Reviewerly/exHarmony huggingface dataset:
Legal Effect of Smart Contracts Based on Blockchain

Smart contracts are increasingly used in financial innovation area. In view of smart contracts' legal effects, a study is implemented in this paper. First, Smart contracts appear as a set of computer codes, and carry the mutual consensus of the transaction parties and under the principle of freedom of contract form. Thus, smart contracts can be understood as a type of contractual written form in the high-tech context. Then the agreement automatic enforcement was made by the five-element structure, and the traditional situation of enforcement uncertainty was avoided. The results show that it is necessary to examine whether the meaning of the machine matches the meaning of the party, and whether the machine meaning is in conformity with the legal provisions. Finally, the conclusion was drawn that the legal effect of smart contract can be clarified in response to the booming financial technology.
Reliability versus Vulnerability of <i>N</i>-Version Programming Cloud Service Component With Dynamic Decision Time Under Co-Resident Attacks

The virtual machine (VM) co-resident architecture of cloud computing enables simultaneous provision of multiple services to different users, but also makes these services vulnerable to co-resident attacks. For example, by establishing side channels, a malicious attacker can access and even corrupt services performed by other VMs co-residing on the same server as the attacker's VM (AVM). We model a threshold-voting-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -version programming service component with multiple independent versions simultaneously performing the same requested service to enhance the service reliability. However, the reliability enhancement can be greatly hindered by the co-resident attack, which may corrupt an adequate number of versions leading to a wrong output. We formulate and solve constrained optimization problems that determine the number of service component versions and the voting threshold to balance two conflicting service performance metrics: reliability (service component success probability) and vulnerability (service corruption attack success probability). Two cases respectively having certain and uncertain knowledge about the attacker's power in terms of the number of AVMs are considered. We also investigate impacts of different model parameters on the service performance as well as on solutions to the considered optimization problems through examples.
Creation of Face for Aiding in Forensic Investigation based on Textual Description

In this work, we have developed a solution that will assist the police department in the criminal investigation process during drawing sketches of a suspect based on the textual description from a witness. We propose a deep learning implementation based on Generative Adversarial Network that consists of generator which generates an image based on text description and discriminator which validates the image generated by generator with the data set. We trained a subset of 400 images collected from the Labeled Face In Wild dataset. The description of images was taken from Face2Text data provided by Albert Gatt. The images generated resemble certain features mentioned in the textual description which can directly aid the cops or can act as an intermediary for sketch artists to work upon.
Assessing the Reliability of Hybrid Clouds with Monte Carlo Simulation

Cloud computing and edge computing are widely used in various fields, providing users with flexible computing resources while ensuring low latency. With the complexity of system structures and the variety of deployment methods, the reliability of cloud applications is difficult to obtain. To tackle this problem, we propose a reliability assessment method based on Monte Carlo Simulation in the environment of hybrid clouds which include public clouds, private clouds, and edge clouds. To verify our method, we have conducted relevant experiments and showed its feasibility.
Work-Efficient Parallel Non-Maximum Suppression Kernels

Abstract In the context of object detection, sliding-window classifiers and single-shot convolutional neural network (CNN) meta-architectures typically yield multiple overlapping candidate windows with similar high scores around the true location of a particular object. Non-maximum suppression (NMS) is the process of selecting a single representative candidate within this cluster of detections, so as to obtain a unique detection per object appearing on a given picture. In this paper, we present a highly scalable NMS algorithm for embedded graphics processing unit (GPU) architectures that is designed from scratch to handle workloads featuring thousands of simultaneous detections on a given picture. Our kernels are directly applicable to other sequential NMS algorithms such as FeatureNMS, Soft-NMS or AdaptiveNMS that share the inner workings of the classic greedy NMS method. The obtained performance results show that our parallel NMS algorithm is capable of clustering 1024 simultaneous detected objects per frame in roughly 1 ms on both Tegra X1 and Tegra X2 on-die GPUs, while taking 2 ms on Tegra K1. Furthermore, our proposed parallel greedy NMS algorithm yields a 14–40x speed up when compared to state-of-the-art NMS methods that require learning a CNN from annotated data.
Weibo Authorship Identification based on Wasserstein generative adversarial networks

