Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of brainstem auditory evoked potentials in early (grade I) essential hypertensive patients

Background: Essential hypertension is one of the most common world’s health diseases. It frequently affects central nervous system (CNS) by producing micro-infarctions which results into altered evoked potentials. Previous studies have shown correlation between hypertension and brain stem auditory evoked potentials. But very scarce data is available on all the parameters of BAEP and essential hypertension especially “amplitude ratio” which distinguishes between central and peripheral impairment. This study was undertaken to evaluate the brain stem auditory evoked potentials (BAEPs) as early marker of cognitive damage in essential hypertension.Methods: BAEPs were recorded in 50 hypertensive subjects and in 50 normotensive controls. Absolute peak latencies, interpeak latencies of different waves and amplitude ratio were compared in both the groups by using unpaired student’s T test.Results: Significant changes in absolute latencies of wave I and V, Interpeak latency I-III, III-V and I-V and amplitude ratio V/I was observed in hypertensive group as compared to control group.Conclusions: Findings of the current study suggests that hypertension does affect the neuronal excitation in the auditory pathways, thereby suggesting that BAEP may provide the early evidence for the presence of CNS dysfunction in the patients of essential hypertension.
An Overview of Neuromorphic Computing for Artificial Intelligence Enabled Hardware-Based Hopfield Neural Network

Compared with von Neumann’s computer architecture, neuromorphic systems offer more unique and novel solutions to the artificial intelligence discipline. Inspired by biology, this novel system has implemented the theory of human brain modeling by connecting feigned neurons and synapses to reveal the new neuroscience concepts. Many researchers have vastly invested in neuro-inspired models, algorithms, learning approaches, operation systems for the exploration of the neuromorphic system and have implemented many corresponding applications. Recently, some researchers have demonstrated the capabilities of Hopfield algorithms in some large-scale notable hardware projects and seen significant progression. This paper presents a comprehensive review and focuses extensively on the Hopfield algorithm’s model and its potential advancement in new research applications. Towards the end, we conclude with a broad discussion and a viable plan for the latest application prospects to facilitate developers with a better understanding of the aforementioned model in accordance to build their own artificial intelligence projects.
Sales Forecast for Amazon Sales with Time Series Modeling

Accurate sales prediction plays an important role in reducing costs and improving customer service levels, especially for B2C(Business to consumer) e-commerce. This paper attempts to forecast future sales at Amazon.com, Inc. based on historical sales data. Firstly, it proposes three possible forecasting approaches according to the historical data pattern, that is Holt-Winters exponential smoothing, neural network auto regression model and ARIMA(Autoregressive integrated moving average). Secondly, it specifies certain accuracy measures using which well determine the suitability of the forecast methods on the available sales data. Finally the three methods will be implemented to forecast Amazons quarterly sales in 2019. The results can help Amazon well manage its future operations.
Online Social Networks and Writing Styles–A Review of the Multidisciplinary Literature

In recent years, author identification has become an active research area, where the major differences are caused by paper or online medium, mode of entry and target audience. Much research has been devoted to analyzing writing styles in handwritten, word-processed and online social networks (OSN) texts. Word processing editors that typically include spell and grammar checkers may influence the writing style as it allows an individual to edit a piece of text to perfection. Thus, similarities may exist between OSN and word-processed texts. Moreover, none of the studies to date have made a detailed comparison of the writing styles across multidisciplinary factors. This paper attempts to close the gap between the writing styles in pre- and post-Internet periods as well as provide an in-depth comparison of the writing styles in OSN texts across three major factors: demographics, personality & behavior, and cybersecurity. The aim is to learn from past literature as we advance these techniques to OSN texts. Thus, in this paper, we also propose a novel machine learning prediction model based on tense morphology, to classify age and gender from English blogs, and the PAN 2013 dataset. This model achieves an accuracy of 94%–98% and 95%–97% for age and gender, respectively.
Characterization of the indigenous goat production system in Asossa zone, Benishangul Gumuz region, Ethiopia

