Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural and electronic properties of sublimated graphene on silicon carbide: A First Principle study

Graphene grown on silicon carbide by Si sublimation is a promising technique where large scale graphene production is possible. Work on sublimated graphene is largely circled around the production technique having a lack study of its inner electronic properties. In this paper a comparative investigation of different properties of sublimated graphene on silicon carbide is performed using Density Functional Theory first principle calculations. For analyzing practical growth condition and properties one atom vacancy defect and N doping is also included in the sublimated graphene layer. Band structure and density of states have been calculated for all three structures including Van der Waals interactions. Difference in band gap from $(100 \sim 130)$ meV at dirac point have been observed for each structure. Comparison of Density of States provides significant prove of its dependency on carrier concentration when 6.67 dopant is added. Charge density have been calculated to have better insight on structural characteristics. These investigation of properties provides significant promises for practical use of graphene in device fabrications.
An improved image restoration algorithm based on the multi-dictionaries

Sparse representation is a useful and wonderful method in the field of image restoration. Compared with the traditional image restoration algorithm, the image restoration algorithm that is based on the sparse representation can improve the quality of restoration effectively. However, it is difficult to express complicated features of an image with one single dictionary, resulting in discrepancies of massive structure. This paper proposes a novel image restoration algorithm based on the multi-dictionaries that combines image clustering and dictionary training. The broken image is divided into a few blocks and generate an index matrix. And a Steering Kernel Regression Weight (SKRW) algorithm is applied for image clustering. To study and explore the inner structures of the image, blocks are sorted into a few categories based on similar SKRW values. And each category will have its own overcomplete dictionary by training with a K-singular Value Decomposition (K-SVD) algorithm. Compared with using one dictionary to express the whole image, multi-dictionaries are matched with different image structures, therefore the image will have a more accurate sparse representation. The dictionaries and sparse coefficients are updated until achieving convergence and the image can be restored after replacing the patches. Experimental results show that our improved algorithm can restore images damaged by noise or scratches more effective than traditional algorithms.
Cooperative Coevolutionary Approximation in HOG-based Human Detection Embedded System

The histogram of oriented gradients (HOG) feature extraction is a computer vision method widely used in embedded systems for detection of objects such as pedestrians. We used cooperative coevolutionary Cartesian genetic programming (CGP) to exploit the error resilience in the HOG algorithm. We evolved new approximate implementations of the arctan and square root functions, which are typically employed to compute the gradient orientations and magnitudes. When the best evolved approximations are integrated into the software implementation of the HOG algorithm, not only the execution time, but also the classification accuracy was improved in comparison with approximations evolved separately using CGP and also compared to the state-of-the art approximate implementations. As the evolved code does not contain any loops and branches, it is suitable for the follow-up low-power hardware implementation.
Improving Neural Machine Translation with Neural Sentence Rewriting

A complex expression is more difficult for machine translation than a simplified sentence. One efficient method to handle this problem is to rewrite the source text into a simplified version before translation while keeping its meaning. In this paper, we propose a novel method to automatically rewrite source sentences based on neural machine translation. We propose a round-trip machine translation method to automatically generate a large amount of high quality rewritten pairs from bilingual corpus and then build an end-to-end sentence rewriting system based on neural network. Experimental results on Chinese-English IWSLT translation tasks show that our method leads to significant improvements over a strong baseline system.
Particle Swarm Optimized Reinforcement Learning-Based Fault Tolerant Control

Data-driven fault-tolerant controllers have recently gained considerable attention due to their important role in modern technological systems. The reinforcement learning is an attractive tool of data-driven but it is limited to the learning speed. A particle swarm optimization is proposed to improve the learning speed of reinforcement learning by transferring the series computation to parallel operation. The numeric simulation shows it is a feasible approach.
Language Resource Extension for Indonesian-Chinese Machine Translation

Machine translation for low-resource languages faces a scientific problem of cross-language semantic paraphrasing in a lack of structured language resource. The exploration of this problem has important theoretical and application value and is also a challenging research hotspot at present. We address the specific low-resource machine translation issue from Indonesian to Chinese, propose a language resource extension method based on cognate parallel corpus, and train a modified neural machine translation (NMT) model by mixing parallel corpus from cognate language. This modified model achieved 20.30 BLEU4 score in the experiment of Indonesian-Chinese machine translation. The manual analysis after simple random sampling of experimental results finds that the effect of the modified NMT is comparable to that of the current Google translation. The experimental results prove that the cognate parallel corpus can improve the low-resource language NMT effectively, which mainly depends on the morphological similarity and semantic equivalence between the cognate languages.
Time-multiplexed System-on-Chip using Fault-tolerant Astrocyte-Neuron Networks

