Miracle-dz/UniFAR-FTUs huggingface dataset:
BGSU University Admissions System: Campus Training Module

Dr. Fei Gao & Dr. Gary Benjamin Advisors The purpose of this project was to create an online training module to acquaint faculty and staff at Bowling Green State University with the University Admissions System and provide them with an overview of the functionality and resources available to them for their recruitment needs. Delivered through the Canvas environment, the course offers flexibility in content delivery through a variety of methods including documents, videos, quizzes and encouragement of self-exploration, in an attempt to accommodate a myriad of learning styles. Upon completion of each section, users should feel empowered to navigate UAS and utilize the data to drive their recruitment goals and initiatives. Subject matter experts, from the BGSU Office of Admissions, were asked to review the content and appropriateness of the course in relation to the project’s objectives. The overall response was positive and reassuring of the training’s fulfillment of those goals. This feedback supports the plan to allow new UAS users to enroll in the course as they gain access to the system and to prioritize the upkeep of the content as UAS expands and additional departments rely on its resources. Recommendations included additional areas for course content development and a frequently asked questions section for general inquiries most commonly posed by departments around campus.
5W2H+M: A Broad Gamification Design Process But Focused on Motivation

Gamification has been increasingly implemented in systems to motivate users. To assist designers in the gamification process there are several frameworks, some of them address motivation in gamification superficially while others focus on motivation and are not broad enough. This work presents the 5W2H+M framework and its use to redesign an already gamified system. Game elements were implemented to motivate users to increase the frequency and quantity of responses; longevity of use; feeling of accompaniment, and; feeling of belonging on the users. An evaluation was conducted with a specialist and showed that the frequency, amount of questions and feeling of belonging improved. However, the longevity and accompaniment can be further addressed.
Improving Speech Enhancement with Phonetic Embedding Features

In this paper, we present a speech enhancement framework that leverages phonetic information obtained from the acoustic model. It consists of two separate components: (i) a long short-term memory recurrent neural network (LSTM-RNN) based speech enhancement model that takes the combination of log-power spectra (LPS) and phonetic embedding features as input to predict the complex ideal ratio mask (cIRM); and (ii) a convolutional, long short-term memory and fully connected deep neural network (CLDNN) based acoustic model that extracts the phonetic feature vector in the hidden units of its LSTM layer. Our experimental results show that the proposed framework outperforms both the conventional and phoneme-dependent speech enhancement systems under various noisy conditions, generalizes well to unseen conditions, and performs robustly to the speech interference. We further demonstrate its superior enhancement performance on unvoiced speech and report a preliminary yet promising recognition experiment on real test data.
A Hybrid Motion Estimation Search Algorithm for HEVC/H.265

High Efficiency Video Coding (HEVC) is the latest video coding standard. It supports the high resolution videos by providing twice the compression efficiency compared to the previous standard H.264. Motion Estimation (ME) in HEVC is the most computation intensive block as a result it consumes a huge amount of total encoding time. This paper proposes a fast hybrid ME algorithm for encoding video frames. The proposed algorithm uses an early termination technique with 18 search points compared to the default Test Zone Search(TZS) algorithm in HM16.8 that uses 77 for diamond search pattern and 81 for square search pattern. It requires an average of 11.19% less encoding time with decrement in BD-PSNR of 0.086dB and increment in BD-BR of 2.87%.
LS algorithm for PAR Reduction in OFDM based Massive MIMO Broadcast systems

This manuscript accosts peak-to-average ratio (PAR) reduction in OFDM based massive Multi-Users (MU) MIMO systems. We propose an iterative Least-Squares (LS) approximation algorithm for limiting the peak exceedance values. In MU MIMO-OFDM broadcast systems, as the dimensionality of the MIMO system grows, the probability of an inactive user increases, resulting in redundant channels. In the LS algorithm, we exploit these redundant channels to limit the peak excursions on the remaining active channels. The algorithm approximates the peak excursions on the active channels utilizing the redundant carriers in a Least-Squares fashion. The PAR on the remaining active channels in then limited by adding this model function to the transmit signal. To attain a target limiting threshold for the peak excursions, the algorithm is performed iteratively. As shown through simulation, the proposed algorithm results in a gain of approximately 4.6 dB with as few as 8 iterations.
Critical Path Delay Improvement in Logic Circuit Operated at Subthreshold Region

