Reviewerly/exHarmony huggingface dataset:
Security in Embedded Systems

In past, most of the embedded systems are designed for dedicated functionality. They are stand-alone. However, with the advent of technological advances, most of these systems are not stand-alone but they are distributed. This caused everyone to think how to secure the embedded systems from hacking, intrusion, illegal data access, sabotage, and so on. These issues are well studied in Internet security. But, these techniques do not apply to different network protocols for embedded systems. No focus was given to the security of embedded hardware, firmware, embedded operating systems, and embedded applications, and embedded data. This chapter briefly introduces the security principles, the security issues in embedded systems, and the methodology to solve them. Section 14.2 introduces basic terminology, possible cyber-attacks on embedded systems, and needed security policies. Section 14.3 gets into details of security vulnerabilities in embedded systems and how to prevent them. Section 14.4 details basic security algorithms. Section 14.5 gives an example on how to implement security protocols on existing real-time network standards. The example is on authentication protocol implemented on CAN standard. Section 14.6 explains possible guidelines to secure embedded systems. The chapter concludes with current security standards for embedded systems and typical secured platform architecture.
Building a Virtual Simulation Teaching and Learning Platform Towards Creative Thinking for Beijing Shahe Education Park

In response to China’s innovation-driven development strategy. The course development team is developing a course on innovation and entrepreneurship education. The course guides students to break through their professional limitations and develop creative thinking from multiple perspectives and at a deeper level. The team breaks away from the previous “teaching and learning” pattern and combines virtual simulation technology to develop an online teaching platform. The team develops virtual simulation modules and designs immersive interactive teaching classes and scenes based on the existing creative thinking courses. Explore the application of virtual simulation technology in online teaching, building a new innovative co-construction pattern, co-management, and sharing of universities, scientific research institutes, and enterprises. Create a virtual simulation service support system with independent intellectual property rights, high market utilization, a wide range of applications, and realize the open sharing of educational resources.
Data Augmentation for Signal Modulation Classification using Generative Adverse Network

Deep learning has been widely investigated for radio applications. The classification performance of the deep learning greatly depends on the quality of dataset. However, the deficiency of the training data is a critical issue limiting the classification accuracy in practical scenarios. In this paper, we proposed to use the generative adversarial network (GAN) as a data augmentation tool to solve the problem of inadequate training issue under the lack of sufficient data samples. The data augmentation process could be realized by Nash equilibrium of generator and discriminator. The result shows that the accuracy of the classifier is increased by nearly 4 percentage in the signal to noise ratio range of 0 to 20 dB after data augmentation.
LipARELU: ARELU Networks aided by Lipschitz Acceleration

We present LipARELU, a novel framework for training L-hidden layer neural networks, equipped to handle large, adaptive learning rates. The framework is based on smoothness assumptions on the proposed activation function, AREL U. The generalization and approximation abilities of ARELU are discussed in detail. The framework assumes weaker conditions on the loss functions used to train the network. Using the fact that the inverse of the Lipschitz constant of the loss function is an ideal learning rate, we compute Lipschitz Adaptive Learning Rates (LALR) for tractable functions such as the Quadratic Loss (QL) and robust functions such as the Mean Absolute Error (MAE). Theoretical and experimental validation testify strength of our approach on several datasets in regression and classification tasks. The performance of our method is comparable to the current state-of-the-art (SOTA) methods.
A Self-enhanced Automatic Traceability Link Recovery via Structure Knowledge Mining for Small-scale Labeled Data

Traceability links between requirements and source code are beneficial to the maintenance and evolution activities. Compared with the proposed unsupervised solutions, supervised solutions are more effective to generate trace links automatically and gaining more attention. However, supervised solutions often need to spend a lot of effort on labeling data. To overcome this limitation, we propose a self-enhanced automatic traceability link recovery approach based on structure knowledge mining for small-scale labeled data, named K2Trace, which not only enhances the semantic representations of artifacts by mining context information but also self-enhances the size of training set by exploring transitive relationships. Evaluation results show that K2Trace can outperform the state-of-the-art baseline approach. K2Trace proves the usefulness of mining knowledge from the structure information of software artifacts, as well as provides a new way to substantially reduce the amount of training data needed for training efficient classification models, which may pave the way for generating accurate trace links.
Bayesian Bidirectional Backpropagation Learning