Most of the livestock population in Asossa zone is comprised of indigenous goats, which have remarkable socioeconomic relevance to the society. This study aimed to generate information on the production system and production constraints of goats in the study area. The study was conducted based on a household survey in which 192 households were purposively sampled. The households were located in Sherkole, Kurmuk and Menge districts that have a high goat population in Asossa zone. Data were recorded in MS Excel data sheet and analyzed using SAS. A chi-square and GLM procedure of SAS was used to test significant differences among categorical and quantitative variables. The primary reason for keeping goats was for cash income with an index value of 0.46, 0.34 and 0.31 for Sherkole Kurmuk and Menge districts, respectively. Milk was the second purpose for rearing goats, with a ranking index value of 0.29, 0.30 and 0.30 for Sherkole, Kurmuk, and Menge districts, respectively. Grazing on natural pasture was the major feed source for goat production in the three districts (with an index of 0.86, 0.91 and 0.0.87 for Sherkole, Kurmuk and Menge districts, respectively). Although the majority of households (59 %, 75% and 62.5% in Sherkole, Kurmuk, and Menge districts, respectively) used yard type of housing, the number of households that used this type of housing was significantly different (p<0.05) among the three districts. Majority of goat owners used an uncontrolled type of mating that accounts 92.2%, 89.1% and 85.9% in Sherkole, Kurmuk, and Menge districts, respectively. Feed shortage (with ranking index value of 0.30, 0.34 and 0.28 in Sherkole Kurmuk and Menge districts, respectively) and water shortage (with ranking index value 0.21, 0.23 and 0.22 in Sherkole Kurmuk and Menge districts, respectively) were the first and second goat production constraints. Age at sexual maturity was 7.52 months for males and 7.84 months for female goats in this region. Goats play a multi-functional role for the community by adapting to the different constraints that need intervention mechanisms from responsible bodies to boost the productivity of the sector. Key words : constraints, Ethiopia, feed, housing, indigenous goats, reproductive performance, production system, water
An Area-Efficient Hybrid Polar Decoder With Pipelined Architecture

As the first kind of capacity-achieving forward error correction (FEC) codes, polar codes have attracted much research interest recently. Compared with traditional FEC codes, polar codes show better error correction performance when successive cancellation list (SCL) decoding with cyclic redundancy check is adopted. However, its serial decoding nature and high complexity of list management lead to its low throughput. Though the adaptive SCL decoding and hybrid decoding can improve the throughput, it comes at cost of implementation area. In this paper, we propose a pipelined hybrid decoding procedure and the corresponding hardware architecture to improve the area efficiency. In our design, the idle decoding cores are employed for successive cancellation (SC) decoding when SCL decoding is not working. The SCL decoding will be activated when the SC decoding fails. Different decoding cores work according to their own operation sequences and share one common processing array to improve the utilization ratio of processing elements. Constant receiving interval is supported with the design of input buffer to store all received codewords. A software platform is established to optimize the design parameters for each module of decoder. Moreover, the corresponding architecture is implemented using 65nm technology. Experimental results show that the proposed decoder can achieve a similar error correction performance with the SCL decoding with list size 16. Compared to the state-of-the-art available hybrid decoder, our proposed pipelined hybrid decoder is $3.07\times $ more area efficient.
Joint Demand Forecasting and DQN-Based Control for Energy-Aware Mobile Traffic Offloading

With the explosive growth in demand for mobile traffic, one of the promising solutions is to offload cellular traffic to small base stations for better system efficiency. Due to increasing system complexity, network operators are facing severe challenges and looking for machine learning-based solutions. In this work, we propose an energy-aware mobile traffic offloading scheme in the heterogeneous network jointly apply deep Q network (DQN) decision making and advanced traffic demand forecasting. The base station control model is trained and verified on an open dataset from a major telecom operator. The performance evaluation shows that DQN with traffic forecasting outperforms others at all levels of mobile traffic demands. Also, the advantage of accurate traffic prediction is more significant under higher traffic loads.
Low-Dose CT Image Denoising Using a Generative Adversarial Network With a Hybrid Loss Function for Noise Learning

Potential risk of X-ray radiation from computed tomography (CT) has been a concern of the public. However, simply decreasing the dose will degrade quality of the CT images and compromise diagnostic performance. In this paper, we propose a noise learning generative adversarial network coupling with least squares, structural similarity and L1 losses for low-dose CT denoising. In our method, noise distributed in the input low-dose CT image is learned by the generator network and then subtracted from the input to generate the final denoised version. The denoised CT images are penalized by the least squares loss function, and they are pulled toward boundary of the decision even though they are classified as normal-dose CT. Least squares stabilize the training process without regularization. Structural similarity and L1 losses are utilized to keep textural details and sharpness of the denoised CT images respectively. Experiments and results show that our method can effectively suppress noise and remove artifacts compared with the state-of-the-art methods. The texture statistical properties, which include mean, standard deviation, uniformity, and entropy, further confirm that the generated noise-reduced CT image is as close as to that of the normal-dose counterpart.
Dynamic Facial Emotion Recognition Using Deep Spatial Feature and Handcrafted Spatiotemporal Feature on Spark

