Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Rider Optimization Algorithm for Solving Engineering Optimization Problems

This paper proposed an improved optimization algorithm for engineering optimization problems. This improved optimization algorithm, namely, gravitational search strategy-assisted rider optimization algorithm (GSSROA), combines the gravitational search strategy (GSS) with the rider optimization algorithm (ROA). In the GSSROA, the search step can be adaptively adjusted in each iteration. The performance of the GSSROA has been verified with three benchmark engineering design problems. The results indicate that the proposed GSSROA has the feasibility and good robustness. Then, the GSSROA is applied to optimize the weight of the front axle of an automobile under the non-linear constraints. The results show that the GSSROA can achieve the optimal structure with a weight that is reduced by 16.82% compared with the initial design.
End-to-End Speech Recognition Sequence Training With Reinforcement Learning

End-to-end sequence modeling has become a popular choice for automatic speech recognition (ASR) because of the simpler pipeline compared to the conventional system and its excellent performance. However, there are several drawbacks in the end-to-end ASR model training where the current time-step prediction on the target side are conditioned with the ground truth transcription and speech features. In the inference stage, the condition is different because the model does not have any access to the target sequence ground-truth, thus any mistakes might be accumulated and degrade the decoding result over time. Another issue is raised because of the discrepancy between training and evaluation objective. In the training stage, maximum likelihood estimation criterion is used as the objective function. However, the ASR systems quality is evaluated based on the word error rate via Levenshtein distance. Therefore, we present an alternative for optimizing end-to-end ASR model with one of the reinforcement learning method called policy gradient. The model trained the proposed approach has several advantages: (1) the model simulates the inference stage by free sampling process and uses its own sample as the input, and; (2) optimize the model with a reward function correlated with the ASR evaluation metric (e.g., negative Levenshtein distance). Based on the result from our experiment, our proposed method significantly improve the model performance compared to a model trained only with teacher forcing and maximum likelihood objective function.
Dispute Resolution for Smart Contract-based Two-Party Protocols

Blockchain systems promise to mediate interactions of mutually distrusting parties without a trusted third party. However, protocols with full smart contract-based security are either limited in functionality or complex, with high costs for secured interactions. This observation leads to the development of protocol-specific schemes to avoid costly dispute resolution in case all participants remain honest. In this paper, we introduce SmartJudge, an extensible generalization of this trend for smart contract-based two-party protocols. SmartJudge relies on a protocol-independent mediator smart contract that moderates two-party interactions and only consults protocol-specific verifier smart contracts in case of a dispute. This way, SmartJudge avoids verification costs in absence of disputes and sustains interaction confidentiality among honest parties. We implement verifier smart contracts for cross-blockchain trades and exchanging digital goods and show that SmartJudge can reduce costs by 46–50% and 22% over current state of the art, respectively.
A methodology to characterize and compute correlation between traffic congestion and health issues via social media

Traffic congestion is one of the most significant problems around the world. Literature shows various analyses of real time traffic incidents detection and crowd sensing. However, few researchers quantified the traffic congestion impacts on public health. To the best of our knowledge, there is no study, which determines the correlation between traffic congestion and public health issues via social media. In this paper, we propose a methodology to compute the correlation between traffic congestion and public health issues through social media analysis. To purse this task, we have used topic modeling and sentimental analysis. We mined a collection of 97 million tweets extracted from Twitter. Subsequently, different filters are applied to get the most traffic-congested locations around the world and the top health issues in the corresponding areas. Additionally, we have performed sentimental analysis to get the public perception about the initiatives taken to improve the health issues in those regions. We have found 36 most traffic congested cities around the world, such as Mexico, Bangkok, Jakarta and Chongqing etc. Apart from that, heart diseases, respiratory and psychological problems are identified as the common problems in traffic congested cities. Almost 71% public comments shows the negative sentiments. Which reflects their level of frustration about the steps taken to reduce the traffic by the higher authorities.
Network Traffic Classification Analysis Using Machine Learning Algorithms

In the world of networking, it sometimes becomes essential to know what types of applications flow through the network for performance of certain tasks. Network traffic classification sees its main usage among ISP’s to analyze the characteristics required to design the network and hence affects the overall performance of a network. There are various techniques adopted to classify network protocols, such as port-based, pay-load based and Machine Learning based, all of them have their own pros and cons. Prominent nowadays is Machine Learning technique due to its vastness in usage in other fields and growing knowledge among researchers of its better accuracy among others when compared. In this paper, we compare two of the basic algorithms, Naïve Bayes and K nearest algorithm results when employed to networking data set extracted from live video feed using Wireshark software. For an implementation of Machine learning algorithm, python sklearn library is used with numpy and pandas library used as helper libraries. Finally, we observe that K nearest algorithm gives more accurate prediction than Naïve Bayes Algorithm, Decision Tree Algorithm and Support Vector Machine.
A Lightweight Assisted Vulnerability Discovery Method Using Deep Neural Networks