We show that training neural classifiers with Bayesian bidirectional backpropagation improves the performance of the network. Bidirectional backpropagation trains a deep network for both forward and backward recall through the same layers of neurons and with the same weights. It maximizes the network's joint forward and backward likelihood. Bayesian bidirectional backpropagation combines prior probabilities at the input and output layers with the likelihood structure of the layers. It maximizes the posterior probability of the network. It differs from other forms of neural Bayesian estimation because it uses the bidirectional likelihood of the network instead of the unidirectional likelihood. Bayesian bidirectional backpropagation outperformed classifiers trained with both unidirectional and bidirectional backpropagation. The networks trained on the CIFAR-10 and CIFAR-100 image test sets. A Laplacian or Lasso-like prior outperformed both Gaussian and uniform priors.
A Strategic Search Algorithm in Multi-agent and Multiple Target Environment

The aim of this study is to investigate how to solve the path-planning problem of multiple competing players towards moving targets within a dynamically changing environment. A novel approach is needed to exceed the classical solutions for single or multiple agent search algorithms. An assignment strategy is introduced for pursuing agents. They can compute the distance towards the targets, while all players are at the current state. The reason to use the assignment strategy is to find a better solution, especially in the round circle cases, where pursuing agents trap and outmanoeuvre the targets. This study set out to explore how the agents can outsmart targets by exploiting repeated A* searches. To find a solution to such a dynamic problem, a heuristic search algorithm Strategy Multiple Target A* (STMTA*) will be developed and implemented. These are promising AI approaches. Pursuing agents can freely communicate and share information. A multi-agent framework will be used. The experimental results suggested that STMTA* is quicker in number of steps and successful in respect of catching targets on each test run approximately by 27%.
A Dilated Convolution-based Denoising Network for Magnetic Resonance Images

Magnetic Resonance Imaging (MRI) are typically corrupted with random noise. This type of noise exhibits the characteristics of Rician distribution in MRI scans. Noise in MRI scans degrades the accuracy of manual and computerized inspection of diseases. Therefore, denoising of MRI images is an indispensable process before the clinical examination of any disease. In this article, we present a novel denoising neural network for MRI images. The proposed network contains a set of dilated convolutions for Rician noise removal. We have used hybrid dilated convolutions to overcome the gridding problem in the network. The residual learning scheme has also been utilized using a set of skip connections. A substantial amount of supervised MRI data has been developed for end-to-end training of the proposed network. Extensive experiments have been performed on synthetic and real MRI datasets to study the effectiveness of the proposed method. The experimental observations indicate that our method not only achieves promising performance but also retains prominent image information effectively.
Cloud Accountability Method: Towards Accountable Cloud Service-Level Agreements

The paper expands on our position paper Adjepon Yamoah and Wen (Assuring Dependable Cloud-Based System Engineering: A Cloud Accountability Method. Vasteras, Sweden, pp. 181, 2016), [], to make two contributions: demonstrate cloud accountability by applying a novel method for assuring cloud dependability to a Reactive Architecture, Adjepon-Yamoah et al. (A Reactive Architecture for Cloud-based System Engineering. ACM, New York, USA, pp. 77, 2015), [], and conduct an evidence-based stochastic (Markov chains) analysis of the Reactive Architecture with a Cloud Accountability System (CAS). This CAS facilitates the novel Cloud Accountability Method (CAM), which provides dependability assurance evidence for the Reactive Architecture. This is achieved using a virtual machine introspection (VMI) technique that counter-checks the dependability metrics (related to availability and reliability) provided by the cloud service providers (CSPs) as digital evidence. This work provides a means of addressing the perceived lack of trust for cloud-based services towards cloud accountability. Method: We evaluate CAM by implementing a prototype of the cloud accountability system (CAS) to facilitate the active monitoring of the Reactive Architecture. Our evaluation is performed using a cloud-based test-bed in the Amazon Web Service (AWS) Elastic Cloud Compute (EC2) environment comprising 5 Linux Virtual Machines (VMs) over a period of 3 h. The 5 instances are classified into two groups: the target VMs of the RA components and the monitoring VM of the CAS. The CAM facilitates the collection, examination, analysis, and reporting of dependability metrics as digital evidence to assure dependability of the AWS EC2 environment. Result: Introspecting our 5 VMs for 3 h, we identified some relevant but unreported dependability incidents on AWS, which cost the user about $65. We then conclude that the CAM is sufficiently capable of meaningfully assuring the dependability of deployed systems in the cloud.
Perfect state transfer in Grover walks between states associated to vertices of a graph

We study perfect state transfer in Grover walks, which are typical discrete-time quantum walk models. In particular, we focus on states associated to vertices of a graph. We call such states vertex type states. Perfect state transfer between vertex type states can be studied via Chebyshev polynomials. We derive a necessary condition on eigenvalues of a graph for perfect state transfer between vertex type states to occur. In addition, we perfectly determine the complete multipartite graphs whose partite sets are the same size on which perfect state transfer occurs between vertex type states, together with the time.
Augmented Reality for Training and Maintenance of Reclosers: A Case Study of a Wearable Application

