Reviewerly/exHarmony huggingface dataset:
What is the Value of the Cross-Sectional Approach to Deep Reinforcement Learning?

Reinforcement learning (RL) for dynamic asset allocation is an emerging field of study. Total return, the common performance metric, is useful for comparing algorithms but does not help us determine how close an RL algorithm is to an optimal solution. In real world financial applications, a bad decision could prove to be fatal. One of the key ideas of our work is to combine the two paradigms of the mean-variance optimization approach (Markowitz criteria) and the optimal capital growth approach (Kelly criteria) via the actor-critic approach. By using an actor-critic approach, we can balance optimization of risk and growth by configuring the actor to optimize the mean-variance while the critic is configured to maximize growth. We propose a Geometric Policy Score used by the critic to assess the quality of the actions taken by the actor. This could allow portfolio manager practitioners to better understand the investment RL policy. We present an extensive and in-depth study of RL algorithms for use in portfolio management (PM). We studied eight published policy-based RL algorithms which are preferred over value-based RL because they are better suited for continuous action spaces and are considered to be state of the art, Deterministic Policy Gradient (DPG), Stochastic Policy Gradients (SPG), Deep Deterministic Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), Soft Actor Critic (SAC), and Evolution Strategies (ES) for Policy Optimization. We implemented all eight and we were able to modify all of them for PM but our initial testing determined that their performance was not satisfactory. Most algorithms showed difficulty converging during the training process due to the non-stationary and noisy nature of financial environments, along with other challenges. We selected the four most promising algorithms DPG, SPG, DDPG, PPO for further improvements. The modification of RL algorithms to finance required unconventional changes. We have developed a novel approach for encoding multi-type financial data in a way that is compatible with RL. We use a multi-channel convolutional neural network (CNN-RL) framework, where each channel corresponds to a specific type of data such as high-low-open-close prices and volumes. We also designed a reward function based on concepts such as alpha, beta, and diversification that are financially meaningful while still being learnable by RL. In addition, portfolio managers will typically use a blend of time series analysis and cross-sectional analysis before making a decision. We extend our approach to incorporate, for the first time, cross-sectional deep RL in addition to time series RL. Finally, we demonstrate the performance of the RL agents and benchmark them against commonly used passive and active trading strategies, such as the uniform buy-and-hold (UBAH) index and the dynamical multi-period Mean-Variance-Optimization (MVO) model.
The Validity and Practicality of Augmented Reality Based Learning Media for Computer Basics Courses

Abstract “Augmented Reality is a technology that gets a lot of attention because it can combine text and image media into its world with these advantages Augmented reality is expected to be a solution in the need for learning media in this study will reveal the validity and practicality of augmented reality-based learning media with research subject students majoring in technology curriculum in the field of education at Padang State University with the results of the study showing that augmented reality based media is valid and practical for lecturers and students
Design of McEliece Cryptosystem Based on QC-MDPC Codes

The McEliece public key cryptosystem based on QC-MDPC codes has been shown to be a relatively secure system. McEliece cryptosystem has been shown to quantum resistant, which is faster and simpler compared with RSA. When different types of codes are used, most of the ciphers are easy to be cracked. Initially, the keys were all large matrices. For example, the size of the generating matrix of Goppa codes is 1024×524, and it increases exponentially with the increase of the coding length and the number of generated polynomials. In this paper, we use QC-MDPC codes instead of Goppa codes to reduce the key sizes and improve the decoding algorithm, which can resist all kinds of attacks. In the 5G era of the Internet of things, the application of this cryptosystem can guarantee more access security.
A Study and comparative analysis of development tools for Virtual Reality applications

Technology has evolved a lot now-a-days. There is tremendous change in the virtual world as well. Virtual Reality (VR) is the technology where things can be viewed virtually. In other words it is the replica of real world which can be viewed by a person if the person is unable to physically go to that place. There are plenty of tools and applications available in the market to develop VR applications. The objective of this research paper to provide a glimpse of these available tools and the comparison of various features among them. The outcome of this research will help VR application developers and start-up companies to quickly decide on which tool will be better for their selected business use case.
Automatic recognition and tracking of liver blood vessels in ultrasound image using deep neural networks

