Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon Nanotube Coordinate Prediction with Deep Learning

The development of carbon nanotube technology has provided a great advantage in applications of many fields including nanotechnology and materials science due to the exquisite mechanical, chemical, thermal and electrical properties of carbon nanotubes. However, due to their size, the scale at which the physical phenomena of carbon nanotubes are apparent is too small to do physical experiments, there is a need for certain computational methods like molecular dynamics simulations. In this present study, we propose a deep learning methodology, alongside a custom data preprocessing method, for precisely determining carbon nanotubes’ coordinates. We experimented with various topologies of neural networks and acquired a top result of 81.34%. Our findings and computation method surpasses the previous work on this field, in terms accuracy and computational time.
Composite Convolutional Neural Network for Noise Deduction

In order to improve the noise reduction performance and the clarity of denoising images, a composite convolutional neural network composed of the convolutional autoencoder network and the feature reconstruction network is proposed. Multiple convolutional layers are added into the autoencoder to extract the image feature information and improve the denoising performance, and the feature reconstruction network is designed to recover the texture and detail information of the image. The cross-connected structure is used to fuse feature information in the convolutional autoencoder network into the feature reconstruction network. Experimental results show that the proposed method has better noise reduction performance than the existing methods for different noise intensity. More texture and detail information could be retained, and the clearer denoising images could be obtained.
Cluster Head Selection Technique Using Four Parameters of Wireless Sensor Networks

To extend lifetime, significant energy saving to create balanced cluster is a challenge of Wireless Sensor Networks (WSN). Selection of proper Cluster Heads (CHs) is one of a solution to overcome this challenge. Here, we have offered a simple balanced CH selection method that will save significant energy as well as increase lifetime of the network. Our system is centralized. In the offered method, we have considered four parameters i.e. remaining Energy, Number of neighbor nodes, Distance between CHs and Base Station (BS) and One hop neighbor information. Moreover, we have dynamically adjusted distance value by another factor of β for each round which will further minimize the energy consumption and increase network lifetime. Proposed protocol will save significant energy by reducing back transmission and sensing every points of the network in a stable manner. We have evaluated the proposed protocol and compared with LEACH-C and An Efficient Cluster Head Selection (ECHS). We have simulated our protocol and found major improvement of performance with respect to 1st Node Death (1ND), End Node Death (END) and consumption of energy vs rounds.
A novel hybrid technique for user navigation pattern prediction using KHM and FP growth

A huge amount of data is available on the web. Several users tend to access the data available on the internet. This makes it difficult to understand the browsing behavior of the user. The browsing history of the user is recorded in the web server log files. Such log files need to be analyzed. The approach described in this paper is based on a novel hybrid technique for analyzing the browsing patterns. It involves a novel and hybrid approach based on clustering and pattern mining algorithm. The approach discussed in this paper is being implemented and the results are analyzed and compared with other related system using two different datasets. The results are also compared concerning the execution time, accuracy and number of frequent patterns.
A Skip Attention Mechanism for Monaural Singing Voice Separation

This work proposes a simple but effective attention mechanism, namely Skip Attention (SA), for monaural singing voice separation (MSVS). First, the SA, embedded in the convolutional encoder-decoder network (CEDN), realizes an attention-driven and dependency modeling for the repetitive structures of the music source. Second, the SA, replacing the popular skip connection in the CEDN, effectively controls the flow of the low-level (vocal and musical) features to the output and improves the feature sensitivity and accuracy for MSVS. Finally, we implement the proposed SA on the Stacked Hourglass Network (SHN), namely Skip Attention SHN (SA-SHN). Quantitative and qualitative evaluation results have shown that the proposed SA-SHN achieves significant performance improvement on the MIR-1K dataset (compared to the state-of-the-art SHN) and competitive MSVS performance on the DSD100 dataset (compared to the state-of-the-art DenseNet), even without using any data augmentation methods.
A Performance Evaluation on Distance Measures in KNN for Mobile Malware Detection

Most of the related works on mobile malware detection for Android Operating System (OS) that are based on machine learning often use classifiers' default settings, and focus on opting either the optimal features or classifier. Even if this approach is understandable and it has proven to provide valuable results classifiers different hyper-parameters should be configured properly in order to achieve classifier's best performance. Thus, this paper investigates the performance of one of the most simple machine learning classifier, such as K Nearest Neighbor (KNN), considering its different hyper-parameters with emphasis on different distance measures. The authors have performed an extensive comparison using various well known distance measures over the Drebin data set. Results show that the proper choice of the distance measure can provide a significant enhancement to the classification accuracy. Specifically, the Euclidean distance that is mostly used for KNN is not the optimal option, instead other distance measures i.e., Hamming, CityBlock, can boost classifier's performance in the context of mobile malware detection. For instance, CityBlock can improve KNN false positive rate up to 33% in comparison to the Euclidean distance.
Performance Comparison between Turbo and Polar Codes