One important challenge of dynamic facial emotion recognition is to effectively obtain the spatial and dynamic change of face structure from videos. Besides, there is an increasing demand for distributed computing of videos, as a result of the speedy production of videos from numerous multimedia sources. To address the above issues, in this work, we propose a novel method for dynamic facial emotion recognition on top of Spark. Furthermore, we introduce an effective dynamic feature descriptor namely, Volume Symmetric Local Graph Structure (VSLGS), which extracts the spatiotemporal features. We also utilize the convolutional neural network (CNN) to obtain deep spatial features. Lastly, these obtained features are concatenated and fed to Spark MLlib Multilayer Perceptron (MLP) classifier to recognize the dynamic facial emotions. An extensive experimental investigation is performed to prove the effectiveness of our method over state-of-the-art methods. Furthermore, we also showed the scalability of the proposed method experimentally.
MATT: A Mobile Assisted Tense Tool for Flexible m-Grammar Learning Based on Cloud-Fog-Edge Collaborative Networking

The proliferation of modern mobile technologies on grammar learning (i.e., m-grammar learning) has generated a multitude of challenges in developing effective pedagogically-informed learning tools. The existing systems have mostly suffered from low motivation and poor learning effectiveness because of the three key reasons, namely: i) a weak tie to motivational theoretical principles, ii) a lack of proper instructional design, and iii) a lack of proper infrastructural design for data sharing between students and instructors. To deal with this issue, this paper presents MATT: a Mobile-Assisted Tense Tool that encapsulates an m-grammar instructional design leveraging upon cloud-fog-edge collaborative networking. Central to MATT is the incorporation of the Cognitive Theory of Multimedia Learning principles to minimize the extraneous cognitive load and a motivational model to increase motivation and learning effectiveness. To ensure effective instructional design, we exploit adaptive and dynamic approaches embodied in a flexible instructional paradigm that takes advantage of collective learning data exchange across cloud (central unit), fog (regional units) and edge (end devices/learners). To demonstrate the overall effectiveness of this system, we describe our findings in the evaluation of both the learning aspect (using a quantitative research design) and collaborative network performance (using numerical simulation). With an appropriate condition of delay-tolerant network-enabled learning data exchange, the results suggest that the students’ cognitive load is low and motivational nature is high after using this system, which led them to perform more positively in the post-test evaluation.
Vision-Based Instant Measurement System for Driver Fatigue Monitoring

In this paper, a vision-based physiological signal measurement system is proposed to instantly measure driver fatigue. A remote photoplethysmography (rPPG) signal is a type of physiological signal measured by a camera without any contact device, and it also retains the characteristics of the PPG, which is useful to evaluate fatigue. To solve the inconvenience caused by the traditional contact-based physiological fatigue detection system and to improve the accuracy, the system measures both the motional and physiological information by using one image sensor. In a practical application, the environmental noise would affect the measured signal, and therefore, we performed a preprocessing step on the signal to extract a clear signal. The experiment was designed in collaboration with Taipei Medical University, and a questionnaire-based method was used to define fatigue. The questionnaire that could directly react to the feeling of the subject was treated as our ground truth. The evaluated correlation was 0.89 and the root mean square error was 0.65 for ten-fold cross-validation on the dataset. The trend of driver fatigue could be evaluated without a contact device by the proposed system. This advantage ensures the safety of the driver and reliability of the system.
Convolutional Neural Network with Data Augmentation for Robust Myoelectric Control

Surface electromyogram (sEMG) armband with electrodes mounted around the user's forearm is one of the most ergonomic wearable EMG devices. Definitely, the signal distribution varies greatly across different wearing positions of the armband based on physiological characters of the sEMG, which may compromise the performance and even make it inapplicable to use a recognition model built in one position. Thus, the electrode shift problem hinders wide applications of the sEMG armband, especially during its long-term and repetitive usage. High-density surface electromyogram signals recorded from a 2-dimensional electrode array carry rich spatial information and can be viewed as a muscular activation image. In the proposed method, a self-designed convolutional neural network (CNN) combined with data augmentation operation is used to learn muscular activity patterns at an original/baseline position of the electrode array. The method cropped the sEMG feature images for data augmentation. The CNN could learn the invariance of electrode shift from the cropped images. The performance of the proposed method was evaluated with data recorded by an armband-like sensor apparatus with five electrode arrays embedded in its inner side, which was placed over forearm of 1 subject performing 8 gestural tasks. The proposed method was able to maintain high task classification accuracies around 95% and outperformed two traditional methods (accuracies ranging from 50% to 75%) under conditions with 4 designated shifts.
Multi-label Patent Classification using Attention-Aware Deep Learning Model