Lower voltage as well as high-speed subthreshold logic circuits are becoming important factors in modern VLSI technology. At subthreshold region, the effects of the process variations and the diminished ratio of active current to ideal current can introduce timing errors. These timing errors are responsible in the vandalism of the working capacity of the circuits operated at subthreshold region. An increase in propagation delay is observed when the threshold voltage of MOSFET is more than the supply voltage. This paper proposes a circuit to reduce the timing errors keeping the energy consumption to minimum amount. The proposed circuit uses a multiplexer implemented using transmission gates, to be used in the longest delay path of the logic circuit which is operated at subthreshold region. Forward body biasing notion is used to lower the MOSFET's threshold voltage and is incorporated in the proposed circuit, thus, reducing the switching time of the MOSFETs. The proposed circuit is implemented using Cadence Virtuoso at 45 nm technology and compared with the circuit studied from literature survey. The supply voltage for the combinational circuit and the proposed circuit and the D flip-flop was 287 mV. The forward body biasing voltage VSBfor NMOS and PMOS MOSFET was equal to 287 mV. The proposed circuit is applied to the longest delay path of the 64-bit ripple carry adder operated at subthreshold region and the switching delay for high going and low going transition are measured and are reduced by 48.14% and 26.60% respectively. Also, the slope of transition of the output signal has been increased by 53.08% & 8.14% for rise and fall respectively. The total propagation delay diminished by 30.12% as compared to existing technique. The energy per cycle and energy-delay product (EDP) turned down by 33.10% and 53.31 % respectively, over the existing technique.
Simulation Experiment Schemas –Beyond Tools and Simulation Approaches

Simulation studies make use of various types of simulation experiments. For users, specifying these experiments is a demanding task since the specification depends on the experiment type and the idiosyncrasies of the used tools. Thus, we present an experiment generation procedure that guides users through the specification process, and abstracts away from the concrete execution environment. This abstraction is achieved by (1) developing schemas that define the properties of simulation experiments and dependencies between them, (2) specifying a mapping between schema properties and template fragments in the specification language of a target backend. We develop schema, template fragments, and mappings for stochastic discrete-event simulation, and show how the concepts can be transported to a different domain of modeling and simulation. Further, we expand the developed "simple" experiment schemas by a schema for experiment designs, and generate executable sensitivity analysis experiments, thereby demonstrating versatility and composability of our approach.
Military Surveillance with Deep Convolutional Neural Network

In this paper, we propose a real time object detection system for images in Military Surveillance area. In traditional methods of object detection, region proposals are generated followed by extraction of features. At the end a classifier runs on these proposals. The speed of the process is slow and the accuracy is unsatisfactory. We propose a new model which provide better and higher mean average precision (mAP). The customization of YOLO model has enhanced Convolutional Neural Network (CNN) layers to 58. The customized dataset contains 22 classes which include 20 classes of Pascal VOC and 2 classes of tanks and guns from internet source. Challenging images like night vision, low resolution, images captured in different climatic conditions like fog, rain and snow are captured to test the model. The proposed model provides 79.12 % and 78.19% mean average precision (mAP) as compare to YOLOv2 model which provides 75.82% and 74.23% mAP using Pascal VOC and customized dataset respectively with input images of size 416*416. The customized Model is validated with different input size images for finding mean average precision with own dataset. We have customized YOLOv2 model by enhancing CNN layers and hyperfine tuned YOLOv2 model. We have used multi-GPUs for faster model training. We achieved ~33 times and ~50 times speedup as compare to CPU (Xeon) using single GPU (Tesla K40) and two GPUs architecture respectively.
Area-Delay Efficient and Low-Power Carry Skip Adder for High Performance Computing Systems

The high performance computing architectures are the primary need for the modern devices having compute-intensive applications such as signal and image processing. This paper presents a novel low complexity carry-skip adder design that provides high-speed and consumes low-power making it suitable for the development of high-performance signal processing cores. The proposed adder is derived from the reformulated Boolean expressions that avoid the redundant computations with reduced critical path delay. The proposed architectures are coded in VHDL and synthesized with Synopsys Design Compiler using 65nm CMOS library. Synthesis results demonstrate that the proposed 32-bit carry skip adder reduces the delay, area and power by 10.2%, 13.6% and 8% respectively than the best known carry skip adder. Finally, the proposed 16- and 32-bit adders reduce the area-delay product by 14.4% and 22.5% respectively over the existing adder.
Computational offloading Strategy based on Dynamic Particle Swarm for Multi-User Mobile Edge Computing