Channel coding is a vital unit of any communications system. Future wireless systems are necessary to be equipped with high performance codes having low complexity encoder and decoder design. The requirements of these systems range from providing high throughput, operating in highly reliable scenarios, to working with short and long information messages, low and high code rates. To obtain maximum coding gain in wireless communication systems, Turbo codes and Polar Codes are used as error correcting codes. Satellite communication and 4G/5G services widely use these codes. These codes are found to approach Shannon capacity performance. Two recursive systematic convolution encoders separated by an interleaver are used to build a turbo encoder. Polar Codes are constructed on the concept of channel polarization. This work focusses on the Bit Error Rate evaluation and analysis of the codes for varying block lengths and code rates.
The Effects of Ontology-Based Gamification in Scripted Collaborative Learning

Scripted Collaborative Learning (CL) promotes meaningful interactions among students. Unfortunately, in some situations, the use of scripts causes motivational problems which in turn may lead to negative learning outcomes. In the last years, gamification has emerged as a promising technique to motivate and engage students towards learning process. However, gamification requires personalization based on knowledge about game design, and their impact on the students' motivation and learning outcomes. To address these issues, we proposed the formalization of an ontology to support the gamification of CL scenarios in which the game elements are tailored for each situation. These CL scenarios are known as ont-gamified CL sessions, and this paper presents the results of one empirical study conducted to evaluate their effects on the participants' intrinsic motivation and learning outcomes. This study was carried out as quasi-experiment with 62 undergraduate Brazilian students who participated either in non-gamified or ont-gamified CL sessions. Findings indicate that the intrinsic motivation was better in ont-gamified CL sessions, and that there was no significant difference on learning outcomes. Based on these results, we concluded that our approach is likely to deal with motivational problems in scripted CL.
Outage Performance of NOMA in Cooperative Cognitive Radio Networks With SWIPT

This paper considers the application of non-orthogonal multiple access (NOMA) into cooperative cognitive radio (CR) networks with simultaneous wireless information and power transfer (SWIPT). For NOMA in cooperative CR networks with SWIPT, the cognitive relay harvests the transmission power from the secondary transmitter with power splitting scheme, while the fixed power allocation scheme is used for the NOMA protocol. The closed-form analytical expression of the overall outage probability for the proposed networks is derived, as well as its diversity order at high signal-to-noise ratio (SNR) region is investigated. Furthermore, compared to OMA in cooperative CR networks with SWIPT, the proposed scheme can always achieve the same diversity order, but lower overall outage performance. Compared with NOMA in cooperative CR networks using its own battery for transmission, the SWIPT NOMA in cooperative CR networks will lead to losing a little of the overall outage performance, but without losing the diversity order.
Hybrid Variable Latency Carry Skip Adder

The hybrid variable latency carry skip adder (HVL-CSKA) is obtained by structural modification of the concatenation and incrementation scheme carry skip adder (CI-CSKA). CI-CSKA uses AND-OR-INVERT (AOI) logic and OR-AND-INVERT (OAI) logic as the skip logic instead of multiplexers as in the conventional carry skip adder. While the AOI and OAI logic helps to improve the speed of the adder along with concatenation and incrementation schemes, the hybrid variable latency structure helps to decrease the power consumption without compromising the speed of the adder. Since full adders form the backbone of a CSKA, the basic structure of full adder (FA) is replaced by a high speed and low power structure for FA for further enhancement in the performance of CSKA. The simulations are carried out by using the software Xilinx ISE 14.7 Design suite and Vivado Design suite 2014.4. The hardware used for implementation is Nexys 4 DDR Artix-7 FPGA board. The structures of the adders are compared based on the parameters such as power dissipation, delay, Power-Delay Product (PDP) and area. HVL-CSKA structure shows a decrease of 22.08 percent and 16.56 percent in delay when compared to conventional CSKA and CI-CSKA respectively. While its power consumption is reduced by 70 percent when compared to other structures of CSKA.
Research and Analysis of Sport Medical Data Processing Algorithms Based on Deep Learning and Internet of Things

