Reviewerly/exHarmony huggingface dataset:
Learning Bilevel Sparse Regularized Neural Network

Sparse regularization has attracted considerable attention in machine learning community these years, which is a quite powerful and widely used strategy for high dimensional learning problems. However, when applied in deep neural networks (DNNs), sparse regularizers have a lot of redundant weights and unnecessary connections, and little work has been devoted to regularizer-based method for DNNs sparsification. Therefore, we aim to develop a proper sparse regularizer that can avoid augmenting excessive computation complexity in DNNs. In this paper, we find that the sparse regularizer learning corresponds to learning a activation function. Further, the regularizer is learned by the bilevel optimization method for smaller number of function evaluations. Moreover, we design a novel learning method, named bilevel sparse regularized neural network (BSRL) to learn the regularization parameters based on the prior knowledge of the system. Experimental results on standard benchmark datasets show that the proposed BSRL framework outperforms other models with state-of-the-art sparse regularizers.
Network Pruning Based On Architecture Search and Intermediate Representation

Network pruning is widely used for compressing large neural networks to save computational resources. In traditional pruning methods, predefined hyperparameters are often required to determine the network structure of the target small network. However, too many hyperparameters are often undesirable. Therefore, we use the transformable architecture search (TAS) method to dynamically search the network structure of each layer when pruning the network width. In the TAS method, the channels number of the pruned network in each layer is represented by a learnable probability distribution. By minimizing computation cost, the probability distribution can be calculated and used to get the width configuration of the target pruned network. Then, the depth of the network was compressed based on the model get in the previous step. The method for compressing depth is block-wise intermediate representation training. This method is based on the hint training, where the network depth is compressed by comparing the intermediate representation of each layer of two equally wide teacher and student models. In the experiments, about 0.4% improvement over the existing method was viewed for the ResNet network on both CIFAR10 and CIFAR100 datasets.
Automated Machine Learning

The last decade has witnessed a surge of machine learning (e.g., deep learning) research and applications in many real-world scenarios, such as computer vision, language processing and data mining. Most machine learning methods have a plethora of design choices that need to be made beforehand, and their performance is shown to be very sensitive to these choices. Furthermore, the desirable choices of algorithm design often vary over different tasks and hence the algorithm configuration requires intensive expertise, which becomes a substantial hurdle for new users and further restricts the applicability and feasibility of modern machine learning methods in a wider range of public fields.
Decision Support System of Student Learning Outcomes Assessment on Digital Electronic Subject using Fuzzy Logic

Outcome Based Education (OBE) has been implemented by several universities in the world. This OBE implementation includes curriculum restructuring, evaluation and reporting of assessment results. This makes the assessment method important, and some lecturers are looking for alternative methods to measure the achievement of the Learning Outcome Program (PLO). This study proposes a Decision Support System in measuring PLO achievement in Digital Electronic courses using fuzzy logic. Fuzzy applied using Mamdani with membership function triangle parameter with fuzzification process using centroid and using gaussian membership function parameter with fuzzification process using bisector. These results are then compared with the calculation methods that have been used so far. These three methods obtained results that are not the same. But with this experiment it can&#x0027;t be judged which one is more appropriate. The choice of the right method still depends on the choice of the lecture that teaches the course. Assessment using fuzzy logic proves that this system can be a decision support system for lecturers in measuring the achievement of the Learning Outcome Program (PLO).
Extended dynamic system modulation for real-time obstacle avoidance

In this paper, a novel real-time obstacle avoidance method based on Dynamic System (DS), is proposed. The proposed method ensures the impenetrability of multiple convex obstacles by online modulating the original velocity field of the DS. It can be applied to perform obstacle avoidance in the state space of the DS with both autonomous and non-autonomous DS-based controllers. While realizing the obstacle avoidance, the equilibrium points of the original DS can be saved. The modulation matrix form is extended based on the earlier dynamic system modulation methods of the literature. The asymmetric modulation provided by this method allows the modulated DS to satisfy the dynamic constraints of a class of DSs. In addition, the proposed method has the inherent ability of multiple-obstacle avoidance and the direction selectivity of avoidance maneuver. Moreover, to solve the simultaneous guidance and obstacle avoidance problem, a guidance law for Unmanned Aerial Vehicles (UAVs) based on the proposed method, is designed. Finally, a numerical simulation is performed to analyze the performance of the proposed method and the obstacle avoidance guidance law.
AutoDerain: Memory-efficient Neural Architecture Search for Image Deraining