Medical ultrasound devices are widely used in clinic because of its convenience, rapid and non-invasive. But ultrasound (US) images have the characteristics of large speckle noise, unclear target and low brightness. Since the deep learning theory has been developed, the accuracy of the tasks in the field of image has been greatly improved. In this paper, a deep neural network structure is established to automatic detect and track the liver vessel targets. Firstly, the dataset is augmented and preprocessed using histogram equalization. Secondly, the RetinaNet is implemented to extract the region of interest (ROI) in the US image. Then, the U-net is used to extract the features of the ROI, and deconvolution is implemented to restore the feature matrix to the size of the original image, which realize the automatic segmentation of blood vessels. Finally, the LSTM network is used to predict the information of vessels in the subsequent image. Experimental results show that the proposed algorithm is fast and robust. The accuracy of the ROI detection is 98.9%. The average error of the distance of the center point of the target is less than 1 mm.
Performance Appraise of Machine Learning Classifiers in Image Splicing Detection using Thepade’s Sorted Block Truncation Coding

Image Splicing is known as a conventional type of digital image manipulation. It is one such type of tampering; also called as image composition. A spliced (or composite) image is usually created by copying and pasting portions of the image onto the same or another image. Spliced image detection mainly deals with finding similarity present in an image and establishing a relationship between authentic image parts and pasted portions of the image. With the increasing popularity and usage of easily available image editing technologies, even for people with minimal expertise it has become much easier to edit image data. Hence splicing is becoming sophisticated day by day making it difficult to detect with naked eyes. Due to the advent of social media and other platforms these spliced images can be circulated in faster ways among users of those platforms and hence it becomes necessary to come up with methods of spliced image detection. This paper proposes use of Thepade's Sorted BTC, various Machine Learning classifiers for splicing detection. Here TSTBTC-Nary is explored with values of n as 2, 4, 6,...16,18 attempted on some machine learning classifiers (BayesNet, NaiveBayes, Logistic, Simple Logistic, SVM, JRip, PART, J48, LMT) for various performance metrics. After validation on 3 benchmark datasets CASIA V1, Columbia and Columbia-Uncompressed, LMT classifier performs better closely followed by Simple Logistic and J48. Better image splicing capabilities are observed with TSTBTC 16-ary closely followed by 18-ary and 14-ary.
The integration of dynamic demand in electricity markets: Blockchain 3.0 as an enabler of microgrid energy exchange, demand response and storage

Microgrid level operated energy markets have specificities well served rather by autonomous instead of aggregated approaches. Ancillary services and peer-to-peer energy exchange also could be organized at the aggregator level but on each prosumer level using blockchain 3.0 solutions. Blockchain 3.0 provides operational advantages such as reduced transaction costs due to the absence of a trusted authority and the lack of single point of failure but it requires relatively increased computational resources and enhanced measures to safeguard privacy. Existing blockchain implementations include Bitcoin, Ethereum, Hyperledger, Corda, Tendermint, Chain Core, Quorum. Each one of these demonstrates different characteristics and they have better fit to different energy market applications. Blockchain energy markets could be organized on Implementation, System Local and Entities level. This chapter provides the relevant bibliography and analyses Blockchain 3.0 technology giving emphasis to storage and demand management microgrid applications.
Analysis of Orthogonal Matching Pursuit Using Orthogonal Bases on Digital Image

Abstract In this paper, we develop an image processing technique that is developed to build any image into the original image, with high image quality and small storage space. The image to be used is in the form of an RGB image which is then processed into a binary image to be represented in the form of a matrix. In addition, we also corrected Compression Sensing to reduce the image sampling rate and improve the accuracy of image reconstruction. In this study, the Orthogonal Matching Pursuit algorithm was used for the image reconstruction process. While the bases used in the image reconstruction process using the Orthogonal Matching Pursuit (OMP) Algorithm are the bases of Haar, Hadamard, and Walsh. The results of the research after reconstruction are that the results of high quality images are obtained, but have less storage space, have good MSE and PSNR values and require a short time during reconstruction.
Person Re-identification via Deep Metric Learning

Now a day's everyone are facing a wide range and variety of threats, ranging from robbery, kidnapping, and terrorism to murder. To avoid these threats, it is necessary for the authorities to collect real time information on what is happening in and around the city. Therefore, to make the cities safer and risk free, new technologies are being developed. Here we built a reliable system that recognizes the person from any angle from a captured image. We can get the input to the systems through CCTV cameras installed in public places where these kinds of life-threatening events take place. It is easy to install these cameras at public places and more easy to monitor and store the data. The developed system uses deep metric learning and the machine learning platform, tenser flow and keras. It is a type of machine learning where system iteratively performs computations to know the patterns. The system processes captured images and compares with existing dataset images to recognize the person. The comparison is done based on certain selected features. The results are more accurate (98.18 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) compared to existing systems.
Motor Failure Detection in FPGA-Based Fault-Tolerant Quadcopters