With the development of computer and information technology, more and more data and image information are generated in medical field. Sports medicine, as an important branch of medical cause, is responsible for ensuring national sports safety and rehabilitation after injury. How to use a large number of sports medical data and cases to accurately analyze and mine useful data and information has become an important research direction of sports medical data processing and mining. This paper will focus on the information mining and analysis of large sports medical data, focusing on the loss of training mode and the accuracy of convolution algorithm. In order to achieve effective prediction and risk assessment of sports medicine-related diseases, this paper starts with the improved convolutional neural network deep learning algorithm, and adopts the resampling algorithm with self-adjusting function, supplemented by tensor convolution self-coding algorithm. Ural network model assists multi-dimensional data analysis of sports medicine. Finally, in order to build an intelligent medical data platform for sports medicine, this paper innovatively proposes a cloud-based hardware-in-the-loop simulation model. Experiments show that this method provides reference and technical support for the realization of a real cloud-based fusion system.
Prediction of Heparin Dose during Continuous Renal Replacement Therapy Surgery by Using the Gradient Boosting Regression Model

In order to reduce the doctor's subjective misjudgments, it is necessary to explore an effective predictive model to predict heparin dose during continuous renal replacement therapy surgery. In this paper, we use a combination of random forest and genetic algorithm to extract features and use EasyEnsemble algorithm to deal with unbalanced data. When training the model, this paper takes the ln transformation of the targeted variables, and then uses the Gradient Boosting Regression model and the Decision Tree Regression model to train. By comparing their mean absolute error, mean square error and square of R, finally, this paper chooses the Gradient Boosting Regression model as the final predictive model. The purpose of this study is to assist doctors in making more accurate judgments by this predictive model.
Performance Comparison of Explicit Pulsed Flip-Flops in Low Power and High-Speed Applications

This paper presents a comparative study in the performance of three pulse flip-flops (P-FFs) designs with an explicit pulse generation structure. It includes explicit pulse triggered data close to output flip-flop (ep-DCOFF), the conditional discharging flip-flop (CDFF) and signal feed-through flip-flop (SFTFF). The Conditional discharging technique is incorporated in CDFF. This flip-flop is having the advantages of reduced internal switching activities, fewer glitches at the output, negative setup time and small input-to-output delay features. The SFTFF is having a signal feed-through mechanism due to its true single phase clock latch based structure. The SFTFF reduces the problem of longest discharge path exists in other conservative flip-flops with the explicit style of pulse production. This design features a better performance in speed and power. The performance of P-FF compared using cadence software in CMOS 90nm technology. The post-layout simulation result shows that SFTFF outperforms ep-DCOFF and CDFF by 36.8% and 23.4% in power consumption respectively. The SFTFF is having the advantage of 22.4% and 48.2% in Data-to-Q delay when compared with ep-DCOFF and CDFF respectively.
Personality Effects on Students' Intrinsic Motivation in a Gamified Learning Environment

Promoting intrinsic motivation in classrooms is the central focus of many researchers. Particularly, several studies found that students' individual differences might affect their level of intrinsic motivation. Since personality is considered as one of the most important indicators of individual differences, this study investigates the effect of personality on students' level of intrinsic motivation in a gamified and self-determined online environment. The obtained results showed that only extraversion, conscientiousness and openness personality traits significantly affect students' level of intrinsic motivation to learn. These findings can be considered by designers of gamified learning environments to provide more game design elements in order to enhance students' intrinsic motivation based on their personalities.
SAFIRE: Scalable and Accurate Fault Injection for Parallel Multithreaded Applications

Soft errors threaten to disrupt supercomputing scaling. Fault injection is a key technique to understand the impact of faults on scientific applications. However, injecting faults in parallel applications has been prohibitively slow, inaccurate and hard to implement. In this paper, we present, the first fast and accurate fault injection framework for parallel, multi-threaded applications. uses novel compiler instrumentation and code generation techniques to achieve high accuracy and high speed. Using, we show that fault manifestations can be significantly different depending on whether they happen in the application itself or in the parallel runtime system. In our experimental evaluation on 15 HPC parallel programs, we show that is multiple factors faster and equally accurate in comparison with state-of-the-art dynamic binary instrumentation tools for fault injection.
Abnormality Detection and Severity Classification of Cells based on Features Extracted From Papanicolaou Smear Images using Machine Learning

