Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Method for Pruning Deep Neural Networks

In order to implement Deep Neural Networks (DNNs) into mobile devices, network pruning has been widely explored for lightening the complexity of Deep Neural Networks in terms of computational cost and parameter-storage load. In this paper, we propose a novel filter-level pruning method which utilizes a deep learning method to pursue compact DNNs. Specifically, we use a DNN model to extract features from the filters at first. Then, we employ a clustering algorithm to force the extracted features roll into different groups. By mapping the clustering results to the filters, we get the "similarity" relationships among the filters. At last, we keep the filter which is closest to the centroid in each cluster, prune out the others, and retrain the pruned DNN model. Compared with previous methods that employ heuristic ways on filters directly or selecting shallow features from filters manually, our method takes advantages of the deep learning method which can represent the raw filters in a more precise way. Experimental results show that our method outperforms several state-of-the-art pruning methods with negligible accuracy loss.
Dynamic Representations Toward Efficient Inference on Deep Neural Networks by Decision Gates

While deep neural networks extract rich features from the input data, the current trade-off between depth and computational cost makes it difficult to adopt deep neural networks for many industrial applications, especially when computing power is limited. Here, we are inspired by the idea that, while deeper embeddings are needed to discriminate difficult samples (i.e., fine-grained discrimination), a large number of samples can be well discriminated via much shallower embeddings (i.e., coarse-grained discrimination). In this study, we introduce the simple yet effective concept of decision gates (d-gate), modules trained to decide whether a sample needs to be projected into a deeper embedding or if an early prediction can be made at the d-gate, thus enabling the computation of dynamic representations at different depths. The proposed d-gate modules can be integrated with any deep neural network and reduces the average computational cost of the deep neural networks while maintaining modeling accuracy. The proposed d-gate framework is examined via different network architectures and datasets, with experimental results showing that leveraging the proposed d-gate modules led to a ~43% speed-up and 44% FLOPs reduction on ResNet-101 and 55% speed-up and 39% FLOPs reduction on DenseNet-201 trained on the CIFAR10 dataset with only ~2% drop in accuracy. Furthermore, experiments where d-gate modules are integrated into ResNet-101 trained on the ImageNet dataset demonstrate that it is possible to reduce the computational cost of the network by 1.5 GFLOPs without any drop in the modeling accuracy.
On‐chip evolution of combinational logic circuits using an improved genetic‐simulated annealing algorithm

Summary This paper presents the on‐chip evolution system of combinational logic circuits by a new hybrid algorithm known as improved genetic‐simulated annealing algorithm (IGASA). IGASA is based on the concept and principles of genetic algorithm (GA) and simulated annealing (SA). The main idea is to combine GA's global search ability with the advantage of SA's fast convergence to reach an optimal solution. In this paper, a new mutation operation and a repeated annealing (RA) process are introduced to overcome the premature convergence of the standard GA and the large number of searches in the later period. An elitist strategy to enhance convergence characteristic of the proposed algorithm is incorporated to better speed up the evolution process. In addition, the new algorithm is implemented in a new evolvable platform using Intel Cyclone V FPGA with an embedded ARM microprocessor. Comparison of the performance of the improved algorithm to the pure GASA algorithm and standard GA is presented on a number of case studies. The experiment results demonstrate that feasible circuits are always achieved by the IGASA algorithm unlike with other algorithms and the number of generations required is less.
Deploying Natural Language Intents with Lumi

short-paper Share on Deploying Natural Language Intents with Lumi Authors: Arthur S. Jacobs UFRGS UFRGSView Profile , Ricardo J. Pfitscher UFRGS UFRGSView Profile , Rafael H. Ribeiro UFRGS UFRGSView Profile , Ronaldo A. Ferreira UFMS UFMSView Profile , Lisandro Z. Granville UFRGS UFRGSView Profile , Sanjay G. Rao Purdue University Purdue UniversityView Profile Authors Info & Claims SIGCOMM Posters and Demos '19: Proceedings of the ACM SIGCOMM 2019 Conference Posters and DemosAugust 2019 Pages 82–84https://doi.org/10.1145/3342280.3342315Published:19 August 2019Publication History 4citation375DownloadsMetricsTotal Citations4Total Downloads375Last 12 Months56Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Non-Parametric Informed Exploration for Sampling-Based Motion Planning

