel corpora or bilingual dictionaries. Recent studies have shown that it is possible to build a bilingual dictionary by aligning monolingual word embedding spaces in an unsupervised way. With the recent advances in generative models, we propose a novel approach which builds cross-lingual dictionaries via latent variable models and adversarial training with no parallel corpora. To demonstrate the effectiveness of our approach, we evaluate our approach on several language pairs and the experimental results show that our model could achieve competitive and even superior performance compared with several state-of-the-art models.
A New Method for Diaphragmatic Maximum Relaxation Rate Ultrasonographic Measurement in the Assessment of Patients With Diaphragmatic Dysfunction

Measurements of ultrasound diaphragmatic motion, amplitude, force, and velocity of contraction may provide important and essential information about diaphragmatic fatigue, weakness, or paralysis. In this paper, we propose and evaluate a semi-automated analysis system for measuring the diaphragmatic motion and estimating the maximum relaxation rate (MRR_SAUS) from ultrasound M-mode images of the diaphragmatic muscle. The system was evaluated on 27 M-mode ultrasound images of the diaphragmatic muscle [20 with no resistance (NRES) and 7 with resistance (RES)]. We computed semi-automated ultrasound MRR measurements on all NRES/RES images, using the proposed system (MRR_SAUS = 3.94 ± 0.91/4.98 ± 1.98 [1/s]), and compared them with the manual measurements made by a clinical expert (MRR_MUS = 2.36 ± 1.19/5.8 ± 2.1 [1/s],) and those made by a reference manual method (MRR_MB = 3.93 ± 0.89/3.73 ± 0.52 [1/sec], performed manually with the Biopac system. MRR_SAUS and MRR_MB measurements were not statistically significantly different for NRES and RES subjects but were significantly different with the MRR-MUS measurements made by the clinical expert. It is anticipated that the proposed system might be used in the future in the clinical practice in the assessment and follow up of patients with diaphragmatic weakness or paralysis. It may thus potentially help to understand post-operative pulmonary dysfunction or weaning failure from mechanical ventilation. Further validation and additional experimentation in a larger sample of images and different patient groups is required for further validating the proposed system.
Single-phase Solid-State Transformer using Multi-cell with Automatic Capacitor Voltage Balance Capability

In this paper, a single-phase solid-state transformer (SST) system based on a multilevel topology using multi-cell is proposed. The proposed SST has an automatic capacitor voltage balancing capability on a primary side due to use of a resonant DC-DC converter. The main contribution of this paper is revealing the fundamental loss design of the proposed topology connected with a 6.6-kV grid. It is predicted that the maximum efficiency of full model SST reaches 99%. The miniature model SST is tested to confirm the fundamental operation with an input voltage of 1320 V, which is 1/5 of the full model. As a result, the sinusoidal input current is obtained with a total harmonic distortion of 4.3%. Besides, the bidirectional operation is verified. Then, it is confirmed that the primary side capacitor voltage of each cell is kept constant and balanced without a voltage balance control. Moreover, the loss analysis is derived in each part and compared with that of the experimental result. The error of the loss between the experimental result and the calculation is less than 5%.
Dione: A Framework for Automatic Profiling and Tuning Big Data Applications

In this demonstration we present Dione a novel framework for automatic profiling and tuning big data applications. Our system allows a non-expert user to submit Spark or Flink applications to his/her cluster and Dione automatically determines the impact of different configuration parameters on the application's execution time and monetary cost. Dione is the first framework that exploits similarities in the execution plans of different applications to narrow down the amount of profiling runs that are required for building prediction models that capture the impact of the configuration parameters on the metrics of interest. Dione exploits these prediction models to tune the configuration parameters in a way that minimizes the application's execution time or the user's budget. Finally, Dione's Web-UI visualizes the impact of the configuration parameters on the execution time and the monetary cost, and enables the user to submit the application with the recommended parameters' values.