During the past years, authorship identification has played a significant role in the public security area. Recently, deep learning based approaches have been used in authorship identification. However, all approaches based on deep learning require a large amount of original data, while the author only posts a limited number of texts to be used as positive samples. As such, the issue of data missing and class imbalance arises, resulting in the classifier overfitting on small data. Furthermore, this approach can be biased by prior of the data. To address these issues, in this paper, we use Wasserstein generative adversarial networks (WGANs) to generate samples for the positive class which lacks of data and present a novel data augmentation framework for authorship identification. To form an augmented training dataset, we mix the generated new samples with the original features in the dataset. As such, the classifier trained on it will not suffer from overfitting and class imbalance, and thus has substantially improved performance. Using crawled Sina Weibo dataset, we empirically evaluate this data augmentation. The experimental results show that our method has a significant improvement of accuracy by 14% compared with powerful baselines. We further validate its effectiveness via a set of comparison experiments.
Tensor Based Analysis Dictionary Learning for Color Video Denoising

In this paper, by utilizing the tensor computation, we propose a novel analysis dictionary learning (ADL) algorithm named tensor analysis dictionary learning (TADL) to effectively represent multidimensional (MD) signals and apply it for color video (four-dimensional signal) denoising. By incorporating correlations among various channels, TADL can handle an arbitrary order tensor of the MD signals more naturally. Different from what is performed in existing ADL algorithms, in TADL the dimensional features of the MD signals are represented by a series of dictionaries collaboratively, and the dictionary atoms are updated simultaneously by adopting the simultaneous codeword optimization (SimCO) on tensors, leading to a computationally efficient optimization process. Experiments show that, 1) the proposed TADL algorithm can deal with color video denoising well, which is impossible for the standard ADL algorithm, and 2) when the noise intensity is relatively high, TADL outperforms state-of-the-art ADL algorithms in terms of computational efficiency.
Distributed Architecture for Fake News Detection

Countering the fake news phenomenon has become one of the most important challenges for democratic societies, governments and non-profit organizations, as well as for the researchers coming from several domains. This is not a local problem, and demands a holistic approach to analyzing heterogeneous data and storing the results. The major contribution of this paper is the proposition of an innovative distributed architecture to tackle the above-mentioned problems. The architecture uses state-of-the-art technologies with focus on efficiency, scalability and also openness, so that community-created components and analyzers could be added.
SEB-Net

As a research area of computer vision and deep learning, scene understanding has attracted a lot of attention in recent years. One major challenge encountered is obtaining high levels of segmentation accuracy while dealing with the computational cost and time associated with training or inference. Most current algorithms compromise one metric for the other depending on the intended devices. To address this problem, this paper proposes a novel deep neural network architecture called Segmentation Efficient Blocks Network (SEB-Net) that seeks to achieve the best possible balance between accuracy and computational costs as well as real-time inference speed. The model is composed of both an encoder path and a decoder path in a symmetric structure. The encoder path consists of 16 convolution layers identical to a VGG-19 model, and the decoder path includes what we call E-blocks (Efficient Blocks) inspired by the widely popular ENet architecture's bottleneck module with slight modifications. One advantage of this model is that the max-unpooling in the decoder path is employed for expansion and projection convolutions in the E-Blocks, allowing for less learnable parameters and efficient computation (10.1 frames per second (fps) for a 480x320 input, 11x fewer parameters than DeconvNet, 52.4 GFLOPs for a 640x360 input on a TESLA K40 GPU device). Experimental results on two outdoor scene datasets; Cambridge-driving Labeled Video Database (CamVid) and Cityscapes, indicate that SEB-Net can achieve higher performance compared to Fully Convolutional Networks (FCN), SegNet, DeepLabV, and Dilation8 in most cases. What's more, SEB-Net outperforms efficient architectures like ENet and LinkNet by 16.1 and 11.6 respectively in terms of Instance-level intersection over Union (iLoU). SEB-Net also shows better performance when further evaluated on the SUNRGB-D, an indoor scene dataset
WEB USAGE MINING-A Study of Web data pattern detecting methodologies and its applications in Data Mining

As we know that net one of the most appropriate technology to share and collect information and the utilization of net is raising every day. Computers are a repository of knowledge that contains huge amount of information; web mining i.e one of the operations of data mining process that utilized for finding information and knowledge present in the Web data. The information is gathered from the server, client, proxy server or database in Web mining. Classification of Web mining methods: web content mining, web structure mining and web usage mining. This paper provides general idea of web minings its types. Our review paper mainly focus on the web usage mining and methods utilize in it. Also it gives a review on the application of web usage mining.
A Parallel Algorithm for Graph Transaction Based Frequent Subgraph Mining