Spike-based brain-inspired systems have shown an immense capability to achieve internal stability, widely referred to as homeostasis. This ability enrols them as the best candidate for next-generation computational neuroscience as they bridge the gap between neuroscience and machine learning. Spiking Neural Networks (SNN), a third generation Artificial Neural Network (ANN), which operates using discrete events of spikes, contributes to a category of biologically-realistic models of neurons to carry out computations. Spiking Astrocyte-Neuron Networks (SANN) have a characteristic attribute homologous to brain self-repair. Although SNNs are more powerful in theory than 2nd generation ANNs, they are not widely in use as their implementations on normal hardware are computationally-intensive. On the contrary, due to the capability of modern hardware such as FPGAs, which operates in MHz and GHz range, facilitates real-time and faster-than-real-time simulations of SNNs. In this work, we overcome the computational overhead of the SNNs using the benefits of real-time hardware computations, utilizing time-multiplexing to design a Self-rePairing spiking Astrocyte Neural NEtwoRk (SPANNER) chip, generic to users‘ choice of task, emphasizing fault-tolerance, targeting safety-critical applications. We demonstrate the proposed methodology on a SANN system implemented on Xilinx Artix-7 FPGA. The proposed architecture has minimal hardware footprints, power dissipation profile and real-time computational capability, enhancing its usability in constrained applications.
HGLPSO: Hybrid Genetic Learning PSO and its Applications to Task Matching on Large-Scale Systems

Matching tasks to be executed with proper resources is essential for improving the performance of grid systems. Assigning a set of tasks to a set of heterogeneous resources is challenging and becomes more complicated when the number of tasks and resources increases. This problem is known as thetask matching problem and is an NP-hard problem. Swarm Intelligence (SI) methods have been adopted as a solution to this problem. One such algorithm is particle swarm optimization (PSO); however, PSO tends to get stuck at local optima in such complex problems. This paper introduces a hybrid genetic learning PSO (HGLPSO) algorithm for the task matching problem in the grid environment. HGLPSO incorporates two genetic learning schemes to create candidate solutions (exemplars). Accordingly, the resulting exemplars possess the right balance of exploration and exploitation search abilities to direct the particles in the search space. The results demonstrate the effectiveness and efficiency of HGLPSO compared with other PSO variants in a heterogeneous grid environment.
Database Binning and Retrieval in Multi-Fingerprint Identification Systems

The increasingly large scale of deployed biometric systems necessitates approaches for computational workload reduction in order to perform identification queries efficiently. Simple database binning based on classification of features in biometric samples is amongst the most frequently used and researched methods for achieving said goal. However, multi-instance database binning appears to be a neglected topic in the scientific literature: best to the authors’ knowledge, for fingerprints there exists only one, entirely theoretical, study on this subject. In this paper, we propose a retrieval algorithm based on multi-instance binning of fingerprint databases, along with usage of statistical information on fingerprint classes and their correlations. The aforementioned statistics are obtained from NIST SD9 database and data obtained from the German Federal Criminal Police Office. Subsequently, the experimental evaluation of the proposed algorithm is performed on the NIST SD9 database. The proposed system is evaluated using a classifier based on the PCASYS tool and neuronal networks. The results show a significant workload reduction from a baseline exhaustive search scenario – down to 12.7% for this particular classifier and 5.8% for a theoretical perfect (completely accurate) classifier. The proposed method could be seamlessly integrated into operational systems, as it relies on well-established features and compatibility with the current acquisition methods.
Classifying Temporal Relations Between Events by Deep BiLSTM

Neural networks illustrate their advantages in comparison with traditional classifier-based methods for event temporal relation classification. However, most of them may not be able to explore the deep semantic representation in the larger hypothesis space because of the shallow architectures (e.g., one-layer CNN or RNN). To address this issue, we propose to use deep bidirectional long short-term memory networks (DBiLSTMs) to classify event temporal relations in this paper, where we concatenate the outputs of all prior layers together as the input for the subsequent layer. The experimental results on TimeBank-Dense and Richer Event Description indicate that the proposed DBiLSTMs has outstanding performance over the state-of-the-art methods.
Sharing Peace Engineering with US-based Minority Students, through the UN’s Sustainable Development Goals, in Peru

