Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Building Robust DNN Applications: An Industrial Case Study of Evolutionary Data Augmentation

Data augmentation techniques that increase the amount of training data by adding realistic transformations are used in machine learning to improve the level of accuracy. Recent studies have demonstrated that data augmentation techniques improve the robustness of image classification models with open datasets; however, it has yet to be investigated whether these techniques are effective for industrial datasets. In this study, we investigate the feasibility of data augmentation techniques for industrial use. We evaluate data augmentation techniques in image classification and object detection tasks using an industrial in-house graphical user interface dataset. As the results indicate, the genetic algorithm-based data augmentation technique outperforms two random-based methods in terms of the robustness of the image classification model. In addition, through this evaluation and interviews with the developers, we learned following two lessons: data augmentation techniques should (1) maintain the training speed to avoid slowing the development and (2) include extensibility for a variety of tasks.
Blind Source Separation Anti-jamming Technology Based on Pade-FastICA Algorithm

In order to improve the resistance of communication systems to the jamming signals from multiple jamming sources without any prior information, based on the conventional fast independent component analysis algorithm (FastICA), we propose a Pade-FastICA algorithm. The main idea of the proposed algorithm is to utilize the Pade approximation theory to approximate the cost function. Simulations are conducted in frequency domain to show the effects of the proposed algorithm, which demonstrates its feasibility. Furthermore, numerical experiments are performed, which show the superiority of the proposed algorithm over the conventional one in terms of bit error rate and computational complexity.
Complex-Valued Convolutional Neural Networks for Automotive Scene Classification Based on Range-Beam-Doppler Tensors

In this work, we solve the task of automotive traffic scene classification using a deep learning approach on low-level radar data. In contrast to existing approaches using 2D camera images, the input are complex-valued 3D range-beam-doppler tensors outputted by an automotive radar. We design, train and evaluate three different networks and analyze the effects of different nuances in processing this type of data in deep neural networks. Particular attention is paid to complex-valued convolutions and their usage on complex-valued 3D tensors. The resulting networks achieve a classification accuracy above 95% on split test datasets and show the usability of automotive radar data for traffic scene classification, which can be integrated into weather robust multi-sensor systems.
Prediction Error Reduction of Neural Networks for Car-Following Using Multi-Step Training

Predicting the surrounding vehicles’ behavior is an important requirement for automated driving as it enables estimating others’ reactions to the own behavior during planning as well as the identification of critical situations. This work proposes a recursive multi-step training scheme for neural networks that predict other vehicles’ positions in a highway car-following scenario. We implement a neural network and compare the proposed approach to the commonly used singlestep training as well as parametric models. For this, the Intelligent Driver Model (IDM) and its derivatives have been calibrated using the same approach. Evaluation is performed on 10 hours of real-world car-following situations, extracted from the extensive HighD dataset. Given equal inputs, we show that a minimal neural network with two layers composed of three neurons each surpasses the prediction performance of both the parametric prediction models and the network trained with the standard single-step approach.
Deep Learning Based Vehicle Position Estimation for Human Drive Vehicle at Connected Freeway

Accurate vehicle position data is essential information for active traffic management in connected freeway. Traffic data of connected vehicles can be collected in real time while the one of the human drive vehicle have to be estimated in connected environment. A vehicle position estimation was proposed for human driving vehicle which are not adjacent to communicated vehicles, where the car-following equation was trained by a complex neural network An improved recurrent neural network(RNN) based on gated recurrent unit (GRU) was adopted in the modeling to solve long-term dependencies. Both historical and present movement data the preceding vehicle were considered in the improved RNN model. Performance of the method was evaluated by vehicle-pair data extracted from NGSIM. The results indicated that the proposed method has higher accuracy than the method based on traditional car-following models.
TestMC: Testing Model Counters using Differential and Metamorphic Testing

Model counting is the problem for finding the number of solutions to a formula over a bounded universe. This is a classic problem in computer science that has seen many recent advances in techniques and tools that tackle it. These advances have led to applications of model counting in many domains, e.g., quantitative program analysis, reliability, and security. Given the sheer complexity of the underlying problem, today's model counters employ sophisticated algorithms and heuristics, which result in complex tools that must be heavily optimized. Therefore, establishing the correctness of implementations of model counters necessitates rigorous testing. This experience paper presents an empirical study on testing industrial strength model counters by applying the principles of differential and metamorphic testing together with bounded exhaustive input generation and input minimization. We embody these principles in the TestMC framework, and apply it to test four model counters, including three state-of-the-art model counters from three different classes. Specifically, we test the exact model counters projMC and dSharp, the probabilistic exact model counter Ganak, and the probabilistic approximate model counter ApproxMC. As subjects, we use three complementary test suites of input formulas. One suite consists of larger formulas that are derived from a wide range of real-world software design problems. The second suite consists of a bounded exhaustive set of small formulas that TestMC generated. The third suite consists of formulas generated using an off-the-shelf CNF fuzzer. TestMC found bugs in three of the four subject model counters. The bugs led to crashes, segmentation faults, incorrect model counts, and resource exhaustion by the solvers. Two of the tools were corrected subsequent to the bug reports we submitted based on our study, whereas the bugs we reported in the third tool were deemed by the tool authors to not require a fix.
A Novel Coil for Wireless Power Transfer System