This paper presents a case study of Copelia, an augmented reality application targeted for smart glasses. Copelia was developed to optimize the workflow of COPEL (Companhia Paranaense de Energia), a major Brazilian electricity company. The application assists its users in the maintenance of reclosers with features such as object recognition, augmented reality (AR), and remote calls. These features allow generalist electricians to perform tasks that would otherwise be exclusively executed by skilled professionals. Copelia was evaluated in laboratory and field tests. Results show that users resisted the application due to its novel interaction paradigm. Most of the reported issues were related to the usability of smart glasses or the graphics interface. A list of good practices was derived from the discussion of these issues. The features themselves proved to be useful, especially the guided instructions. Despite an initial negative reaction, Copelia gained appraisal by its users over time, who highlighted its usefulness for training and maintenance.
Research on Speech Emotion Recognition Technology Based on Machine Learning

In recent years, with the continuous improvement of computer computing power and the emergence of big data processing, artificial intelligence has become an important research field for researchers. The current research on emotion and consciousness is an important part of artificial intelligence research, and the research on speech emotion recognition technology is receiving more and more attention from researchers. Speech emotion recognition has a very broad application prospects. For example, it can be used for student education to identify students’ emotions in time and make appropriate processing,for intelligent human-computer interaction to discover the speaker’s emotional changes in time. It can also be used for criminal investigation to detect the psychological state of the suspect and assist in polygraph detection.This paper mainly studies the MFCC parameters and KNN algorithm of speech signals, and realizes MFCC feature extraction based on MATLAB, and realizes emotion classification through KNN algorithm. The CASIA corpus was used for training and testing, and finally reached an accuracy of 78.00%.
Research on Motion-Less VR: Development and Basic Evaluation of a System That Enables 2-DoF Upper Limb Motion

The authors propose a VR system called “Motion-Less VR”, which does not require motion of the real body. In this paper, we developed a system that enables 2-DoF motion of the shoulder and elbow, and basic experiments were conducted.
Personalized and Dynamic top-k Recommendation System using Context Aware Deep Reinforcement Learning

With the ever-increasing number of choices in just about every domain, ranging from e-commerce platforms to music and video streaming apps, it requires a highly personalized and best-in-class experience to make the users stay. Relevant, dynamic and smart item recommendations play a significant role in providing quality user experience. The majority of existing work either relies on explicit user feedback or gives static recommendations that poorly adapt to the changing user preferences. This paper proposes novel model architectures, namely, Sequential Feature Bandits(SFB) and Cross Feature Bandits(CFB) for providing online top-k context-aware recommendations. It leverages the item features, and uses implicit user feedback with a reinforcement learning backed dynamic algorithm. We use deep-q learning to maximize the cumulative estimated reward(CMR) and click-through rate(CTR) along with an experience replay strategy to prevent the proposed models from getting destabilized. Detailed experiments were conducted on a commercially available news article data set with implicit user-feedback and user-item context features. A thorough experimentation shows an average increase of 23.4% in CTR values and proves the effectiveness of the proposed SFB and CFB architectures for real-time recommendation.
User Mobility Simulator for Full-Immersive Multiuser Virtual Reality with Redirected Walking

Full-immersive multiuser Virtual Reality (VR) setups envision supporting seamless mobility of the VR users in the virtual worlds, while simultaneously constraining them inside shared physical spaces through redirected walking. For enabling high data rate and low latency delivery of video content in such setups, the supporting wireless networks will have to utilize highly directional communication links, where these links will ideally have to “track” the mobile VR users for maintaining the Line-of-Sight (LoS) connectivity. The design decisions about the mobility patterns of the VR users in the virtual worlds will thus have a substantial effect on the mobility of these users in the physical environments, and therefore also on performance of the underlying networks. Hence, there is a need for a tool that can provide a mapping between design decisions about the users' mobility in the virtual words, and their effects on the mobility in constrained physical setups. To address this issue, we have developed and in this paper present a simulator for enabling this functionality. Given a set of VR users with their virtual movement trajectories, the outline of the physical deployment environment, and a redirected walking algorithm for avoiding physical collisions, the simulator is able to derive the physical movements of the users. Based on the derived physical movements, the simulator can capture a set of performance metrics characterizing the number of perceivable resets and the distances between such resets for each user. The simulator is also able to indicate the predictability of the physical movement trajectories, which can serve as an indication of the complexity of supporting a given virtual movement pattern by the underlying networks.
TeLCoS: OnDevice Text Localization with Clustering of Script