The University System of Maryland’s Louis Stokes Alliance for Minority Participation (LSAMP) shares the United Nations’ Sustainable Development Goals (SDGs) with underrepresented minority (URM) STEM students. In July 2018, faculty and students from three institutions within the USM LSAMP worked with World SPEED - the Student Platform for Engineering Education Development, to share the UN’s SDGs during the 2018 conference of the Latin and Caribbean Consortium of Engineering Institutions (LACCEI) in Lima, Peru. Students from the US, Peru, Colombia, Brazil, and other countries were exposed to the SDGs in both English and Spanish through World SPEED’s SDG-focused panel and team-based exercises. The exercises allowed students, working in teams that mixed countries (and languages), to develop solutions to challenges based on the SDGs. Students discussed research in the context of the SDGs, such as SDG 3: Better health solutions for people around the world, SDG 4: Global quality education, SDG 5: Gender equality, and SDG 7: Affordable and clean energy solutions that will allow humans to generate clean, reusable power. This project allowed URM students from the US to consider engineering in the context of "peace," contributing to STEM identity, retention, and utilization of their talents for a peaceful purpose.
DLoBD: A Comprehensive Study of Deep Learning over Big Data Stacks on HPC Clusters

Deep Learning over Big Data (DLoBD) is an emerging paradigm to mine value from the massive amount of gathered data. Many Deep Learning frameworks, like Caffe, TensorFlow, etc., start running over Big Data stacks, such as Apache Hadoop and Spark. Even though a lot of activities are happening in the field, there is a lack of comprehensive studies on analyzing the impact of RDMA-capable networks and CPUs/GPUs on DLoBD stacks. To fill this gap, we propose a systematical characterization methodology and conduct extensive performance evaluations on four representative DLoBD stacks (i.e., CaffeOnSpark, TensorFlowOnSpark, MMLSpark/CNTKOnSpark, and BigDL) to expose the interesting trends regarding performance, scalability, accuracy, and resource utilization. Our observations show that RDMA-based design for DLoBD stacks can achieve up to 2.7x speedup compared to the IPoIB-based scheme. The RDMA scheme also scales better and utilizes resources more efficiently than IPoIB. For most cases, GPU-based schemes can outperform CPU-based designs, but we see that for LeNet on MNIST, CPU + MKL can achieve better performance than GPU and GPU + cuDNN on 16 nodes. Through our evaluation and an in-depth analysis on TensorFlowOnSpark, we find that there are large rooms to improve the designs of current-generation DLoBD stacks.
A Sensing Framework for Indoor Spatial Awareness for Blind and Visually Impaired Users

In this paper, we present Bluetooth low energy (BLE)-based sensing framework that provides real time spatial awareness for blind and visually impaired (BVI) users while navigating independently through large public venues. The proposed framework includes the following functionalities using only the received signal strength indicator obtained from the BLE sensors: 1) determining the location of the user, 2) estimating the user’s moving direction, and 3) detecting the proximity of landmarks next to the user. We evaluate these functionalities individually. Moreover, we incorporate the proposed framework in PERCEPT indoor navigation system and test it with BVI users in a large public venue. Testing results show that the location, moving direction, and landmark proximity computed by the framework, although not very accurate, provide sufficient information to enable BVI users to independently navigate in large indoor venues. This conclusion aligns with Nobel Prize winning findings that confirm the spatial nature of the entorhinal-hippocampal system and the existence of a positioning system in the brain.
Depth MHI Based Deep Learning Model for Human Action Recognition

Human action analysis based on deep learning has become a hotspot in the fields of intelligent video. Recently, the approaches in depth-based human action recognition provide other ways to recognize human actions. The depth provide important supplementary information to improve the performance based on RGB stream. Meanwhile, the deep learning methods are effective for both RGB and depth features representation. In this paper, we use the deep learning model to learn the discriminative patterns for human action recognition from the depth-based motion history images (MHIs). This method is evaluated on both the 3D human action datasets RGBD-HuDaAct and NTU RGB+D. The experimental results show that our proposed approach achieves good accuracy for recognizing the indoor actions.
Sentiment Analysis Based on Weibo Comments