Quadcopters have recently become a very important component in search and rescue missions. This paper proposes solutions to several problems facing quadcopters in such missions. First, a fault-tolerant FPGA-based architecture is developed which can recover from both soft and hard failures. It is then proven through reliability modeling that this architecture has a considerably higher lifetime than a similar architecture with no fault tolerance. Second, it is shown how to use accelerometers to detect motor faults in order to improve mission decision-making.
Application of Generative Adversarial Network in Semantic Segmentation

In order to improve the accuracy of image segmentation without changing the structure of original semantic segmentation models, an approach to train semantic segmentation models by using generative adversarial network (SS-GAN) is proposed. Using adversarial network to distinguish the source of segmented images, the model can learn the high-order relationship between pixels to enhance the spatial continuity of pixels in the segmented image. There are three aspects related to the work: constructing the generative model of fully convolutional network (FCN) structure by using VGG, and segment image preliminarily; constructing the adversarial model and training it by combining the original images, fake segmented images and real segmented images; modifying the loss function, adding the anti-loss to assist segmentation model training, encouraging generative network to learn the inter-pixel relationship independently. Experiments on PASCAL VOC and Cityscapes datasets show that the proposed method achieves better performance than the existing advanced methods, and improves IoU by 1.56% and 1.93%, respectively.
Semantic Matching for Short Texts: A Cross Attention Mechanism

Abstract Semantic matching is a hot topic in the field of NLP. It is widely used in information matching, machine translation, intelligent Q&amp;A, and answering system. With the rapid development of artificial intelligence, people pay more attention to friendly human-computer interaction and how to obtain accurate and effective information. Aiming at the problem of the diversity and structure of Chinese, this paper introduces the cross attention mechanism. Texts provide attention to each other, then to information retrieval. Experiments on aetc-nlp dataset show that the method is effective.
A Method for Localizing and Grasping Objects in a Picking Robot System Using Kinect Camera

This paper proposes a method for localizing and grasping objects in a picking robot system using Kinect stereo camera sensor. To do this task, the followings are done. Firstly, an image processing system including Kinect camera sensor is described. Secondly, RGB color map and new depth map for image inpainting are obtained using Kinect SDK mapping function to align RGB image with depth image. Thirdly, a new rectangle representation algorithm based on the Kinect camera is proposed to localize the grasping point of the object. Finally, the effectiveness and the applicability of the proposed algorithms is verified by using experiment. The experimental results show that the proposed algorithm successfully detects an object and finds its grasping points with accuracy 100% for no transparent objects and with 95% for transparent objects. The proposed algorithm also has higher accuracy to successfully find the grasping points than the 2D planar method.
Efficient self-testing system for quantum computations based on permutations*

Verification in quantum computations is crucial since quantum systems are extremely vulnerable to the environment. However, verifying directly the output of a quantum computation is difficult since we know that efficiently simulating a large-scale quantum computation on a classical computer is usually thought to be impossible. To overcome this difficulty, we propose a self-testing system for quantum computations, which can be used to verify if a quantum computation is performed correctly by itself. Our basic idea is using some extra ancilla qubits to test the output of the computation. We design two kinds of permutation circuits into the original quantum circuit: one is applied on the ancilla qubits whose output indicates the testing information, the other is applied on all qubits (including ancilla qubits) which is aiming to uniformly permute the positions of all qubits. We show that both permutation circuits are easy to achieve. By this way, we prove that any quantum computation has an efficient self-testing system. In the end, we also discuss the relation between our self-testing system and interactive proof systems, and show that the two systems are equivalent if the verifier is allowed to have some quantum capacity.
A Fast Algorithm for Image Segmentation Based on Global Cosine Fitting Energy Model

Image segmentation plays an important role in computer vision and image processing. Level set model is a classical image segmentation method. During level set evolution, almost all the level set energy minimization of image segmentation are based on the gradient descent method and the finite difference scheme. The speed of evolution is slow and easy to fall into local minima. In this paper, we propose a fast sweeping optimization algorithm to minimize global cosine fitting (GCF) model. When moving a pixel from the one side region to the another side region of evolving contour, the sweeping algorithm calculates the energy change directly and checks whether the cosine fitting energy is decreased. With this, we can avoid solving the Euler-Lagrange equation and the partial differential equation, which usually take a lot of time. Moreover, our proposal is robust to initial level set contour and it automatically handles the topological variety, algorithm automatic termination and no longer requires the reinitialization step, parameter adjustment and the distance regularization term. The experiments on real noise and synthetic images show the effectiveness of the sweeping algorithm.
The band structure of a model of spatial random permutation