Thousands of vulnerabilities are discovered in programs every day, which is extremely harmful to software security. Thus, discovering vulnerabilities in projects has become a central issue. Facing a sustained growth of software complexity and large code size, manual code auditing becomes time-consuming and labor-intensive. With more open source programs available and a high degree of code formalization, it is possible to study features from source code to guide vulnerability discovery work. In this paper, we present a lightweight-assisted vulnerability discovery method using a deep neural network (LAVDNN) to detect weakness and to provide guidance for manual auditing. The method proposed in this paper leverages function names as semantics features to uncover weak functions in large-scale open source programs. First, we extract function names and classify into weak and benign datasets. Then, we construct deep neural networks and compare the performances of different models. According to the experimental results, our method performs well for both C/C++ and Python programs, with the $F_{2}$ -score reaching 0.91 and 0.915, respectively. Ultimately, we evaluate the method by comparing with other approaches using the libraries FFmpeg 0.6 and LibTIFF 4.0.6. The results show that the LAVDNN could narrow the range of functions to be analyzed and report more weak functions without any prior vulnerability information. As a lightweight-assisted tool, the LAVDNN significantly reduces the false positive rate and hardly misses weak functions.
A Static-dynamic Approach for UI Model Generation for Mobile Applications

Nowadays, smartphone users are increasingly relying on mobile applications to complete most of their daily tasks. To ensure acceptable quality and to meet its specifications, mobile apps need to be tested thoroughly. As testing mobile apps becomes challenging and tedious, test automation can alleviate this process. Model-based testing is an approach for test automation that is popularly used to test mobile applications. In order to benefit from model-based testing, there is a need for technique and tool for automated model generation. Therefore, this paper presents a hybrid approach for automated User Interface (UI) model generation for mobile applications. It performs static analysis of application’s bytecode to extract UI information, followed by a dynamic crawling to systematically explore and reverse engineer a model of the application under test. We then evaluate our approach on several open-source mobile applications. The results showed that our approach can generate a high-quality model from mobile applications.
Continuous Mechanical Indexing of Single-Cell Spheroids Using a Robot-Integrated Microfluidic Chip

In single-cell/spheroid analyses, flow cytometry plays a key role in high-throughput measurements. Among the considerable numbers of indexes available for a target evaluation, mechanical characteristics such as a Young's modulus have been focused upon as new indexes related to physiological condition. However, a continuous mechanical indexing system has yet to be achieved due to the difficulty in flow control, which brings about a fluctuation of the force sensor probe. In this letter, we propose an automated mechanical indexing system of spheroids. By utilizing a combination of two syringe pumps, we succeeded in positioning the target spheroids without an undesirable pinching. We conducted experiments on the continuous mechanical indexing of single-cell spheroids using 26 mesenchymal stem cell spheroids. The evaluation results of positioning showed that the error in the position control was 14.1 $\mu$m with a standard deviation of 87.7 $\mu$m, which corresponded to a measurement error of the Young's modulus of under 2%. In addition, we succeeded in achieving a measurement throughput of 3.14 spheroids per minute, which is approximately 2 times faster than a manual operation. Finally, we demonstrated the on-chip sorting of single spheroids based on the mechanical index to show the adaptability of the proposed method toward the flow cytometry.
Focus-on-form in a Collaborative Output Task: Exploring a Dictogloss

This study was motivated by the pedagogic interest in dictogloss as a procedure combining form-focused and communication driven pair/group work leading to collaborative dialogue and looked inside a dictogloss to analyze learners’ spoken attention to form while doing the task. The dictogloss task employed in this study elicited discussions on a wide variety of forms, as has been claimed. However, there was a difference in the amount of form-focused episodes (FFEs) the learners produced. The linguistic feature targeted by the study was less frequently noticed and discussed by the learners although one single chosen structure appeared in the text repeatedly. The success rate of the FFEs was moderately high but the problem was that the learners had no way of knowing which linguistic forms to those problems they identified were correct at the moment when they had to make a decision. These results were discussed and implications for pedagogy were suggested.
Computer Aided Brain Tumor Detection via Rule Based Eliminated Watershed Segmentation