With the development of the Internet of Things, the number of existing mobile terminals is increasing, more and more data need to be processed, and the requirements for performance and computational analysis capabilities of mobile terminals are getting higher and higher, but the price of existing mobile devices will follow Performance has increased dramatically, so we need to consider both cost and performance factors, and the proposed mobile edge calculation lays the theoretical foundation for solving this problem. In this paper, two improved particle swarm optimization algorithms, combined with the greedy strategy particle swarm optimization algorithm and dynamic particle swarm optimization algorithm, optimize the task assignment and carry out a series of experiments and tests.
Quantitative Model Checking for a Resilient Control System

Automatic controllers are interfacing computer systems and physical systems. Because they are physically interacting with surrounding environments, their misbehavior can critically harm anyone nearby. For their safe operations, it is critical to ensure that their behaviors conform to the specification in normal operational conditions and any hazardous actions due to unexpected operational environments are prevented. In this paper, we introduce a quantitative model checking technique to the design of a controller so that the resulting system satisfies the specification in normal condition. Furthermore, we applied the technique to check and forewarn if a sensor reading now may lead to a violation of the specifications in the future.
A Review on Credibility Perception of Online Information

Credibility generally regards one’s belief in the truth due to an initial impression of a subject. Different types of credibility assessment are taken into account by online readers as a process to decide whether a trust relationship would concur between them and the source of the information based on the quality of the information and the reputation or quality of a source. A large body of literature exists on online credibility evaluation, with particular emphasis on online social media (OSM), specifically Twitter. In this paper we will provide the background knowledge on the theoretical part of information credibility, discuss the reader’s credibility perception behaviours and factors that have an impact on credibility judgements. We then followed up by a description of credibility evaluation methodologies and summarise the research gaps regarding information credibility perceptions and propose some future work in the information credibility perception research area.
Stochastic Positivity and Stability Analysis for Discrete-Time Linear Systems

In this article, the stochastic positivity and stability problems are addressed for the linear systems in discrete-time form. Firstly, a new and novel definition is given to describe the positivity characteristic of certain systems in the sense of probability. Then, by rigorous discussion/analysis, sufficient criteria are established, which guarantee that the linear system is stochastically positive in the sense of probability with respect to some given threshold. After that, certain nonnegativeness property is provided for the mathematical expectation of the solutions concerning the considered stochastic linear system. Subsequently, stochastic stability in different forms is studied. Finally, illustrative examples are provided to demonstrate the validity/effectiveness of the developed results.
On Model-guided Neural Networks for System Identification

Many techniques exist to train neural networks to approximate a complex systems, such as deep learning methods. It is well known that despite their robustness with respect to over-fitting, such trained models may be brittle as some fundamental principles of the systems are missing. For many engineering applications, the model class may be derived from first principles (or fundamental principles). In this paper, ideas from both methodologies are combined to arrive at a robust model that is interpretable from first principles, but goes beyond this by capturing structure from the available data. The paper presents two examples to illustrate the ideas. First a synthetic data set based on simulations is used. Next a well known data set from functional magnetic resonance imaging (fMRI) is used. In these two examples, a few representative neural networks are used in combination with model information coming from first principles. The preliminary results show that the framework is highly beneficial and yields excellent system identification fidelity.
The H2O Dissociation and Hydrogen Evolution Performance of Monolayer MoS2 Containing Single Mo Vacancy: A Theoretical Study

The molybdenum disulfide (MoS2), as a promising and nonprecious catalyst, has attracted growing interest towards the hydrogen production, especially the dissociation of water and the hydrogen evolution reaction (HER). However, the known active sites are limited to edges and one primitive cell missing vacancies. Herein, the potential catalytic activities of Mo-vacancies in the inert basal plane of MoS2 are investigated by means of first-principle density functional theory (DFT) calculations. Mo-vacancies was found to have the promising catalytic activities for the splitting of H2O and have the comparable or even better catalytic activities for the HER, in compared with the precious Pt. Moreover, the catalytic activities of these active sites are well understood in terms of the analysis of the adsorption energy, the total and partial density states (DOS, PDOS), and the Bader charge transfer. Our theoretical works suggest that formation of the specified vacancy defects on the inert basal plane will enhance the catalytic activities of MoS2 for the dissociation of H2O and the HER, which improves the efficiency of hydrogen production.
T-Caching: Enhancing Feasibility of In-Network Caching in ICN