We study a random permutation of a lattice box in which each permutation is given a Boltzmann weight with energy equal to the total Euclidean displacement. Our main result establishes the band structure of the model as the box-size N tends to infinity and the inverse temperature $$\beta $$ tends to zero; in particular, we show that the mean displacement is of order $$\min \{1/\beta , N\}$$ . In one dimension our results are more precise, specifying leading-order constants and giving bounds on the rates of convergence. Our proofs exploit a connection, via matrix permanents, between random permutations and Gaussian fields; although this connection is well-known in other settings, to the best of our knowledge its application to the study of random permutations is novel. As a byproduct of our analysis, we also provide asymptotics for the permanents of Kac–Murdock–Szegő matrices.
The Text Mining and Classification Analyses of Tumor Based on Twitter

The study is combined with the text mining and classification method to analyze the tumor-related tweets on Twitter in order to comprehend the public opinion focus on the social media. The contribution of the study included the following two points. First, the study used the text mining method to explore the content of tumor-related tweets and found the keywords according to their frequencies. Second, the study applied the classification analysis with four different algorithms to explore the relationship and the importance of the keywords. The study also found the random forests model with the best performance in four models and concluded that the Twitter users focused more on surgery issues, psychological issues and the technical issues on the topic of tumor.
Efficient Estimation of General Additive Neural Networks: A Case Study for CTG Data

This paper discusses the concepts of interpretability and explainability and outlines desiderata for robust interpretability. It then describes a neural network model that meets all criteria, with the addition of global faithfulness. This is achieved by efficient estimation of a General Additive Neural Network, seeded by a conventional Multilayer Perceptron (MLP) by distilling the dependence on individual variables and pairwise interactions, so that their effects can be represented within the structure of a General Additive Model. This makes the logic of the model clear and transparent to users, across the complete input space. The model is self-explaining. The modelling approach used in this paper derives the partial responses from the MLP, resulting in the Partial Response Network (PRN). Its application is illustrated in a medical context using the CTU-UHB Cardiotacography intrapartum database (n = 552) to infer the features associated with caesarean deliveries. This is the first application of the PRN to this data set and it is shown that the self-explaining model achieves comparable discrimination performance to that of Random Forests previously applied to the same data set. The classes are highly imbalanced with a prevalence of caesarean sections of 8.33%. The resulting model uses 4 from 8 possible features and has an AUROC of 0.69 [CI 0.60, 0.77] estimated by 4-fold cross-validation. Its performance and features are compared also with those from a Sparse Additive Models (SAM) which has an AUROC of 0.72 [CI 0.64, 0.80]. This is not significantly different and requires all features. For clinical utility by risk stratification, the odds-ratio for caesarian section vs. not at the prevalence threshold is 3.97 for the PRN, better 3.14 for the SAM. Compared for consistency, parsimony, stability and scalability the models have complementary properties.
Intelligent Fault Diagnosis with Deep Architecture

The status judgment of converter valve equipment is an important part of ultra high voltage maintenance. However, accurate judgment of possible failures remains challenging. This paper proposes a novel multi-region deep architecture to improve abnormal judgement by considering long-range context information in increasingly finer spatial regions. Specifically, the specified characteristics of video sequence will be completely explored through C3D and recurrent neural network with LSTM cells, extracting high-level spatio-temporal features. In addition, spatial pyramid crop is proposed to characterize each video at different spatial pyramid levels. These sampling sub-videos can encode locally saliency among categories. Experimental results demonstrate the effectiveness of our model, compared with other state-of-the-art approaches.
Hardware-Aware NAS Framework with Layer Adaptive Scheduling on Embedded System

Neural Architecture Search (NAS) has been proven to be an effective solution for building Deep Convolutional Neural Network (DCNN) models automatically. Subsequently, several hardware-aware NAS frameworks incorporate hardware latency into the search objectives to avoid the potential risk that the searched network cannot be deployed on target platforms. However, the mismatch between NAS and hardware persists due to the absent of rethinking the applicability of the searched network layer characteristics and hardware mapping. A convolution neural network layer can be executed on various dataflows of hardware with different performance, with which the characteristics of on-chip data using varies to fit the parallel structure. This mismatch also results in significant performance degradation for some maladaptive layers obtained from NAS, which might achieved a much better latency when the adopted dataflow changes. To address the issue that the network latency is insufficient to evaluate the deployment efficiency, this paper proposes a novel hardware-aware NAS framework in consideration of the adaptability between layers and dataflow patterns. Beside, we develop an optimized layer adaptive data scheduling strategy as well as a coarse-grained reconfigurable computing architecture so as to deploy the searched networks with high power-efficiency by selecting the most appropriate dataflow pattern layer-by-layer under limited resources. Evaluation results show that the proposed NAS framework can search DCNNs with the similar accuracy to the state-of-the-art ones as well as the low inference latency, and the proposed architecture provides both power-efficiency improvement and energy consumption saving.