Learning-based image deraining methods have achieved remarkable success in the past few decades. Currently, most deraining architectures are developed by human experts, which is a laborious and error-prone process. In this paper, we present a study on employing neural architecture search (NAS) to automatically design deraining architectures, dubbed AutoDerain. Specifically, we first propose an U-shaped deraining architecture, which mainly consists of residual squeeze-and-excitation blocks (RSEBs). Then, we define a search space, where we search for the convolutional types and the use of the squeeze-and-excitation block. Considering that the differentiable architecture search is memory-intensive, we propose a memory-efficient differentiable architecture search scheme (MDARTS). In light of the success of training binary neural networks, MDARTS optimizes architecture parameters through the proximal gradient, which only consumes the same GPU memory as training a single deraining model. Experimental results demonstrate that the architecture designed by MDARTS is superior to manually designed derainers.
Exploring the use of IoT and blockchain technology in a 3rd generation bicycle sharing platform within a smart university campus

The functioning of a smart city relies upon six key factors, which are smart governance, environment, living, people and mobility. This paper focuses on the smart mobility factor, within the context of a smart university which is a surrogate of a smart city. A proof of concept transport system will be developed. The transport system will consist of a third-generation bicycle-sharing platform, which will explore the usage of several technology paradigms such as the Internet of Things (IoT) and blockchain technology. The transportation system will consist of a permissioned Proof Of Authority (POA) blockchain network of docking stations, which users of the smart university campus will interact with through a mobile application to rent and dock bicycles.
Research of Immersion in Virtual Reality Technology for the Stage of Information Systems Design

Virtual reality technology (VR) is a comprehensive technology that allows you to immerse a person in an immersive virtual world using specialized devices (virtual reality helmets). Virtual reality provides a complete immersion in the computer environment surrounding the user and responding to his actions in a natural way, manipulating objects and programmable events in the virtual environment. Virtual reality constructs a new artificial world transmitted to man through his sensations: vision, hearing, touch and others. A person can interact with a 3D, computerized environment, as well as manipulate objects or perform specific tasks by gaining user experience. Virtual reality is an evolving technology for the transfer of user experience (User Experiment: UI) from person to person. Currently, at the stage of information systems design, the use of induced virtual environment technology in dynamic interaction systems leads to the need to analyze the effectiveness of visual perception and transfer to the human neocortex. Visual perception represents a fundamental scientific task and is studied in many areas of science: neurophysiology, psychophysics, psychology, computer graphics and virtual environment, computer vision, computer science theory.
OAB - An Open Anomaly Benchmark Framework for Unsupervised and Semisupervised Anomaly Detection on Image and Tabular Data Sets

We introduce OAB, an Open Anomaly Benchmark Framework for unsupervised and semisupervised anomaly detection on image and tabular data sets, ensuring simple reproducibility for existing benchmark results as well as a reliable comparability and low-effort extensibility when new anomaly detection algorithms or new data sets are added. While making established methods of the most popular benchmarks easily accessible, OAB generalizes the task of un- and semisupervised anomaly benchmarking and offers besides commonly used benchmark data sets also semantically meaningful real-world anomaly data sets as well as a broad range of traditional and state-of-the-art anomaly detection algorithms. The benefit of OAB for the research community has been demonstrated by reproducing and extending existing benchmarks to new algorithms with very low effort allowing researchers to focus on the actual algorithm research.
Uncovering input-sensitive energy bottlenecks in oversubscribed GPU workloads

Graph algorithms are at the core of data-intensive applications in many computational domains. Although accelerator-based implementations have the potential for delivering improved energy efficiency on today’s large-scale graph analytics, there are many challenges to reaching the desired goals. This paper examines the issues of GPU oversubscription and input sensitivity in energy efficient graph processing. We introduce the notion of energy signatures, a collection of runtime events that can determine fluctuations in power consumption as a function of input graph characteristics. The study shows that variations in volume, density, diameter and dispersion in graphs can alter the runtime behavior of GPU applications and these changes in program behavior in turn impact the energy efficiency of the application. Based on the insight gained from our study, we develop three input-aware optimization techniques to mitigate energy bottlenecks. On experiments conducted on a Unified Memory supported GPU, the optimizations yield a 11.97% energy savings on average on Breadth-First Search and Single-Source Shortest Path algorithms.
Based on WeChat Small Procedures County-Level Fresh Shopping Supermarket Operation Analysis