Frequent subgraph patterns play an important role in feature mining for graph data. The problem of mining these patterns is defined as finding subgraphs that appear frequently according to a given frequency threshold. Usually, frequent subgraph mining (FSM) is conducted in graph transaction setting, in which graph database contains many small graphs. Since multicore processors are quite popular this day, many algorithms can be accelerated with multi-thread technique. This paper proposed a multi-thread frequent subgraph mining algorithm and achieved considerable acceleration in the experiments. In this paper, a parallel frequent subgraph mining algorithm named PTRGRAM (Parallel Transaction based Graph Mining) which can take full advantage of the multi-core performance of current processors was proposed. In the algorithm, the data synchronization between multiple threads is based on the producer-consumer model. In addition, to speed the support computing, the embedding node list is introduced for optimization. Finally, experimental performance evaluations were conducted with two graph datasets, demonstrating that the proposed algorithm outperforms the single-threaded gSpan and FFSM algorithm.
Performance Analysis of FBMC-OQAM System for Barcode and QR Code Image Transmission

Nowadays, the use of barcode and QR code has been very common. The implementation of barcode and QR code is not only to ease the identification and inventory of goods but it is also used for goods tracking, place tracking, document management, and others. The performance of FBMC-OQAM communication system for image transmission of barcode and QR code become important to be researched considering both image data input have different characteristics. This study uses Zero Forcing (ZF) equalization as a symbol detection. The result of the study shows that in general, the accepted image data on system which used ZF equalization is better than the other without using ZF. The result of simulation also showed BER on barcode image as much as 1.94e-03 and on QR code as much as 8.125e-05 which meant that the performance of QR code transmission system earns better result compared to barcode. Based on the reading process of the data by reader or scanner, it showed that barcode image needed higher SNR, that was 27 dB compared to QR code image that only needed SNR 20 dB. In addition, barcode image enables data misreading even though the reader or scanner could detect the code that was on SNR 19 dB, it is different from QR code which the result of image reading earned correct information if it reached threshold.
Hierarchical Label Embedding Networks for Financial Document Sentiment Analysis

With the rapid development of the Internet, document data have become an important source of information in the financial field. The application of documents sentiment analysis in the financial field has attracted increasing attention. It is obviously impractical to extract sentiments manually from a large amount of financial document, but natural language processing (NLP) technology can solve this problem. The research object of this paper focuses on the research reports of listed companies, which is a kind of long financial document published by experts in the field. In this paper, we propose a hierarchical label embedding neural network model for sentiment analysis of financial documents. This model adopts hierarchical network structure to capture the structural information of financial documents. Moreover, the model also includes an expression embedding mechanism for focusing on important content. We believe that most of the words and sentences in a document are consistent with the sentiments of the labels marked by the author. The label embedding mechanism can pay more attention to the content that is consistent with the sentiments of the labels during the document's hierarchical representation. Experiments showed that our method is more effective than other advanced methods on the established dataset.
Quest for pedagogical technology and its use in education systems

In connection with the modernization of the education system, the role of using information technologies and filling the educational information environment (EIE) with them has greatly increased. One of the meta-disciplinary results of the main educational programm of basic general education is “formation and development of competence in the use of information and communication technologies”. Since this requirement is meta-subject, that is, according to inter-subject concepts and universal educational actions, it means that this requirement applies to all subjects.
Wheeled Robot Control with Hand Gesture based on Image Processing

Computer vision based on shape recognition has a lot of potential in human and computer interaction. Hand gestures can be used as symbols of human interaction with computers which are preferred in the use of various hand gestures in sign language. Various tasks can be used to set remote control functions, control robots, and so on. The process of processing images or hand drawings using computer vision is called image processing. In this paper, a wheeled robot control system can be moved according to the given hand gesture commands. There are 6 forms of hand gestures that are made as input, and each hand gesture gives one command for the movement of a wheeled robot. The method used to classify each hand gesture, namely Convolutional Neural Network (CNN). CNN is a branch of the Artificial Neural Network (ANN) that can perform extraction features and create desired categories. The results of the classification will be carried out and sent to a wireless robot to run a movement. The result of this system is the movement of the wheeled robot following the given hand gestures. Variables that affect this system are training parameters and environmental parameters which include the amount of light intensity, distance, and tilt angle. The accuracy of the entire system obtained is 91.33%.
Change-Encryption: Encryption Using Spatiotemporal Information as a Function Model

