Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-automatic Feedback for Improving Architecture Conformance to Microservice Patterns and Practices

Microservices are one of the most recommended architectural styles for distributed applications that support independent development and deployment, enable rapid release, and are highly scalable and polyglot. Many well-established patterns and best practices have been documented in the literature. As there are many such guidances, they have numerous interdependencies, and system designs must adhere to many other architecture constraints, too, implementations do not always conform to those guidances. In complex or large systems, it can be hard and tedious to spot violations. Our work aims to offer automated support for architecting during the continuous evolution of microservice-based systems. More specifically we aim to provide the foundations for an automated approach for architecture reconstruction, assessing conformance to patterns and practices specific for microservice architectures, and detect possible violations. Based on this, we provide actionable options to architects for improving architecture conformance as part of a continuous feedback loop. That is, our goal is to support architecting in the context of continuous delivery practices, where architecture violations are continuously analyzed and fix options are continuously suggested.
Deep Vocoder: Low Bit Rate Compression of Speech with Deep Autoencoder

Inspired by the success of deep neural networks (DNNs) in speech processing, this paper presents Deep Vocoder, a direct end-to-end low bit rate speech compression method with deep autoencoder (DAE). In Deep Vocoder, DAE is used for extracting the latent representing features (LRFs) of speech, which are then efficiently quantized by an analysis-by-synthesis vector quantization (AbS VQ) method. AbS VQ aims to minimize the perceptual spectral reconstruction distortion rather than the distortion of LRFs vector itself. Also, a suboptimal codebook searching technique is proposed to further reduce the computational complexity. Experimental results demonstrate that Deep Vocoder yields substantial improvements in terms of frequency-weighted segmental SNR, STOI and PESQ score when compared to the output of the conventional SQ-or VQ-based codec. The yielded PESQ score over the TIMIT corpus is 3.34 and 3.08 for speech coding at 2400 bit/s and 1200 bit/s, respectively.
Linear and Nonlinear Control Approaches for the Cart Inverted Pendulum Problem

This paper presents a complete review of the cart inverted pendulum (CIP) problem. The design, modeling, and control of such system as well as the implementation of a mechatronic prototype are herein presented. Several linear and nonlinear approaches such as PD, PID, and adaptive control are proposed to stabilize the system. Both theoretical and experimental performances are compared to evaluate the precision of the mathematical formulations. The aim of this study is to serve as a tutorial or supporting material for understanding the dynamics governing this system together with the potential control strategies.
Modified Dual Input Dual Output DC-DC converter for Bladeless wind energy harvesting System

In this paper, a new modified Dual Input Dual Output (DIDO) DC-DC converter is investigated to dispense power from integrated input source to integrated output. The proposed converter is synthesized by reshaping the dual input source instead of single input with single input multiple output (SIMO) converter. The first input from bladeless wind turbine through inverter and the second input are from rechargeable battery. The proposed converter setup has the capability to step up and step down the power with high voltage gain and improved efficiency which is used to harvest energy from bladeless of the wind turbine. The voltage ratings are different for dual input voltage sources and have the potential to provide power to load, without interruption even the collapse of any one voltage source. The first output supplies power directly to load and second output is deployed for being charged the rechargeable battery. The investigated converter is simulated through Matlab/simulink tool and a prototype model of 100W has been developed. The proposed converter precisely adopts effective shoot through fortification. The converter have certain benefits like improved dynamic behavior, able to utilize maximum energy, can be utilized for variable speed drives with efficient regulation during step change load and attains a efficiency of 92.3%.
Bug Question Answering with Pretrained Encoders

Bug question answering is an effective way to acquire the required bug information and to help bug comprehension. Many existing approaches use keyword matching techniques to obtain more bug information directly without understanding the semantic information of bug data, which make the returned results irrelevant to the input queries. To alleviate this problem, we present a novel bug question answering approach named BERT-BugQA that takes advantage of the Bidirectional Encoder Representations from Transformers (BERT) which can fully consider the bidirectional context of bug information. In special, we design a common paradigm to construct the bug reading comprehension dataset for this approach. Empirical study demonstrates that BERT-BugQA is effective to automatically obtain the answers, and the F1-score values of Mozilla and Eclipse project are 0.84 and 0.83, respectively, which are better than the state-of-the-art Q&A approaches.
Performance Comparison for Different Neural Network Architectures for chest X-Ray Image Classification