Wireless power transfer (WPT) technology is now widely used because it's safe, convenient and efficient. The performance of the WPT system is largely determined by the coupling between the transmitter coil and the receiver coil. This paper proposed a novel coil that concludes a main coil and two auxiliary coils and the three coils are electrically connected in series with one another. The design parameters of the proposed coil and its effects are analyzed. Results show that, with properly chosen design parameters, the proposed coil could not only reduce the electromagnetic field (EMF) formed in a location away from the coils but also prevent or minimize a reduction in power transmission efficiency.
Modulation Methods based on Phase-Shifted PWM for H- Bridge Multilevel Inverters

This paper analyzes the modulation methods based on phase-shifted PWM (PS-PWM) scheme for cascaded H-bridge multilevel inverter (CHMI). The PS-PWM scheme is one of the modulation schemes for CHMI and it generates same power loss such as switching loss and conduction loss on each cell. However, the PS-PWM scheme leads to the most switching loss than the other schemes for CHMI. Because the switching loss causes failure of power semiconductor switch, it should reduce for reliability of CHMI. In this paper, it is compared and discussed that total harmonic distortion (THD) of modulation methods based on PS-PWM scheme for reducing switching loss. To analyze characteristic each modulation method, the simulation of 3-phase 5-level CHMI is conducted.
CP-Less MIMO Discrete Fourier Transform Spread OFDM

In this work, we present the first Cyclic prefix (CP)-less Multiple-Input Multiple-Output (MIMO) OFDM System. It is based on incorporating DFT spreading, signal discrimination with Walsh-Hadamard coding, and PAPR reduction with Tukey (tapered cosine) time-domain windowing techniques. This combination of mechanisms provides an effective combination of benefits related to Bit Error Rate (BER), Peak-to-Average Power Ratio (PAPR), Complementary Cumulative Distribution Function (CCDF) of PAPR, and Out-of-Band (OOB) spectral power. Our simulation study shows that the proposed scheme achieves Out-of-Band (OOB) spectral power attenuation of 142 dB with PAPR of 8.56 dB and BER of 10−4 under a scenario of Eb /N0 = 15 dB with a 16-QAM modulation.
Opening the Network Edge for Programmability of Mission Critical Session Control

Programmability is one of the key features of fifth generation (5G) system which enables flexibility in provisioning customized services. 5G supports a wide range of mission critical use cases with challenging requirements of ultrahigh reliability and availability, and low latency. These use cases can benefit from programmability of quality of service (QoS) as the connectivity requirements can vary across different mission critical scenarios. In this paper, an application programming interface (API), that allows third party mission critical applications to initiate sessions with specified QoS, is presented. The API exposes session control capabilities at the network edge utilizing the infrastructure of Multi-access Edge Computing. The proposed API is described by typical use cases, information flows and data types. The API feasibility is demonstrated by session state models and performance evaluation.
An IRS-Aided mmWave Massive MIMO Systems Based on Genetic Algorithm

Millimeter wave (mmWave) is considered as a key technology of the next-generation wireless communications. Besides, intelligent reflecting surface (IRS) has been proposed as a new and low-cost solution to maximize the spectrum efficiency of wireless system. In this paper, we study an IRS-aided mmWave massive MIMO system, where each reflecting element at the IRS has only a finite number of discrete phase shifter. To maximize the spectrum efficiency of the IRS-aided mmWave system, a genetic algorithm (GA) based search scheme is proposed to obtain a near-optimal phase shifter of the IRS. Moreover, an alternating optimization algorithm is used for the integrated design of active hybrid beamforming and passive intelligent reflecting surface. The numerical results are provided to validate the good performance of the proposed algorithm.
Is Software Engineering Research Addressing Software Engineering Problems? (Keynote)