Recent research in the field of text localization in a resource constrained environment has made extensive use of deep neural networks. Scene text localization and recognition on low-memory mobile devices have a wide range of applications including content extraction, image categorization and keyword based image search. For text recognition of multi-lingual localized text, the OCR systems require prior knowledge of the script of each text instance. This leads to word script identification being an essential step for text recognition. Most existing methods treat text localization, script identification and text recognition as three separate tasks. This makes script identification an overhead in the recognition pipeline. To reduce this overhead, we propose TeLCoS: OnDevice Text Localization with Clustering of Script, a multi-task dual branch lightweight CNN network that performs real-time on device Text Localization and High-level Script Clustering simultaneously. The network drastically reduces the number of calls to a separate script identification module, by grouping and identifying some majorly used scripts through a single feed-forward pass over the localization network. We also introduce a novel structural similarity based channel pruning mechanism to build an efficient network with only 1.15M parameters. Experiments on benchmark datasets suggest that our method achieves state-of-the-art performance, with execution latency of 60 ms for the entire pipeline on the Exynos 990 chipset device.
Investigation of the Method of Interpolation of Movement to Video Frames

The methods of processing images and frames are moving by leaps and bounds; the development of game projects is a very dynamically developing industry. Processing video frames requires serious processing power, which affects the provision of acceptable performance. This article is a study aimed at studying the impact of using the motion interpolation method concerning video frames. It is possible to increase performance with such an application, for example, the frame rate (fps) in computer games. In the study, the algorithms for calculating the optical flow are used to select objects, determine the pixel shifts: Farneback, Bros, and TVL 1; technologies for accelerating calculations: OpenCL and CUDA.
On the (im)possibility of improving the round diffusion of generalized Feistel structures

Generalized Feistel structures (GFS) are widely employed as the underlying structure of primitives like block ciphers and hash functions. In order to improve its slow diffusion, several design ideas have been proposed. In this contribution, we explore the (im)possibility of further improving the round diffusion of GFS by modifying its round permutations. First, we generalize a technique called sub-block dividing, which further divides each sub-block into smaller blocks. We prove that the diffusion round of a round permutation with sub-block dividing is four, regardless of the number of sub-blocks. Moreover, we show that the round diffusion of GFS can be improved by alternately using two different round permutations instead of a single permutation. We present the first results that, by using two round permutations, 10- and 12-block GFS partially and fully reach the lower bounds on the diffusion round, respectively.
Simplified square to hexagonal lattice conversion based on 1-D multirate processing

Hexagonal image sampling and processing are theoretically superior to the most commonly used square lattice based sampling and processing, but due to the lack of commercial image sensors, current research mainly relies on virtually hexagonally sampled data through square to hexagonal lattice conversion, which is a typical 2-D interpolation problem. This paper presents a simplified and efficient square to hexagonal lattice conversion method. The method firstly utilizes the separable nature of the interpolation kernel to simplify the original 2-D interpolation into 1-D interpolation along the horizontal direction only, and then it applies the 1-D multirate technique to further simplify the shift-variant 1-D interpolation into shift-invariant 1-D convolutions. Compared with the original 2-D interpolation version, the proposed method becomes both simple and computationally efficient, and it is also suitable for implementation with parallel processing and hardware. Finally, experiments are performed and the results are consistent with the analysis.
CTC-Based End-To-End ASR for the Low Resource Sanskrit Language with Spectrogram Augmentation

Sanskrit is one of the Indian languages which fares poorly, with regard to the development of language-based tools. In this work, we build a connectionist temporal classification (CTC) based end-to-end large vocabulary continuous speech recognition system for Sanskrit. To our knowledge, this is the first time an end-to-end framework is being used for automatic speech recognition in Sanskrit. A Sanskrit speech corpus with around 5.5 hours of speech data is used for training a neural network with a CTC objective. 80-dimensional mel-spectrogram together with their delta and delta-delta is used as the input features. Spectrogram augmentation techniques are used to effectively increase the amount of training data. The trained CTC acoustic model is assessed in terms of character error rate (CER) on greedy decoding. Weighted finite-state transducer (WFST) decoding is used to obtain the word level transcriptions from the character level probability distributions obtained at the output of the CTC network. The decoder WFST, which maps the CTC output characters to the words in the lexicon, is constructed by composing 3 individual finite-state transducers (FST), namely token, lexicon and grammar. Trigram models trained from a text corpus of 262338 sentences are used for language modeling in grammar FST. The system achieves a word error rate (WER) of 7.64% and a sentence error rate (SER) of 32.44% on the Sanskrit test set of 558 utterances with spectrogram augmentation and WFST decoding. Spectrogram augmentation provides an absolute improvement of 13.86% in WER.