Nowadays, the diagnose of diseases or abnormally clinical conditions of the chest can be done by reading chest X-Ray images by radiologists. The diagnosis from radiologist is considered qualitative analysis, which collects and analyzes the non-numerical data, which relates to the individual’s experience. Consequently, errors may occur during the diagnosis, such as misdiagnosis. Therefore, this research has a purpose for developing the software program which gathers Medical Image Processing and Artificial Intelligence focuses on Deep Learning Neural Network, implementing on MATLAB program by training and classifying on labeled and non-labeled chest X-Ray images between normal condition and 13 abnormal cases. It is also stated that the trained model results are considered quantitative analysis, which can be confirmed or rejected by testing the causal relationship between variables. In this research, we apply several pre-trained architectures to our network, which, finally, the ResNet-50 architecture gave the highest percentage of accuracy compared with other architectures. Moreover, it is the network that appropriates for our image dataset.
An Efficient Pattern Recognition Based Method for Drug-Drug Interaction Diagnosis

The diagnosis of interactions between two drugs is an essential procedure in drug development. Many medical tool’s offer inclusive records related to DDI. However, this tool’s results are not very satisfactory. The main aim is to propose an efficient approach based on pattern matching that identifies the interaction between two drugs. In this study, the goal is to collect the data from the DrugBank, which is a publicly available source. The drug-related data includes drug ID, drug names, and various kinds of sentences of drug-drug interaction. Drug names will be identified by drug names dictionary defined in the corpus, and sentences will be determined according to given patterns. These sentences will treat as input data, and preprocessing steps will perform in these sentences. Various types of features are selected for machine learning classification. Then all the attributes will be classified into desired classes. The proposed method gains 95.4% accuracy from the random forest classifier.
Onboarding to Open Source Projects with Good First Issues: A Preliminary Analysis

While open source software (OSS) development projects always seek contributions from newcomers to make the projects sustainable, newcomers often face with a challenge of onboarding. For developers with little experience, it is difficult to decide which of the many OSS projects to contribute to and which issues to tackle. To mitigate the barrier against onboarding, some OSS projects start using a label so called good first issue (GFI) which indicates the issue is easy to resolve and suitable for newcomers to tackle. The final goal of this study is to construct a model for recommending good first issues to help reduce maintainers’ manual effort of selecting and labeling issues as GFIs and at the same time to help find GFIs suitable for each newcomer. Toward the final goal, we analyze GFIs in GitHub to deeply understand the current state of the GFI mechanism and its impact on new members’ onboarding. This paper reports a result of our preliminary analysis of GFIs, based on 9,475 GFIs collected from 11 famous OSS projects in GitHub. We find that (1) developers who have resolved GFIs have 92 fewer median PR (pull request) posts than developers who have resolved regular issues (i.e., developers tackling GFIs have less experience, compared to other developers), (2) on average, GFIs are resolved 32.5% more than regular issues (i.e., GFIs are easier to resolve. The dataset of GFIs might be able to be used as a training set for recommending GFIs to developers with less experience), and however (3) the percentage of developers who keep contributing to the same project even after resolving GFIs varies greatly from project to project (24.9% to 83.9%) (i.e, GFIs guide newcomers’ onboarding only in particular projects). Based on the analysis result, we discuss the contents of GFIs which tend to keep newcomers on OSS projects.
Enhancing the Prediction of MERS-CoV Survivability Using Stacking-Based Method

Saudi Arabia has recorded the highest Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infections globally. Nearly 2,000 cases have been recorded in Saudi Arabia, with a high mortality rate, since the outbreak in 2012. The source of the disease remains unclear, and it has been evident that MERS-CoV can spread through communicating, directly or indirectly, with humans or animals. In our study, we evaluated different machine learning models that can accurately predict the probability of a patient's recovery from MERS-CoV. The data was from the Saudi Ministry of Health’s website corresponding to the years from 2015 to April 2018. A stacking-based ensemble learning has been built to increase the performance of individual models. In our study, we examined the following individual classifiers: naïve Bayes, support vector machine, logistic regression, k-nearest neighbour, Bayesian networks, J48, and random forest along with the proposed stacking-based model. The results show that, in most cases, simple machine learning techniques perform well when predicting recovery unlike predicting death cases. The proposed stacking-based ensemble learning method has shown improvement in the prediction of death cases while maintaining a good predictive power for recovery cases. The proposed technique, which is an ensemble learning method, performed best with 0.751 balanced accuracy and 0.750 G-Mean. Predicting the survivability of patients can help in decision-making on prevention and recovery of MERS-CoV.
An Improved Offset Min-Sum LDPC Decoding Algorithm for 5G New Radio

This paper presents an Improved Offset Min-Sum (IOMS) algorithm for Low-Density Parity-Check (LDPC) decoders, used in 5th generation (5G) mobile communication. The proposed algorithm IOMS applies a multiplication factor to check-node update information messages. The simulation results show that the proposed IOMS algorithm offers a better error correction performance than the existing Min-Sum-based algorithms at the expense of a slight increase in complexity. At the BER of 10–5, the IOMS achieves a decoding gain of 0.1dB with respect to the OMS algorithm.
OTA: An Operation-oriented Time Allocation Strategy for Greybox Fuzzing