Brian Randell described software engineering as “the multi-person development of multi-version programs”. David Parnas expressed that this “pithy phrase implies everything that differentiates software engineering from other programming” (Parnas, 2011). How does current software engineering research compare against this definition? Is there too much focus currently on research into problems and techniques more associated with programming than software engineering? Are there opportunities to use Randell's description of software engineering to guide the community to new research directions? In this extended abstract, I motivate the keynote, which explores these questions and discusses how a consideration of the development streams used by multiple individuals to produce multiple versions of software opens up new avenues for impactful software engineering research.
The Symptom, Cause and Repair of Workaround

In software development, issue tracker systems are widely used to manage bug reports. In such a system, a bug report can be filed, diagnosed, assigned, and fixed. In the standard process, a bug can be resolved as fixed, invalid, duplicated or won't fix. Although the above resolutions are well-defined and easy to understand, a bug report can end with a less known resolution, i.e., workaround. Compared with other resolutions, the definition of workarounds is more ambiguous. Besides the problem that is reported in a bug report, the resolution of a workaround raises more questions. Some questions are important for users, especially those programmers who build their projects upon others (e.g., libraries). Although some early studies have been conducted to analyze API workarounds, many research questions on workarounds are still open. For example, which bugs are resolved as workarounds? Why is a bug report resolved as workarounds? What are the repairs of workarounds? In this experience paper, we conduct the first empirical study to explore the above research questions. In particular, we analyzed 221 real workarounds that were collected from Apache projects. Our results lead to some interesting and useful answers to all the above questions. For example, we find that most bug reports are resolved as workarounds, because their problems reside in libraries (24.43%), settings (18.55%), and clients (10.41%). Among them, many bugs are difficult to be fixed fully and perfectly. As a late breaking result, we can only briefly introduce our study, but we present a detailed plan to extend it to a full paper.ACM Reference Format: Daohan Song, Hao Zhong, and Li Jia. 2020. The Symptom, Cause and Repair of Workaround. In 35th IEEE/ACM International Conference on Automated Software Engineering (ASE ‘20), September 21–25, 2020, Virtual Event, Australia. ACM, New York, NY, USA, 3 pages. https://doi.org/10.1145/3324884.3418910
Investigation on the Use of Hidden Layers, Different Numbers of Neurons and Different Activation Functions to Detect Pupil Dilation Responses to Stress

Stress is an important problem for people that causes health problems and economic losses. When it becomes chronic, it paves the way for many diseases. Studies in this area have made significant progress in measuring stress levels with the help of data from wearable devices and sensors. In this study, using supervised deep learning methods, we worked on the detection of pupil dilation, which is accepted as one of the stress indicators. In our experiment, two different films containing positive and funny scenes and negative and stressful scenes were shown to the participants. Meanwhile, the pupil diameter was measured continuously. After the obtained signals were cleared of noises, deep learning studies were carried out on them. With these experiments, the effect of different activation functions used in hidden layers along with the different number of hidden layers and neuron numbers on learning were examined. After the trials with Hyperbolic Tangent, ReLU and Swish activation functions, the highest accuracy for classifying the stress of the participants from their pupil responses was obtained with the Swish activation function with 90.79%.
Verified from Scratch: Program Analysis for Learners' Programs

Block-based programming languages like Scratch support learners by providing high-level constructs that hide details and by preventing syntactically incorrect programs. Questions nevertheless frequently arise: Is this program satisfying the given task? Why is my program not working? To support learners and educators, automated program analysis is needed for answering such questions. While adapting existing analyses to process blocks instead of textual statements is straightforward, the domain of programs controlled by block-based languages like Scratch is very different from traditional programs: In Scratch multiple actors, represented as highly concurrent programs, interact on a graphical stage, controlled by user inputs, and while the block-based program statements look playful, they hide complex mathematical operations that determine visual aspects and movement. Analyzing such programs is further hampered by the absence of clearly defined semantics, often resulting from ad-hoc decisions made by the implementers of the programming environment. To enable program analysis, we define the semantics of Scratch using an intermediate language. Based on this intermediate language, we implement the Bastet program analysis framework for Scratch programs, using concepts from abstract interpretation and software model checking. Like Scratch, Bastet is based on Web technologies, written in TypeScript, and can be executed using NodeJS or even directly in a browser. Evaluation on 279 programs written by children suggests that Bastet offers a practical solution for analysis of Scratch programs, thus enabling applications such as automated hint generation, automated evaluation of learner progress, or automated grading.
The Classification and Propagation of Program Comments