Patent classification is challenging and essential for any further patent analysis task. We tackle the classification task on lower level patent classification (subgroup level) by using AttentionXML. Recently, pretraining methods for Natural Language Processing (NLP), such as DistilBERT pre-trained model, have achieved state-of-the-art results on some NLP tasks such as text classification. In this work we focus on investigating the effect of applying DistilBERT pre-trained model and fine-tuning it for the important task of multi-label patent classification. Moreover, the large USPTO-3M dataset (3,050,625 patents) based on CPC subclass and subgroup level is used for the purpose of comparing previous deep learning related studies.
Spatial Correlation based Data Redundancy Elimination for Data Aggregation in Wireless Sensor Networks

The dense distributed deployment of Wireless Sensor Networks (WSNs) causes the sensors to generate big amount of data which are highly correlated and redundant. Transmitting such spatially correlated and redundant data consumes more sensor energy and consequently decreases the lifetime of network. Eliminating redundancy is thus necessary in the sensed data and also while processing the sensed data. By employing appropriate data aggregation techniques, the data redundancy can be minimized. We leverage statistical techniques in sensor networks and propose a novel Spatial Correlation based Data Redundancy Elimination for Data Aggregation (SCDRE) protocol that eliminates redundancy at two levels: at source level, it uses simple data similarity function and at aggregator level, it uses correlation coefficient to eliminate redundancy and aggregate the data. We have evaluated the proposed protocol in terms of aggregation ratio, data accuracy and energy consumption and the results show that SCDRE outperforms other existing techniques. In addition, SCDRE is more robust in the presence of noise and outliers. By eliminating data redundancy to greater extent, our protocol experiences less communication overhead and significantly enhances the lifetime of sensor networks.
Adopting Continuous Integeration and Continuous Delivery for Small Teams

Continuous Integration is the practice of merging small code changes frequently rather than merging large number changes at the end of a development cycle. In addition, after each integration, the system should be re-built and re-tested to ensure that the system is still working after each change, and it’s safe to build on top of it. Continuous delivery is software practice in which the software can be deployed to the market at any given time. Developing continuous integration and continuous delivery platform will solve a lot of problems in old agile methods such as increases time to find and fix bugs as testing process late, deploying software that has not been fully tested and shared testing resources which cases delivery delays, when multiple projects are ready for testing one project get tested and others are queued waiting for the testing resource to be released. Also continuous delivery guides development organization to build valuable software and to increase the number of releases. With all this benefits there are some problem obstructing companies from moving to the new platform, some of them are human factors such as human resistant to changes and other are technical such as moving to new platform require learning new tools and training. To help overcome these problems we need to develop simple platform which is more usable and easy to learn. Also some problems happen when applying CI/CD some of them related to testing and others related to integration. Testing problem are: ambiguous test result which is test results that doesn’t guided the developers to finds bugs, which increases time to find and fix it; time consuming tests which require developers to waiting large amount of time for testing reports after each small changes, this waiting cause the developers to practice large commit (merging large number of changes) which slows the integration process. Slow integration mean large number of conflict lines, now instead of doing productive process developers wasting time to solve this conflicts. To overcome these problem we introduce a new way to configuring tests. As you see solving time consumes test will solve integrations problems. To reduce testing time we need to organize the test in such way that tests are more appointed and targeted to changed code only. This method enforces a concept called micro-pipeline. A micro-pipeline is group of testing blocks, each block is responsible for specific type of test for specific unit of code, connected together in such a way that passing the block tests triggers the next test block in series. When integration happen the developer specify test block which is responsible for testing made changes only. The idea behind micro-pipeline is to test affected part only.
An investigation on bio-aerosol concentrations in the different wards of hospitals of Isfahan University of medical sciences

Background: Bioaerosol is defined as airborne particles such as living organisms include bacteria, viruses, fungi and their related metabolites, such as endotoxin. The exposure to bioaerosols in the hospital may causes infection especially in immunocompromised patients. The purpose of this study was to investigate the Bioaerosol concentrations in air of various parts of three hospitals of Isfahan University of medical sciences, Iran. Methods: An Anderson biosampler was used for the bacterial and fungal collection. The effect of factors such as humidity, temperature and outdoor Bioaerosol concentrations were also evaluated. Findings: The lowest mean of fungal and bacterial concentration was observed in hospital operating rooms and the highest concentration was detected in infectious diseases ward of hospital 1 and 2 and surgery ward of hospital 3. Significantly, bacterial levels were found to be higher in hospital wards than outdoor; except hospital operating rooms. Conclusion: Our results show that bioaerosol concentrations in hospitals were relatively high and in particular, the higher levels of bacteria than outdoor might be related to the presence of patients, their activity, unsuitable ventilation and disinfection. Therefore, environmental control measures are needed to assure hospital environmental quality especially in wards with immunocompromised patients.