Coverage-based greybox fuzzing (CGF) has been widely studied and commonly used for software vulnerability detection. Existing CGF fuzzers fairly allocate execution time for each mutation operation to generate test cases. However, the fair-time-allocation strategy is revealed to be inefficient by our significant experimental observation that different operations have heterogeneous effectiveness on coverage. Those ineffective operations with vast test cases thus occupy the majority of limited runtime, reducing the opportunities for effective operations to explore more paths and find potential vulnerabilities.In this paper, we propose a novel operation-oriented time allocation strategy OTA, which dynamically allocates operation execution time in real time to cope with the effectiveness variation per operation. OTA has three distinguishing advantages: (1) the execution time per operation is novelly initialized on demand and program-dependent; (2) the execution time for each operation is dynamically weighted by its real-time effectiveness on exploring new coverage; (3) the determination of the execution time per operation is well controlled to achieve a quick convergence. Extensive experiments based on real-world programs and the LAVA-M dataset have been conducted to evaluate the path discovery and vulnerability detection abilities of OTA, which substantially outperforms 5 state-of-the-art fuzzers. In addition, OTA exposes 18 previously unknown vulnerabilities in 6 well-tested programs with 13 confirmed with new CVE IDs.
0.55 W, 88%, 78 kHz, 48 V-to-5 V Fibonacci Hybrid DC–DC Converter IC Using 66 mm3 of Passive Components With Automatic Change of Converter Topology and Duty Ratio for Cold-Crank Transient

For 48 V mild hybrid vehicles, a sub-0.5 W, 48 V-to-5 V dc–dc converter fulfilling: 1) high efficiency with small volume; 2) constant switching frequency of less than 500 kHz to avoid the frequency band of AM radio; and 3) constant output voltage (VOUT) against the sudden drop of input battery voltage (VIN) under cold cranking is required. To meet the requirements, a 0.55 W, 88%, 78 kHz, 48 V-to-5 V Fibonacci hybrid (FH) dc–dc converter IC using 66 mm3 of passive components is proposed. In the FH dc–dc converter, by adding an inductor and an output capacitor to a 1/5 Fibonacci switched-capacitor (SC) dc–dc converter, the SC dc–dc converter also works as a buck converter without adding power transistors. When VIN drops from 48 V to 20 V in 1 ms in the automotive cold cranking, the FH dc–dc converter cannot keep 5-V VOUT, because the output voltage of the internal 1/5 SC dc–dc converter drops from 9.6 to 4 V and the internal buck converter cannot generate 5-V VOUT from 4 V. To solve the problem, a new control method named automatic change of converter topology and duty ratio (ACCD) is proposed. In ACCD, when VIN drops less than 31 V, the converter topology automatically changes from 1/5 SC dc–dc converter to 1/3 SC dc–dc converter and the duty ratio of the pulsewidth modulation signal automatically decreases 3/5 times, thereby achieving the constant 5-V VOUT under cold cranking. To reduce the volume of the FH dc–dc converter, all transistors and diodes, including ten power transistors, gate drivers, bootstrap circuits, and the controller, are fully integrated on 4.6 mm × 2.3 mm IC fabricated with 180 nm BCD process. In the measurements, the proposed 0.55 W, 48 V-to-5 V FH dc–dc converter IC achieved the highest efficiency (88%) with the smallest volume of passive components (66 mm3) at the lowest switching frequency (78 kHz) compared with previous publications.
Six-track Standard Cell Libraries with Fin Depopulation, Contact over Active Gate, and Narrower Diffusion Break in 7nm Technology

In this article we present three 6-track standard cell libraries based on ASAP7 PDK which is extended to include three technologies, contacts over active gates (COAG), fin depopulation, and a diffusion break taking a space of one contacted poly pitch (CPP). All these three technologies are invented to reduce standard cell area and thus chip area. Experimental results show that fin depopulation solely can achieve 8.3% area saving, COAG brings about another 9.3%, and a diffusion break of 1 CPP adds another 2.5% more. These three technologies all together bring about 20% area saving when compared with that obtained by employing a 7.5-track cell library without excising these three technologies.
Adaptive Data Generation and Bidirectional Mapping for Polyp Images