With the continuous development and improvement of RFID technology and communication technology, the application of the Internet of things is getting closer and closer to our lives. In view of the low operating efficiency of the supermarket shopping system, it is difficult for consumers to find the desired products in the shopping process, and it takes a lot of time to queue up, etc., so fast shopping methods with the ability of WeChat payment, scan and other WeChat products have emerged one after another. The purpose of this paper is to study the operation of local fresh shopping supermarkets at the county level based on WeChat small program and to make suggestions on how to better combine offline shopping with mobile technology. The system is mainly composed of WeChat applet client, server and data management terminal. The server adopts the Think PHP5 framework technology, the client adopts the MINA framework of WeChat applet, and the data management terminal adopts the CMS framework. The client page implementation communicates with the server REST API interface over the network to obtain My SQL data. After the program was put into use in two supermarkets in a county for a period of time, this paper conducted a questionnaire survey on the supermarket customers and collected the evaluation on the application of WeChat small program in the supermarket. The result showed that 84% of the customers were satisfied. Based on this, technical suggestions are provided for the popularization of WeChat applet in the operation of shopping supermarket in the future.
The Object Recognition Research Based on Convolution Neural Network

Convolution neural networks include convolution computation and feedforward neural networks with depth structure. It is one of the most successful application fields of deep learning algorithm that can learn a lot of mapping relationship between input and output without any precise mathematical expression between input and output. The basic structure of convolutional neural network is input layer, convolution layer, pooling layer, full connection layer and output layer. Some improvements are proposed in this paper. First, a convolution layer and a pool layer are added to the original basic structure. Second, the new structure adopts hybrid pool in the pool stage. Thirdly, the maxout activation function is used in the full connection layer. Finally, based on the data set cifar-10, this paper studies the training and testing of convolutional neural networks for 10 categories of aircraft, horse, bird, ship, deer, dog, frog, automobile, cat and truck. The experimental results show that the accuracy rate of testing is 69.48%. Obviously the testing result is satisfactory.
Global-local Feature Aggregation Strategy with Optimal Probability for Video Object Detection

For the challenge of video object detection, feature aggregation between frames in the video can effectively improve the object detection accuracy. However, the existing literature does not have sufficient researches on frame selection strategies. A global-local feature aggregation strategy based on the optimal probability is proposed to solve the problem of the effectiveness and efficiency of frame selection, aiming at more efficient selection of frames for feature aggregation. To obtain the similarity probability between the current object proposals and other frames, all of the suggestion boxes in each frame are calculated the similarity probability with the current object proposals. And then the partial frames are selected with the best similarity probability for feature aggregation. As shown in the experimental results, compared with the existing video object detection algorithms, the mean average precision of the algorithm using the proposed strategy in this article is improved.
Trainable Preprocessing for Reduced Precision Neural Networks

Applications of neural networks are emerging in many fields and are frequently implemented in embedded environment, introducing power, throughput and latency constraints next to accuracy. Although practical computer vision solutions always involve some kind of preprocessing, most research focuses on the network itself. As a result, the preprocessing remains optimized for the human perception and is not tuned to neural networks. We propose the optimization of preprocesing along with the network using backpropagation and gradient descent. This open up the accuracy versus implementation cost design space towards more cost-efficient implementations by exploiting reduced precision input. In particular, we evaluate the effect of two preprocessing techniques: color conversion and dithering, using CIFAR10 and ImageNet datasets with different networks.
A Class Attendance System Based on Cloud Face Recognition for Multi-users

This paper introduces a class attendance system for multi-users. This system is based on an OpenCV image recognition module to design a face recognition application that can be used for check-in. OpenCV is used as the primary control recognition module to recognize and track the target face through image processing. Meanwhile, Baidu Cloud stores a face database and provides face recognition and matching scores for face images. This system builds on a client-server structure so that multiple users can use it in different classrooms to check-in at the same time. All faculties can use this for class attendance purposes at any place.
An Efficient Neural Network based on Early Compression of Sparse CT Slice Images

Recently, research on diagnosing diseases through artificial intelligence has been conducted in various medical fields, including Thyroid-associated ophthalmopathy. We introduce a computationally efficient CNN architecture, which is optimized for CT images and designed especially for mobile devices with very limited computing power. The proposed architecture utilizes three operations, pointwise convolution, depth-wise separable convolution and channel shuffle, to reduce computation cost for handling a series of CT image slices for a patient. On CT images, the proposed model achieves ∼ 3.5 × actual speedup over ShuffleNet-v2 without degenerating prediction accuracy.
Generative Adversarial Networks With Attention Mechanisms at Every Scale