In Information-Centric Networking (ICN), in-network caching is one of the core functions to implement efficient content distribution. As content is served from the network cache, redundant transmission across the same link is reduced and better quality services are provided to the user. However, caching operations (locating cached content, validating/writing content to the cache) may be a large burden on routers. In this article, we substantially reduce the caching overhead of routers by introducing T-Caching, a new in-network caching model in which routers selectively cache some of the most popular content recommended by content providers. In T-Caching, tokens are used 1) to enable routers to maximize caching benefits by simply controlling the amount of content inserted into the cache; 2) to allow content providers to recommend the most popular content as much as routers would actually cache. Our simulation and empirical studies using synthetic data, as well as real-world traces, show that T-Caching allows routers to insert much less than 1 percent of the content into the cache, compared to typical caching mechanisms. Nevertheless, the cache-hit ratio is improved by up to 2<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="kim-ieq1-2970702.gif"/></alternatives></inline-formula>9 times depending on the cache size. We believe that T-Caching can significantly contribute to improving feasibility and performance of in-network caching in ICN.
Convolutional Network with Densely Backward Attention for Facial Expression Recognition

The emergence of convolutional neural network (CNN) has enabled facial expression recognition to accomplish significant outcomes nowadays. However, while existing multistream networks are subject to costly computation, the attention- embedded approaches do not involve multiple levels of semantic context in the predefined CNN. Based on the observation that emotions via a person's face are fusion of various muscular modalities, relying upon the outputs and corresponding attentional features of the deepest layer in the CNN is insufficient due to loss of informative details through multiple sub-sampling stages. Therefore, this paper introduces a CNN with densely backward attention to leverage the aggregation of channel-wise attention at multi-level features in a backbone network for reaching high recognition performance with cost-effective resource consumption. Particularly, cross-channel semantic information in high-level features are exploited densely to recalibrate finegrained details in low-level versions. Then, a step of multi-level aggregation is further executed for thorougly involving spatial representations of important facial modalities. As a consequence, the proposed approach gains highest mean class accuracy of 79.37% on RAF-DB, which is competitive with the state-of-the- arts.
Polycentric Circle Pooling in Deep Convolutional Networks for High-Resolution Remote Sensing Image Recognition

Most existing deep learning-based methods use feature maps extracted from convolutional neural networks (CNNs) for classification and detection of high-resolution remote sensing images (HRSIs). However, directly applying these features to classification and object detection in HRSI is problematic because of rotational variations. In this article, we design networks using the polycentric circle pooling (PCP) strategy to alleviate the abovementioned problem. The PCP network (PCP-net) structure can generate a fixed-length representation for different input image sizes and encode rotation-invariant information. With these advantages, PCP-net should in general improve the CNN-based HRSI classification methods. Specifically, on the basis of the concentric circle pooling network structure, we improve the structure using multiple concentric circle centers to generate more robust rotation-invariant information. Using two challenging HRSI scene datasets, we prove that PCP-net improves the accuracy of CNN architectures for a scene classification tasks. PCP-net can be conveniently applied to object detection because the output size is fixed regardless of image size. Experiments applying the faster region-CNN to a publicly available ten-class object detection dataset demonstrate that our proposed PCP can achieve accuracy higher than that of a region of interest pooling in the HRSI object detection task.
A Novel Snowball-Chain Approach for Detecting Community Structures in Social Graphs

Community detection in social networks has been a widely explored problem to gain information about the dense groups of user favoring some particular idea or topic, or sharing common interests. Inspired from the process of snowball sampling, this paper presents a novel community detection approach, termed as snowball-chain (SbChain), for identifying communities in social networks. SbChain follows a bottom-up approach to find the most prominent nodes based on the degree of overlapping neighbors and clustering coefficient, that may form some cliques. The novelty of SbChain lies in a single overlapping hyperparameter requirement, λ, to merge snowballs to form a community. λ also helps in deciding the coarseness of communities to be identified from the social graph. The proposed approach is evaluated over different real-world and synthetic benchmark datasets and compared with some state-of-the-art methods in terms of the number of identified communities and their modularity.
Optimizing Time-series Prediction on China’s Green Trade Economy

With the impact of green trade barriers in recent years, China’s green trade economy is facing enormous challenges. Using data from the website of the General Administration of Customs of the People’s Republic of China (GACC), this paper aims to employ Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Long Short Term Memory (LSTM) model to forecast the total import and export volume of China’s green trade, and error analysis is made according to the forecasting result. Considering that inflation is an important influence factor in forecasting economics, the models are optimized by eliminating inflation through consumer price index (CPI) and the following results are obtained. Firstly, the LSTM model obtains better performance than the SARIMA model. Secondly, forecasting accuracy of the optimized models is improved. Thirdly, China‘s green trade economy shows a steady trend in the near future.