Brain cancer is one of the most fateful diseases today. Early diagnosis is of great importance in the treatment of this disease. To accomplish a fast and accurate diagnosis, numerous studies have been performed around the world. In this study, a computer aided tumor detection task is proposed for brain MR images. To prevent over-segmentation a set of methods such as bilateral, gauss, order statistics filters, morphological and sharpening operations are applied for denoising, emphasizing fine details and enhancement steps prior to watershed segmentation. Finally, a rule based elimination is proposed to reduce the false positive detections and increase the performance. Experimental results demonstrate that the proposed method is satisfying to detect brain tumors.
Streamflow Prediction with Deep Learning

In this study, some part of streamflow modelling and data analysis carried out within the frame of a comprehensive project on the web-based development of a watershed information system is reported. Flowrate prediction is a challenging work because it involves nonlinear, chaotic, multidimensional, instantaneous and continuous processes. The study basically aims to present the daily discharge predictions from the actual discharge using Recurrent Neural Networks (RNNs) as a deep learning approach. RNN is back ended by the LSTM (Long Short-Term Memory) and improved by an Adam optimization algorithm. The initial results are found promising compared to those of conventional Artificial Neural Network (ANN) models.
Model Reference Adaptive Control of Load Transporting System on Unmanned Aerial Vehicle

The effective control of gimbal, Vertical Take-Off and Landing (VTOL) and Load Transporting System (LTS) in Unmanned Aerial Vehicles (UAV), which are widely used in mapping, search-and-rescue, exploration and surveillance, border security, real-time image transfer by tracking target and leaving payloads to specified targets in hazardous regions directly affect task performance. In this study, Model Reference Adaptive Control (MRAC) of LTS which has an important role to be able to leave payloads on UAV in real time with minimum error to specified targets is carried out and its effect on duty performance is investigated. The three payloads in the cubic structure are transported by LTS originally designed for real-time specified targets. DC gear motors are used in the LTS so that payloads can be left to real time specified targets. Environmental testing is conducted taking into account the limitations of the physical properties of the LTS on the autonomously moving UAV, and the impact on MRAC’s mission performance is examined.
Hand Gesture Recognition Systems with the Wearable Myo Armband

The hand gesture recognition systems deal with identifying a given gesture performed by the hand. This work addresses a hand gesture recognition method to classify and recognize the numbers from 0 to 9 in Turkish Sign Language based on surface electromyography (EMG) signals collected from a wearable device, namely the Myo armband. To accomplish such a goal, we have utilized machine learning techniques to recognize the hand gestures. In this context, seven different time domain features are extracted from the raw EMG signals using sliding window approach to get distinctive information. Then, the dimension of the feature matrix is reduced by using the principal component analysis to reduce the complexity of the deployed machine learning methods. The presented study includes the design, deployment and comparison of the machine learning algorithms that are k-nearest neighbor, support vector machines and artificial neural network. The results of the comparative comparison show that the support vector machines classifier based system results with the highest recognition rate.
Leaky cavity 850 nm Single-Mode VCSELs for high-speed data transmission over multi-mode fiber

Optical data transmission with 850 nm Vertical-Cavity Surface-Emitting lasers (VCSELs) is a key technology in ultra-short data interconnects. However, for data transmission over a longer distance, dispersion in multi-mode fiber limits the performance. To achieve high data rates at a distance above 1 km VCSELs with narrow optical spectrum can be used. Oxide-confined VCSELs can be engineered in a way to increase leakage of the high order transverse optical modes thus promoting single-mode operation. Such leaky single-mode VCSELs experience smaller dispersion and hence allow transmission over longer distances.Important feature of the leaky design is the leakage of high order modes through the second cavity of the VCSEL. This effect can be used for lateral integration to photo-diodes or waveguides.In this paper, we demonstrate high speed transmission at 25 Gbit/s up to 1 km of OM4 multimode fiber using 850 nm leaky VCSELs. The results are benchmarked with a reference multi-mode VCSELs.
Short-Term Wind Speed Forecast With Low Loss of Information Based on Feature Generation of OSVD

Improving the accuracy of wind speed forecast can reduce the randomness and uncertainty of the wind power output and effectively improve a system’s wind power accommodation. However, the high-dimensional historical wind speed information should be taken into account in the wind speed forecast, which increases the complexity of the model and reduces the efficiency and accuracy of a forecast. Feature selection by the Filter method can effectively reduce the feature dimension, but losing all the information of low-importance features. Although the feature reduction can retain the partial information of all features, it causes the loss of the partial information of high-importance features. In order to reduce the information loss caused by traditional FS and FR, short-term wind speed forecast with low information loss based on OSVD feature generation is proposed. First, the original wind speed series is denoised by OVMD. Then, based on the 96-dimensional original wind speed feature set, the OSVD is used to generate features. Furthermore, the extended original feature set EFS is obtained by combining the initial feature set with the features generated by OSVD. Gini importance is used to measure the importance of all features in EFS, and the forward feature selection is combined with random forests to determine the optimal subset. Finally, the optimal model determined by the new method is compared with seven models to verify the advancement of the new method. The experiments show that it reduces the information loss. Thus, the model has a higher forecast accuracy than the traditional model.
Trend Analysis in Machine Learning Research Using Text Mining