Efficient exploration of the search space is crucial for faster convergence in sampling-based motion planning. An effective sampling method must first concentrate on quickly finding a good initial solution and then focus the search on regions that can potentially improve the current best solution. In this paper, we propose a non-parametric exploration technique that addresses these challenges. The proposed algorithm prioritizes search by utilizing heuristics. After an initial solution is found, the method generates samples in the “$L_{2} -$informed set”, while leveraging collision data to reduce the number of samples in the obstacle space. We demonstrate the efficiency of the proposed approach with several benchmarking experiments.
Lightweight Machine Learning for Efficient Frequency-Offset-Aware Demodulation

Carrier frequency offset (CFO) arises from the intrinsic mismatch between the oscillators of a wireless transmitter and the corresponding receiver, as well as their relative motion (i.e., Doppler effect). Despite advances in CFO estimation and tracking techniques, estimation errors are still present. Residual CFO creates a time-varying phase error, which degrades the decoder's performance by increasing the symbol error rate. The impact is particularly visible in dense constellation maps (e.g., high-order QAM modulation), often used in modern wireless systems such as 5G NR, 802.11ax, and mmWave, as well as in physical security techniques, such as modulation obfuscation (MO). In this paper, we first derive the probability distribution function for the residual CFO under Gaussian noise. Using this distribution, we compute the maximum-likelihood demodulation boundaries for OFDM signals in a non-closed form. For modulation schemes with unequal-amplitude reference constellation points (e.g., 16-QAM and higher, APSK, etc.), the “optimal” boundaries have irregular shapes, and more importantly, they depend on the time since the last CFO correction instance, e.g., reception of frame preamble. To approximate the optimal boundaries and provide a practical (real-time) demodulation scheme, we explore machine learning techniques, specifically, support vector machine (SVM). Our SVM approach exhibits better accuracy and lower complexity in the test phase than other state-of-the-art machine-learning approaches. As a case study, we apply our CFO-aware demodulation to enhance the performance of a MO technique. Our analytical results show a gain of up to 3dB over conventional demodulation schemes, which exceeds 3dB in complete system simulations. Finally, we implement our scheme on USRPs and experimentally corroborate our analytic and simulation-based findings.
Cooperative Model of Evolutionary Algorithms Applied to CEC 2019 Single Objective Numerical Optimization

A cooperative model of well-known evolutionary algorithms is proposed and tested on CEC 2019 benchmark suite. The four adaptive algorithms are chosen for this model, namely Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) and three variants of adaptive Differential Evolution. Although the three algorithms use constant population size, the proposed model employs an efficient linear population-size reduction mechanism. The provided results show that the Cooperative Model of Evolutionary Algorithms (CMEAL) is able to solve seven out of ten optimization problems.
Measuring Interestingness of Theorems in Automated Theorem Finding by Forward Reasoning Based on Strong Relevant Logic

The problem of automated theorem finding is one of 33 basic research problems in automated reasoning which was originally proposed by Wos in 1988, and it is still an open problem. The problem implicitly requires some metrics to be used for measuring interestingness of found theorems. A set of metrics for measuring interestingness of theorems in automated theorem finding by forward reasoning based on strong relevant logic have been proposed, and a case study to measure interestingness of the theorems of Tarski's geometry was performed. The result of the case study showed that the proposed metrics were hopeful for measuring interestingness of theorems in automated theorem finding. However, there is no research about the influence of different logical fragments of strong relevant logic on theorem finding. This paper presents a case study to measure interestingness of theorems obtained by using forward reasoning based on different logical fragments. The result shows that different logical fragments have different effects on the four factors of interestingness of theorems.
Analysis of Lightweight Message Authentication Codes for IoT Environments

In recent years, Internet of Things (IoT) have spread everywhere from smart home to wireless sensor networks. The physical devices connected to the internet in IoT networks are resource constrained, so implementing security using conventional cryptographic algorithms are not suitable for these resource constrained devices. Recently, National Institute of Standards and Technology (NIST) has published the techniques of lightweight cryptography, which provides a way to implement security in a limited resource environment (IoT environment). Lightweight Message Authentication Code (MAC) is one of the technique in lightweight cryptography. This paper provides an overview of performance analysis of the lightweight cryptography algorithms, which are hash-based Lightweight MAC - LightMAC, CHASKEY on NUCLEO-F401RE IoT board, which has STM32F401RE microcontroller. Performance of these algorithms is analyzed based on the power consumption, memory consumption, execution time and throughput with various supported key sizes and message lengths.
Probabilistic Task Assignment for Specialized Multi-Agent Robotic Systems