Polyps are one of the main reasons of colorectal cancer. Early screening of diseases has critical value for clinical diagnosis. However, considering variations in the size and shape of polyps, colonoscopy is a challenging task in the diagnosis of polyps. Traditional methods depend on the doctor’s experience. Eye fatigue and lack of attentiveness during long and back-to-back procedures may lead to a decrease in detection rate, and the estimated average missed diagnosis rate is about 4%-12%, which is time-consuming, labor-intensive, and ineﬃcient. And another challenging task is that medical images are scarce and fraught with legal concerns regarding patient privacy. Inspired by this limitation, we propose a new simulated transfer model based on Cycle Generative Adversarial Nets. It learns a mapping from medical images to segmented images and then trains another learning model to generate synthetic medical images. We use the CVC-ClinicDB datasets to train our model. 196 ETIS-LaribPolypDB test datasets to evaluate our proposed method. For generated segmented images, our method achieves a dice coeﬃcient of 0.763 and AUC of 0.914. For generated synthetic polyp images, we use ’Toy Experiment’ to verify the performance of our method. You can see more result information in this website https://github.com/qiqi7788/Adaptive-Data-Generation-and-Bidirectional-Mappingfor-Polyp-Images.
Research on Prediction of Short-term Passenger Flow of Urban Rail Transit Based on Deep Neural Network

Aiming at the existing short-term traffic flow prediction models, this paper proposes a deep neural network prediction model CNN-LSTM. This model is a combination of the convolutional neural network and the long-short term memory neural network, to better mine the time characteristics of the short-term traffic passenger flow data. This research takes Shanghai metro passenger flow data as an example, trains MLP, LSTM, GRU, CNN, and CNN-LSTM models, compares the applicability of five kinds of prediction model for different time granularity, verifies the accuracy of the models. And the research results show that the precision of the prediction models improves with the increase of the granularity of time. Compared with the existing single model, the CNN-LSTM hybrid model has more accurate prediction performance, its model errors are significantly reduced, and its goodness of fit is improved, which can provide data support for urban rail transit management and operation, and increase its adaptability to emergencies.
Exploring Tourist Behavior from Social Media Using Geotagged Photographs

In this paper, we propose an approach for exploring tourists’ behavior based on the extracted dataset from geotagged photographs. We transform the dataset to a suitable format type and apply k-mean clustering to cluster the different tourists’ behavior. After that, the tourists’ behavior of each cluster, especially behavior regards interesting attraction, has been analyzed by statistical test. Entropy also has been employed to measure the diversity of place in each attraction category for presenting each clusters’ characteristics. The results have been demonstrated that k-mean clustering presents the tourists’ behavior into eight groups and each of the tourists prefers conspicuously different attractions.
Three-term Optimal Controller for Discrete-Time Control System

For the discrete-time control system represented in state-space form, a three-term controller has been designed based on three successive sampled values of output for the optimal control of the system. It is of much reduced order as there is no necessity of having complete state feedback. Limitation, however, is that ideally number of outputs must be half of number of states of the system. Otherwise, it calls for pseudo-inverse of a matrix. In such case control achieved of course will be a sub-optimal one.
Subcarrier Allocation for Rate Maximization in Multiuser OFDM NOMA Systems on Downlink Beamforming

In this paper, we present resource allocation algorithms to maximize system capacity for multicarrier nonorthogonal multiple access (MC-NOMA) systems on downlink beamforming. The proposed solution has two steps. First, a user pairing algorithm is applied, and then a subcarrier allocation scheme is proposed to maximize sum capacity. In this system under consideration, each subcarrier can be assigned to two users per antenna beam for transmitting data. After performing the user pairing algorithm, the subcarrier allocation process is performed where a subset of subcarriers is assigned to the cluster which includes groups of users for data delivery. Each group have two users, which are a strong user (SU) and a weak user (WU) and the data steam is transmit to one group per one antenna beam. The simulation results illustrate that the proposed system outperforms the OFDMA system.
Deep Learning assisted Cross-Family Profiled Side-Channel Attacks using Transfer Learning

Side-channel analysis (SCA) utilizing the power consumption of a device has proved to be an efficient technique for recovering secret keys exploiting the implementation vulnerability of mathematically secure cryptographic algorithms. Recently, Deep Learning-based profiled SCA (DL-SCA) has gained popularity, where an adversary trains a deep learning model using profiled traces obtained from a dummy device (a device that is similar to the target device) and uses the trained model to retrieve the secret key from the target device. However, for efficient key recovery from the target device, training of such a model requires a large number of profiled traces from the dummy device and extensive training time. In this paper, we propose TranSCA, a new DL-SCA strategy that tries to address the issue. TranSCA works in three steps – an adversary (1) performs a one-time training of a base model using profiled traces from any device, (2) fine-tunes the parameters of the base model using significantly less profiled traces from a dummy device with the aid of transfer learning strategy in lesser time than training from scratch, and (3) uses the fine-tuned model to attack the target device. We validate TranSCA on simulated power traces created to represent different FPGA families. Experimental results show that the transfer learning strategy makes it possible to attack a new device from the knowledge of another device even if the new device belongs to a different family. Also, TranSCA requires very few power traces from the dummy device compared to when applying DL-SCA without any previous knowledge.