A Papanicolaou Smear (PAP) test is a screening method developed for cervical cancer that involves the microscopic examination of cervical cells carefully extracted and spread out as a smear and stained specially. A Pap test reveals premalignant and malignant changes and the changes that are due to non-carcinogenic conditions like inflammation. The diagnosis of this test are based upon key features of the nucleus and cytoplasm of the affected cell or the cell under observation. This work is aimed at devising a classification algorithm using supervised methods to efficiently classify the affected cells from normal cells and further group the affected cells Logistic Regression. [9] All algorithms and models were trained and validated using the Azure Machine Learning Studio.
Sensor Fusion for IoT-based Intelligent Agriculture System

Sensors in agriculture are in use from weather monitoring to autonomous watering. Using low-cost sensors allows designers to create a prototype for a hardware environment to implement data acquisition and mining process. Thus, the relation between sensors can be understood and a test environment for sensor fusion can be created. In this paper, different input devices are synchronized by using a microcontroller system and each data, obtained from the sensors, are sent wirelessly by an (Internet of Things) IoT device to the cloud, by recording and monitoring from the graphical user interface on the web as a real-time environment to apply data mining algorithms thereafter. This study uses the regression trees to obtain the sensor data relations from 8 different data related to light, temperature, humidity, rain, soil moisture, atmospheric pressure, air quality, and dew point. Each sensor data has a different effect on the agricultural monitoring, however, reducing the number of sensors can reduce the cost of a system, by giving still accurate observations via sensor substitution proposed. Therefore, by using the regression trees, the classification of sensor data is inspected in this study. A test prototype of the hardware together with the software design is created for data monitoring and sensor fusion in different combinations. In the end, after fusion tests for all possible cases, outstanding results for each sensor substitution is presented. Temperature and dew point can be obtained using other sensors by fusing the train data on the regression tree by 92% and 84% accuracy respectively with a 5% numerical error margin in the leaf nodes on the regression tree.
Beyond Error Propagation: Language Branching Also Affects the Accuracy of Sequence Generation

Sequence generation tasks, such as neural machine translation (NMT) and abstractive summarization, usually suffer from exposure bias as well as the error propagation problem due to the autoregressive training and generation. Many previous works have discussed the relationship between error propagation and the accuracy drop problem (i.e., the right part of the generated sentence is often worse than its left part in left-to-right decoding models). In this paper, taking NMT as a typical sequence generation task, we measure the accuracy of the generated sentence with various metrics and conduct a series of analyses to deeply understand the accuracy drop problem. We obtain several interesting findings. First, The role of error propagation on accuracy drop is overstated in the literature, although it is indeed a cause to the accuracy drop problem. Second, Characteristics of a language play a more important role in causing the accuracy drop problem: the left part of the generated sentence in a right-branching language (e.g., English) is more likely to be more accurate than its right part, while the right part is more accurate for a left-branching language (e.g., Japanese). Our discoveries are also confirmed on other generation tasks (e.g., image captioning, abstractive summarization and language modeling) with multiple left/right-branching languages, as well as in various model structures.
About Satisfying String Stability Using Heterogenous Unidirectional Controllers

This paper deals with the problem of string stability in a chain of acceleration-controlled vehicles. There exist two different definitions of string stability in literature. It is known that those versions of string stability are impossible to achieve with any linear homogenous controllers if the vehicles only use relative information of few vehicles in front. Previous works have shown that adding absolute velocity into the controller, allows to satisfy the weaker definition of string stability. In this paper, we consider stronger definition of string stability, under using heterogenous controllers in platoon. We prove it is possible to guarantee the most strict definition of string stability using linear heterogenous unidirectional controllers with PD coupling gain between the vehicles.
Moderating Impact of Job Resources on the Relationship Between Job Stress with Turnover Intention and Creativity

The purpose of current research was to determine whether some aspects of job resources (e.g., training and reward, supervisor’s support and technological support) can moderate the relationship between job stress (e.g., overload, ambiguity and conflict) with turnover intention and creativity. On the basis of the conservation resources approach and job demands-supportperformance model, current research investigated whether training, reward, and supervisor’s and technological support serve as a buffer to relieve the impact of job stress on turnover intention and creativity. Accordingly, randomly selected of one hundred eighty-seven employees from a public sector organization in Bandarabas, Iran, answered the job stress, turnover intention, creativity and job resources scales. Results suggested that training and technological support moderates the relationship between role conflict and turnover intention. Also, technological support was found to moderate the role ambiguity and overload and crativity relationship.