This paper introduces a probabilistic approach for assigning specialized individual agents among a robotic swarm to corresponding constrained tasks. Based on the assumption that each individual agent possesses specialized capabilities, the proposed approach evaluates probabilistic fitting of the available robot individuals based on the requirements imposed by the current task, which takes the form of a recognized target object in a specific environment. A formal matching scheme is developed to evaluate a task-agent fitting score among all available agents. It assigns the most qualified and available specialized robotic agent as the best responder to perform the recognized task. A simulation study is presented to validate the efficiency and robustness of the proposed approach.
Virtual Reality Instruction Followed by Enactment Can Increase Procedural Knowledge in a Science Lesson

A 2×2 between-subjects experiment (a) investigated and compared the instructional effectiveness of immersive virtual reality (VR) versus video as media for teaching scientific procedural knowledge, and (b) examined the efficacy of enactment as a generative learning strategy in combination with the respective instructional media. A total of 117 high school students (74 females) were randomly distributed across four instructional groups - VR and enactment, video and enactment, only VR, and only video. Outcome measures included declarative knowledge, procedural knowledge, knowledge transfer, and subjective ratings of perceived enjoyment. Results indicated that there were no main effects or interactions for the outcomes of declarative knowledge or transfer. However, there was a significant interaction between media and method for the outcome of procedural knowledge with the VR and enactment group having the highest performance. Furthermore, media also seemed to have a significant effect on student perceived enjoyment, indicating that the groups enjoyed the VR simulation significantly more than the video. The results deepen our understanding of how we learn with immersive technology, as well as suggest important implications for implementing VR in schools.
Lifelong Learning for Heterogeneous Multi-Modal Tasks

In this work, we investigate the lifelong learning problem from the viewpoint of heterogeneous multi-modal fusion. The main challenges come from the fact that the common representation between heterogeneous modalities should be persistently learned and the learned classifier for each multi-modal task should be persistently updated. To address this problem, we construct a multi-modal lifelong learning framework which deals with the consecutive multi-modal learning tasks and develop an efficient online dictionary learning algorithm to solve the multi-modal lifelong learning problem. Finally, we perform experimental validation on a complicated material recognition task and show the promising results.
Residual Capsule Network

Convolution Neural Network (CNN) has been the most influential innovations in the filed of Computer Vision. CNN have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks - CNN need a large dataset, hyperparameter tuning is nontrivial and importantly, they lose all the internal information about pose and transformation to pooling. Capsule Networks have addressed the limitations of CNNs and have shown a great improvement by calculating the pose and transformation of the image. On the other hand, deeper networks are more powerful than shallow networks but at the same time, more difficult to train. Simply adding layers to make the network deep has led to vanishing gradient problem. Residual Networks introduce skip connections to ease the training and have shown evidence that they can give good accuracy with considerable depth. Putting the best of Capsule Network and Residual Network together, we present Residual Capsule Network, a framework that uses the best features of both Residual and Capsule Networks. In the proposed model, the conventional Convolutional layer in Capsule Network is replaced by skip connections like the Residual Networks to decrease the complexity of the Baseline Capsule Network and seven ensemble Capsule Network. We trained our model on MNIST and CIFAR-10 datasets and have noted a significant decrease in the number of parameters when compared to the Baseline models.
Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques

Rooftop photovoltaics (PV) systems are attracting residential customers due to their renewable energy contribution to houses and to green cities. However, customers also need a comprehensive understanding of system design configuration and the related energy return from the system in order to support their PV investment. In this study, the rooftop PV systems from many high-volume installed PV systems countries and regions were collected to evaluate the lifetime energy yield of these systems based on machine learning techniques. Then, we obtained an association between the lifetime energy yield and technical configuration details of PV such as rated solar panel power, number of panels, rated inverter power, and number of inverters. Our findings reveal that the variability of PV lifetime energy is partly explained by the difference in PV system configuration. Indeed, our machine learning model can explain approximately 31 % ( 95 % confidence interval: 29–38%) of the variant energy efficiency of the PV system, given the configuration and components of the PV system. Our study has contributed useful knowledge to support the planning and design of a rooftop PV system such as PV financial modeling and PV investment decision.
Cycle-Consistent Diverse Image Synthesis from Natural Language

Text-to-image translation has become an attractive yet challenging task in computer vision. Previous approaches tend to generate similar, or even monotonous, images for distinctive texts and overlook the characteristics of specific sentences. In this paper, we aim to generate images from the given texts by preserving diverse appearances and modes of the objects or instances contained. To achieve that, a novel learning model named SuperGAN is proposed, which consists of two major components: an image synthesis network and a captioning model in a Cycle-GAN framework. SuperGAN adopts the cycle-consistent adversarial training strategy to learn an image generator where the feature distribution of the generated images complies with the distribution of the generic images. Meanwhile, a cycle-consistency loss is applied to constrain that the caption of the generated images is closed to the original texts. Extensive experiments on the benchmark dataset Oxford-flowers-102 demonstrate the validity and effectiveness of our proposed method. In addition, a new evaluation metric is proposed to measure the diversity of synthetic results.
A Proposed Framework for Ranking Critical Information Assets in Information Security Risk Assessment Using the OCTAVE Allegro Method with Decision Support System Methods