With the rapid development of natural language processing, sentiment analysis has become a very hot research direction. Based on Weibo comments, in order to make up for the defects that the sentiment dictionary cannot update the new words in real time, the paper uses the support vector machine with better classification performance to do the sentiment classification. In this paper, Word2vec is used to train texts of language model. The main innovation is combining Word2vec and TF-IDF to achieve the improvement of word vectors. Then the average of all the improved word vectors in the Weibo sentence is used as the input of support vector machine for emotional analysis. Finally, Word2vec is used to construct the LSTM deep learning model as a comparative experiment. The experimental results show that the proposed method has a higher accuracy than the LSTM model based on Word2vec and avoids the disadvantages of LSTM training model on the complexity of the training model and the long training time.
Effect of inhibitory firing patterns on the stochastic resonance in feed-forward-loop neuronal network motifs

Neurons express diverse firing patterns in terms of their morphological, biochemical and electrophysiological properties. Different firing patterns can switch the information transmission in neuron network. However, it is not clear how the firing patterns of the neurons affect information transmission. Here we investigate the effect of different firing patterns of inhibitory neuron on network information transmission of triple-neuron feed-forward-loop motif. Results show that the stochastic response behavior can be optimized by certain noise intensity, which indicates stochastic resonance (SR) occurs in the neuronal network motifs. Changing the inhibitory neuron from burst spiking neurons to fast spiking neurons does not affect the optimal noise intensity. In the case of the same noise intensity and coupling coefficient, the bursting neurons produce higher transmission efficiency than the fast firing neurons. Different firing patterns of neuron in the output of the network motif achieved a greater impact on the transmission efficiency of the motif than the neuron located in the middle position. This simulation directly quantifies the influence of firing patterns on information transmission, which has great significance for improving signal transmission efficiency and detecting signals at different operating points.
Energy-Angle Domain Initial Access and Beam Tracking in Millimeter Wave V2X Communications

With plentiful spectrum resources, millimeter-wave (mmWave) band has been the forefront candidate to enable the requirements of lower latency and faster data rate for future automotive systems. However, the incorporation of mmWave directional transmission and diversely vehicular mobility triggers frequent beam realignment, thus largely increasing the beamforming overhead and leaving less time for data transmission. In this paper, an energy-angle domain access and transmission frame structure is investigated for mmWave vehicle-to-everything communications, which consists of two phases, initial access and data transmission. Considering the transmission interruption issue caused by blockage, we propose an energy-angle domain initial access scheme, by which the signals are labeled by different directions with multi-power level. Several performance metrics are subsequently obtained to evaluate the proposed scheme. Next, we formulate the access time-throughput tradeoff problem mathematically and prove that there indeed exists the optimal access time which yields the highest throughput for data transmission. Moreover, a binary-decision beam tracking scheme is designed to maintain the directional link connection in the data transmission phase. Numerical evaluations and simulations have been conducted to verify the correctness of our point of view and the superiority of our proposed methods.
IoTNegViz: An interactive tool for visualizing negative aspects of IoT

The "Internet of Things", or IoT, brings to people a brand new approach with a wide range of applications of how we live in this ever changing world. Besides various advantages of IoT that users can benefit from, the downsides of such cyber physical system are often neglected. This paper introduces IoTNegViz, an analytic tool for visualizing possible negative aspects from a cyber physical system in IoT. First, the data is extracted from Twitter, then a natural language processing tool is applied for generating and categorizing keywords. The visualization is utilized for exploring and capturing information in a spatial manner. To evaluate the usefulness and efficiency of IoTNegViz, we conduct a use-case to gather feedback and experience form users.
Hierarchical Facial Age Estimation Using Gaussian Process Regression

Automatic age estimation from facial images has attracted increasing attention due to its promising potential in real-life computer vision applications. However, due to uncontrollable environments, insufficient and incomplete training data, strong person-specific and large within- age span variations, age estimation has become a challenging problem. Among published age estimation, hierarchical age estimation methods have achieved comparable performance improvement than single level approaches. Most of the published hierarchical approaches have mainly used support vector machines to classify age groups followed by support vector regression for withina- age group age estimation. In this paper, we present a novel hierarchical Gaussian process framework for automatic age estimation. It consists of multi-class Gaussian process classifier to classify the input images into different age groups followed by a warped Gaussian process regression to model group specific aging patterns. In this paper, we separately tune the hyper-parameters for each age group at the regression stage. Compared with existing single level Gaussian process approaches for age estimation, our approach is computationally efficient at both the levels of hierarchy. Partitioning data into different age groups and learning group-wise hyper-parameters is computationally more efficient than learning complete training data. Misclassifications at the group boundaries are compensated at the regression stage by overlapping the neighboring age ranges. Finally, through extensive experiments on two popular aging datasets, the FG-NET and the Morph-II, we demonstrate the effectiveness of our algorithm in improving age estimation performance.