Existing works in image synthesis have shown the efficiency of applying attention mechanisms in generating natural-looking images. Despite the great informativeness, current works utilize such mechanisms at a certain scale of generative and discriminative networks. Intuitively, the increased use of attention should lead to a better performance. However, due to memory constraints, even moving a single attention mechanism to a higher scale of the network is infeasible. Motivated by the importance of attention in image generation, we tackle this limitation by proposing a generative adversarial network-based framework that readily incorporates attention mechanisms at every scale of its networks. A straightforward structure of attention mechanism enables direct plugging in a scale-wise manner and trains jointly with adversarial networks. As a result, networks are forced to focus on relevant regions of feature maps learned at every scale, thus improving their own image representation power. In addition, we exploit and show the usage of multiscale attention features as a complementary feature set in discriminator training. We demonstrate qualitatively and quantitatively that the introduction of scale-wise attention mechanisms benefits competitive networks, thus improving the performance compared with those of current works.
A Multi-Channel Graph Attention Network for Chinese NER

Incorporating lexicons into Chinese NER via lattice inputs is proven to be effective because it can exploit both boundary and semantic information of words to achieve better results. Previous works have tried various structures such as RNN, CNN, GNN and Transformer to accommodate lattice inputs with great success. In this paper, we propose a Multi-Channel Graph Attention Network (MCGAT), which consists of three word-modified graph attention networks. MCGAT considers relative position relations between characters and words, and combines statistical information of word frequency and pointwise mutual information to further improve the performance of the model. Experiments on four datasets show that our model achieves comprehensive improvements on the original model and outperforms the state-of-the-art model on three datasets using the same lexicon. MCGAT is competitive in terms of efficiency and has a faster inference speed than the transformer-based model. We also demonstrate that MCGAT can significantly improve the performance of pre-trained models like BERT as a downstream network.
Ultra-Short-term PV Power Forecasting Based on a Support Vector Machine with Improved Dragonfly Algorithm

Photo-voltaic (PV) is one of the most abundant sources on the earth for the generation of electricity. Although, due to the stochastic nature of PV characteristics to sustain constant power, an accurate PV power prediction is needed for a grid-connected PV system. The proposed model of support vector machine (SVM) with improved dragonfly algorithm(IDA) is used to forecast the PV power. Previously, Theexecution can be done by dragonfly algorithm (DA) through adaptive learning factor along with the differential evolution technique. The IDA is used to select the best support vector machine parameters. Eventually, the suggested model provides better performance as compared to the other algorithm such as SVM with dragonfly algorithm(SVM-DA). It is suitable for forecasting ultra-short-term PV power.
Fault Diagnosis of Power Transformer Based on Stacked Sparse Auto-Encoders and Broad Learning System

The power transformer is an important part of the power system, and the diagnosis of the power transformer plays an important role in its safe operation. In order to improve the reliability and accuracy of power transformer fault diagnosis, this paper uses the dissolved gas in power transformer oil to propose a fault diagnosis method based on stacked sparse auto-encoders(SSAE) and broad learning system(BLS). The sparse auto-encoder has a powerful data reconstruction ability, which can extract the essential characteristics of the fault data by reconstructing the original data, and improve the diagnostic accuracy. The broad learning system reconstructs the network through incremental learning, and uses the pseudo-inverse calculation method to quickly solve the hidden layer-output layer weight, avoiding the use of gradient update method, improving training speed and preventing local optimization. Using KNN classifier to realize the feature clustering and label classification of the target domain samples. The simulation results show that the proposed method can effectively identify the fault type of power transformers with a satisfactory accuracy.
Learning-based Assistant for Data Migration of Enterprise Information Systems

Data migration from source to target information system is a critical step for modernizing information systems. Central to data migration is data transform that transforms the source system data into target system. In this paper we present a tool that assists the experts in creating the data transformation specification by (a) suggesting candidate field matches between the source and target data models using machine learning and knowledge representation, and (b) rules for the data transformation using program synthesis. It takes the expert’s feedback for the identified matches and synthesized rules and proposes new matches and transformation rules. We have executed our tool on real-life industrial data. Our schema matching recall at 5 is 0.76, while for the rule generator recall at 2 is 0.81.
Geochemical Exploration

Each mineral deposit type has a set of characteristics—often clearly defined and well understood—some of which will be very different from the host rocks and will be therefore used in mineral exploration. The deposits’ physical characteristics will be used in geophysical exploration, their lithological, mineralogical, and structural characteristics will be used in prospecting, and their chemical characteristics will be used in geochemical exploration.
Energy efficient based algorithm of robots swarm searching in an unknown area

In the International Space Station, there is a common problem with air leaks [1]. Astronauts spend months on fixing that issue. Due to this issue, NASA has to carry more air every time, increasing the costs of the flights, and reducing the amount of useful weight. In addition, the astronauts have to spend more time on these routine tasks, when they could make some measurements and experiments. A swarm of micro robots with measuring devices would be able to find the leaking points faster and without big energy costs.