The security of an organization lies not only in physical buildings, but also in its information assets. Safeguarding information assets requires further study to establish optimal security mitigation steps. In determining the appropriate mitigation of information assets, both an information security risk assessment and a clear and measurable rating are required. Most risk management methods do not provide the right focus on ranking the critical information assets of an organization. This paper proposes a framework approach for ranking critical information assets. The proposed framework uses the OCTAVE Allegro method, which focuses on profiling information assets by combining ranking priority measurements using decision support system methods, such as Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP). The combined OCTAVE Allegro-SAW and OCTAVE Allegro-AHP methods are expected to better address risk priority as an input to making mitigation decisions for critical information assets. These combinations will help management to avoid missteps in adjusting budget needs allocation or time duration by selecting asset information mitigation using the ranking results of the framework.
Incremental Prediction Model of Disk Failures Based on the Density Metric of Edge Samples

Disks are the main equipment for data storage in data centers. The prediction of disk failure is of great significance for the reliability and security of data. On account of the few abnormal samples in the disk datasets, it is difficult to satisfy the requirement of supervised and semi-supervised algorithms for the number of abnormal data while the unsupervised algorithms have poor performance on recall rate when solving the problems of local anomalies and wrapped a nomalies. This paper presents an incremental learning disk failure prediction model using the density metric of edge samples. An isolation region is built by searching the nearest neighbor of each sample. We calculate the nearest training point of the test point which is not a global anomaly and the nearest training point of the obtained nearest training point by Euclidean distance. The global metric of abnormal degree of the test sample comes from the ratio of the radius of the region where the two nearest training points are located. Then, the local metric of abnormal degree of the test sample comes from the ratio between the nearest distance from the test point to the edge of the training point region and the radius of the region. Abnormal scores of test points can be obtained by combining two measurements. We identify the SMART attributes that are significantly related to disk failures and promote their weights in the next time the attributes are inputted. The experiments are carried on the synthetic and public datasets which contain local anomalies and wrapped anomalies. The proposed method outperforms the typical unsupervised algorithms such as iNNE, iForest and LOF, and the achieved recall rates increase at most 7%. Furthermore, the contrast tests on the public disk datasets also verify the proposed method has better performance on recall rate.
Vision-Based Dynamic Control of Car-Like Mobile Robots

Most existing controllers for Car-Like Mobile Robots (CLMR) are designed to handle dynamic effects by decoupling speed and steering controls, also assume that full states are accessible, which are unrealistic for real-world applications. This paper presents a combined speed and steering control system for CLMR. To provide the essential state for the controller, a newly developed visual algorithm is adopted for estimating the high-update rate longitudinal and lateral velocities of the robot which cannot be accurately measured by wheel encoders due to the skidding and slipping effects. The stability of the proposed system can be guaranteed by Lyapunov method since the velocity estimation error, the speed tracking error and the lateral deviation converging to zero simultaneously. Real-world experiments are conducted on an electric autonomous tractor with online estimation to demonstrate the feasibility of the approach.
Detecting SQL Injection Attacks Using Grammar Pattern Recognition and Access Behavior Mining

SQL injection attacks are a kind of the greatest security risks on Web applications. Much research has been done to detect SQL injection attacks by rule matching and syntax tree. However, due to the complexity and variety of SQL injection vulnerabilities, these approaches fail to detect unknown and variable SQL injection attacks. In this paper, we propose a model, ATTAR, to detect SQL injection attacks using grammar pattern recognition and access behavior mining. The most important idea of our model is to extract and analyze features of SQL injection attacks in Web access logs. To achieve this goal, we first extract and customize Web access log fields from Web applications. Then we design a grammar pattern recognizer and an access behavior miner to obtain the grammatical and behavioral features of SQL injection attacks, respectively. Finally, based on two feature sets, machine learning algorithms, e.g., Naive Bayesian, SVM, ID3, Random Forest, and K-means, are used to train and detect our model. We evaluated our model on these two feature sets, and the results show that the proposed model can effectively detect SQL injection attacks with lower false negative rate and false positive rate. In addition, comparing the accuracy of our model based on different algorithms, ID3 and Random Forest have a better ability to detect various kinds of SQL injection attacks.