Change-Encryption is a distributed layer-by-layer symmetric encryption technology combined with blockchain technology and cryptography. The approach relies on the identity information of all the nodes in the blockchain, and the time, space, and other attributes are used as encryption parameters to enable repeated encryption. In this work, the identity information of the data sender and receiver is considered as the key, and the spatial and identity information values of the node are considered as dynamic encryption parameters to increase the uncertainty of decryption, thereby solving the data security problem of the blockchain technology during communication. In addition, the self-identification principle of the data receiver, reduction principle of the data sender, and two unique countermeasures employed in the Change-Encryption are introduced in detail. The findings demonstrate the value of the proposed approach.
Research on Text Classification Method of Distribution Network Equipment Fault based on Deep Learning

With the development of science and technology, the intelligent distribution network is making rapid progress and the demand for power supply is also increasing. The classification and analysis of the power outage causes in distribution network is helpful to the prediction of power failure. There is a large gap in the proportion of categories of the sample data of power grid outage causes, and serious category imbalance will lead to the deviation of classification results. In order to classify the original data more reasonably, the sample value added technology based on generative adversary network is used to expand the category data with less data in this paper. In order to realize automatic classification of power grid outage causes, the method of text data preprocessing and the text classification model based on deep learning are studied. Finally, the experimental results show the influence of sample capacity and classification accuracy.
Algorithmic Counting of Zero-Dimensional Finite Topological Spaces With Respect to the Covering Dimension

Taking the covering dimension dim as notion for the dimension of a topological space, we first specify the number zdimT0(n) of zero-dimensional T0-spaces on {1,…,n} and the number zdim(n) of zero-dimensional arbitrary topological spaces on {1,…,n} by means of two mappings po and P that yield the number po(n) of partial orders on {1,…,n} and the set P(n) of partitions of {1,…,n}, respectively. Algorithms for both mappings exist. Assuming one for po to be at hand, we use our specification of zdimT0(n) and modify one for P in such a way that it computes zdimT0(n) instead of P(n). The specification of zdim(n) then allows to compute this number from zdimT0(1) to zdimT0(n) and the Stirling numbers of the second kind S(n, 1) to S(n, n). The resulting algorithms have been implemented in C and we also present results of practical experiments with them. To considerably reduce the running times for computing zdimT0(n), we also describe a backtracking approach and its parallel implementation in C using the OpenMP library.
A Method for Online Interpolation of Packet-Loss Blocks in Streaming Video

In this paper we examine and apply a linear-time matrix transformation for online interpolation of missing data blocks in frames of a streaming video sequence. We show that the resulting algorithm produces interpolated pixels that are sufficiently consistent within the context of a single frame that the missing block/tile is typically unnoticed by a viewer of the video sequence. Given the strenuous time constraints imposed by streaming video, this is essentially the only standard of performance that can be applied.
A Low-Dimensional Representation Learning Method for Text Classification and Clustering

Natural language processing applications often suffer the curse of dimensionality. In this paper, we propose a low-dimensional text representation learning algorithm, which preserves the pairwise similarity relations of texts. Our method maximizes the log-probability of observing similar texts conditioned on its feature representation. To generate enough similar text pairs for training the objective function, we first build an adjacency graph based on the pairwise similarity relations of the texts, and then propose a simulated sampling strategy to generate the co-occurrence text sequences from the adjacency graph. Experiments on four long and short text datasets demonstrate that our method outperforms several state-of-the-art dimensionality reduction methods. Our method is also better than Doc2vec except on the 20 Newsgroups” dataset for text clustering. Our method can also be applied to the representation learning of images rather than specified in texts.
Analysis of differential distribution of lightweight block cipher based on parallel processing on GPU

As the fast development of IoT technology, various security solutions have to be considered when the corresponding solutions are being deployed. Due to the lightweight nature of the IoT devices such as the RFID tags and so on, traditional encryption schemes such as AES which are relatively heavy in the sense of operations cannot be applied here. Lightweight block ciphers have since become a default standard when considering security protections on such lightweight IoT devices. Compared with the security analysis approaches by taking advantage of the differential or linear cryptanalysis, the security margin of lightweight block ciphers can be further derived more accurately due to the small internal state. In this paper, we investigate the security margin of the lightweight block cipher structure especially the SPN design by taking advantage of the parallel computing power of modern GPU architecture. We show how to accelerate the computing of the statistical distinguisher, which is the crucial point for analyzing the security of the cipher design. Our proposed methods gain notable advantage against traditional CPU architecture in terms of time complexity and possess extensibility for other block ciphers.