This paper aims to identify the trends in machine learning research using text mining. The researcharticles contain significant knowledge and research results. However, they are long and have many noisy results such that it takes a lot of human efforts to analyze them. Text mining can be used to analyze and extract useful information from a large number of research articles quickly and automatically. Text mining is the method of defining innovative, and unseen knowledge from unstructured, semi-structured and structured textual data. This knowldege contributed to very important information that can derive from textual data. In this paper, text mining methods are applied to detect trends of terms that occur in the research articles and how they varies over time. We collected21,906 scientific papers from six top journals in the field of machine learning published in period 1988-2017 and analyzed them using text mining. Our result analysis shows a changing trend of various terms in Machine learning research in three decades. The analysis of our study helps the upcoming researchers to explore the significant research area of machine learning.
ACCOUNTING FOR THE RELATIONSHIP BETWEEN MONEY AND INTEREST RATES

In time-series from the United States, the relationship between the money to income ratio and the nominal interest rate is a negative and stable one. In Swedish data, there is no such stable relationship. In this paper, we argue that this difference can be explained by the differences in the shock processes that have hit the two countries. Using a dynamic general equilibrium model driven by shock processes estimated to fit the two countries, we find that we can account for the main properties of the data remarkably well.
Evaluation of human upper limb motion comfort based on motion capture system

Biomechanical study of human upper limb is one of the most referenced aspects for the optimization of upper limb posture and the design of related products. A real-time optical motion capture system—Motion Analysis Eagle Digital System has been used to capture the real-time position of the key points of the human body in the course of the specified action. In this paper, the focus of the work is to calculate the experimental data of the human upper limb by MATLAB software. We get the each joint angle of the human upper limb by the inverse kinematics, and the joint torque is obtained by the inverse dynamic. We put forward to use optimization functions to calculate the muscle forces, and the optimization algorithm is verified by OpenSim simulation. Last, the calculation and evaluation flow of human upper limb comfort is established. The experiment proves that the human upper limb muscles are in the comfort state during taking the book from the right rear of the body. The experimental conclusion put a theoretical foundation for studying on human upper limb motion and designing of related auxiliary products
Emotion Recognition from Speech Signals using Excitation Source and Spectral Features

The task of recognition of emotions from speech signals is one that has been going on for a long time. In the previous works, the dominance of prosodic and spectral features have been observed when it comes to recognition of emotions. But a speech signal also consists of Source level information which gets lost during this process. In this work, we have combined several spectral features with several excitation source features to see how well the model can perform the emotion recognition task. For the task in hand we have taken 3 databases namely, Berlin Emotional Database (Berlin Emo-DB), Surrey Audio-Visual Expressed Emotion (SAVEE) Database and Toronto emotional speech set (TESS) Database. The reason behind taking these databases is that the variation they offer is effective to judge the robustness of the recognition model. We chose Sequential Minimal Optimization (SMO)and Random Forest to perform classification.
Inter-Intra Information Preserving Attributed Network Embedding

To alleviate the problem caused by the sparsity of network structure which is often the case in large-scale network, attributed network embedding has attracted an increasing amount of attention. Some existing attributed network embedding models integrate linkage structure and node attribute by adding a consistency criterion on the structure representation and attribute representation, whereby the similarity between them can be ensured. However, to enforce the structure and attribute representations to be similar may cause the information distortion due to the inherent difference between these two kinds of information. Additionally, the existing models mainly focus on learning the inter-relation between structure and attribute information, while the intra-characteristic of each information is ignored, leading to the information loss. To address the above two problems, we propose a novel model named Inter-Intra Information Preserving Attributed Network Embedding (IINE) to effectively learn the node representations of attributed network, which can not only capture the inter-relation between structure and attribute information with less information distortion but also effectively preserve the intra-characteristic of each information. The proposed model is composed of a primary model named coupled autoencoder and two auxiliary models named structure miner and attribute miner. The coupled autoencoder trains the node representation by smoothly combining both structure and attribute information, while the structure miner and attribute miner are utilized to further mine the intra-characteristic from the corresponding information so as to assist the primary model. The Extensive experiments are conducted on seven real-world datasets, and the results confirm the superior performance of IINE over several state-of-the-art models.