Natural language comments are like bridges between human logic and software semantics. Developers use comments to describe the function, implementation, and property of code snippets. This kind of connections contains rich information, like the potential types of a variable and the pre-condition of a method, among other things. In this paper, we categorize comments and use natural language processing techniques to extract information from them. Based on the semantics of programming languages, different rules are built for each comment category to systematically propagate comments among code entities. Then we use the propagated comments to check the code usage and comments consistency. Our demo system finds 37 bugs in real-world projects, 30 of which have been confirmed by the developers. Except for bugs in the code, we also find 304 pieces of defected comments. The 12 of them are misleading and 292 of them are not correct. Moreover, among the 41573 pieces of comments we propagate, 87 comments are for private native methods which had neither code nor comments. We also conduct a user study where we find that propagated comments are as good as human-written comments in three dimensions of consistency, naturalness, and meaningfulness.
Tunable Multi-Band Microstrip Antenna Based on Artificial Magnetic Conductor

The use of artificial magnetic conductor (AMC) on microstrip antenna design allows the dimension of antenna to be more compact so that it is beneficial for the use in portable communication devices. Apart from its small size, the microstrip antenna can generate multiple frequency response which is more cost effective due to its suitability for several applications which have appropriate frequency specifications. In this paper, an AMC-based tunable multi-band microstrip antenna is designed by incorporating external components, namely varactor diode, into the AMC structure. The incorporation of varactor diodes with varied reverse DC bias voltages can affect the capacitance value on the antenna. Two layers of 1.6 mm thick FR4 Epoxy dielectric substrate are used for designing the antenna with the dimension of 51 mm × 51 mm × 3.2 mm. The characterization results show that the proposed microstrip antenna could produce tunable multi-band frequency response in the frequency range of 1.575 GHz to 2.595 GHz along with the reverse DC bias voltage variation. The higher the applied reverse DC bias voltage across the varactor diode, the smaller the capacitance value yielding the higher the resonant frequency.
A Key Transport Protocol for Advance Metering Infrastructure (AMI) Based on Public Key Cryptography

Advance metering infrastructure (AMI) is an important element of the Smart Grid as it is expectable to simplify the transport of meter reading and control information from meters to the utility provider. Smart meters are generally located in insecure area of house and can potentially be tampered which can lead towards exposure of consumer usage. Security is an important factor to achieve secure communication and providing protection from malicious attacks in these Advanced Metering Infrastructure (AMI) based smart grids in unfavorable situations. The management of keys especially key distribution in AMI is an open challenge. The proposed protocol consists of four phases: Initialization, Registration, Mutual authentication and Key exchange to establish a session key by the Head-End System (HES) and the Smart Meter (SM) after authenticating each other. The paper also contains discussion on the security of the proposed protocol and simulation evaluation for selected communication parameters.
LSTM for Cloud Data Centers Resource Allocation in Software-Defined Optical Networks

Nowadays, artificial intelligence provides an excellent opportunity for scientists to improve the efficiency of resource allocation in communication networks. In this paper, we focus on applying two methods: Long-Short Term Memory and Monte Carlo Tree Search, to solve the problem of cloud resource allocation in dynamic, real-time traffic scenarios. We use a framework of Software Defined Elastic Optical Networks and cloud resources available from Amazon Web Services. Results show that the application of Monte Carlo Tree Search and Long-Short Term Memory provides superior performance, which is an excellent opportunity for network operators to achieve better utilization of their networks, with lower operational costs.
Variational autoencoder as a generative tool to produce de-novo lead compounds for biological targets

A variational autoencoder (VAE) is a generational deep learning model based on encoding a particular observation into a latent space and then decoding it while incorporating some random noise with the intuition of being able to generate slightly different forms of the input observation. Here, I propose a VAE that is able to generate denovo drug compounds by feeding in a known set of drug compounds against a particular biological target. To demonstrate the ability of the proposed VAE as a generative model, known drug molecules belonging to the class of Neuraminidase (NA) inhibitors were taken from the ZINC database and fed into the VAE model as one-hot encoded SMILES strings. Similarly, active NA inhibitors and decoy molecules together were also fed to compare efficiency. The generated molecules were then screened to remove impractical structures. Next, a drug-likeness (QED) score was computed for each candidate molecule and a cutoff of 0.5 was used to extract viable candidates. To ensure that the generated drug compounds were active NA inhibitors, a series of Artificial Neural Networks (ANNs) classifiers based on three different characterization techniques, namely chemical fingerprinting, molecular descriptions and graph convolutions, were developed to identify active NA inhibitors from decoy molecules. The feature candidate data were then fed into the three designed ANNs to obtain the final set of novel, viable and active NA inhibitors. Seventy-one new NA inhibitors were obtained after three runs of the VAE model under different parameterizations. The proposed VAE can hence be used to generate de-novo drug compounds for a wide variety of biological targets.