Reviewerly/exHarmony huggingface dataset:
A Word-length Optimized Parallel Framework for Accelerating Option Pricing Model

We present an efficient hardware structure for accelerating option pricing, using the Black-Schole model. Within the structure, a Multiplier Array is devised to make the pricing process fully-pipelined, and an efficient WELL19937 structure is adopted as the basic uniform random number generator, which is proven to have excellent equidistribution, very long period, and is also easy to be parallelized. Moreover, to maximize the performance/complexity efficiency, a word-length optimization method is used to determine the optimal integer and fractional word-length for signals in the design. We also develop a framework to parallelize the option pricing process, based on the Fast Jump Ahead technique. Experimental results show that our parallel framework has good scalability and is capable of achieving performance speedup roughly linearly with the number of parallel simulation cores, while retain exactly the same calculation results. Meanwhile, our framework is superior to other existing architectures reported in the literatures in terms of throughput rate, resource usage as well as the period and quality of the basic uniform random number generator. Furthermore, we implement the framework with 17 parallel simulation core targeting Xilinx xc5vlx330t FPGA device. Compared with implementations on CPU and GPU with similar chip manufacturing process, the throughput is about 365 and 2.6 times faster, while the throughput-power efficiency achieves 3293 and 59.4 time speedup, respectively.
Feature Engineering Framework based on Secure Multi-Party Computation in Federated Learning

Data and features often determine the upper limit of results, so that feature engineering is an important stage of federated learning. The existing research schemes all carry out feature engineering based on publicly sharing data. One is plaintext data sharing, the other is ciphertext data sharing, but both types of sharing bring security and efficiency problems. To address these challenges, we propose a feature engineering framework based on Secure Multi-party Computation, which supports multi-party participation in feature engineering and confines feature data locally to ensure data security. Moreover, the computational efficiency of the core algorithm of the framework is also improved compared with the existing methods.
Convolutional Capsule Network Based in-Loop Filter for HEVC

Recently, several convolutional neural network (CNN)-based in-loop filtering algorithms are proposed to improve the high efficiency video coding (HEVC). However, regular CNN-based filters can only apply a single model to the whole image, but a single model usually cannot adapt well to all local features in the image. To solve this problem, we propose an inloop filtering algorithm based on convolutional capsule network (CC-net), and adapt it into the HEVC hybrid video coding framework as a new in-loop filter. This article is the first to apply capsule to the filtering work of video encoding, which is proposed to use localized dynamic routing algorithm to improve the self-adaptability of the filter to different local features in the image, and we integrate the model into HEVC encoding loop. Experimentally, our proposed method brings average 7.9%, 5.4% and 4.1% BD-BR reductions under all intra, random access and low-delay P configurations, respectively, as well as, 0.4dB, 0.2dB and 0.2dB BD-PSNR gains respectively.
A Comparative Study of Various Lossless Compression Techniques of Steganography and Cryptography

Data transaction is increasing day by day over internet which is resulting into a great challenge of their security. Data security had been a great matter of concern for which different technique like steganography, cryptography came into picture. These techniques are used to convert the data into unreadable format so that it can be protected from any kind of attack. However, the attackers are also updating themselves. Thus, the combination of various compression techniques has been implemented for higher security. In this paper, a study of various stenographic compression technique and combination of steganography and cryptography methods are reviewed and discussed in detail. The result of the analysis of various ideas helps in proposing a more complex and secure method. We come to a solution that, the combination of Huffman compression technique along with other compression technique and Cryptography is best suited for higher secrecy of data and less distortion in stegno image. Elliptical curve cryptography or public-key cryptography is also proposed for the better encryption of data and secrecy of key as well.
A Comparative Framework for Blocking Artifacts Removal of compressed Images using Fuzzy Logic

Restoration of an image with blocking artifacts caused by low-bit-rate compression is a difficult task, and blocking artifact assessment techniques play an essential role in the computer vision area. An artifacts removal approach is a critical step in improving the image processing area&#x0027;s dependability and security, allowing for improved understanding in a variety of applications such as pattern recognition, object categorization, surveillance systems. Removal of artifacts is a processing technique that is utilized to give improved picture Quality, and several artifacts removal procedures have previously been used by researchers in the image processing era for this goal. However, when a collection of artifacts is present in an image such as line artifacts, motion artifacts etc. they do not yield acceptable outcomes. We have suggested a comparative methodology for removing line and motion artifacts from digital images utilizing fuzzy logic in this study. This study&#x0027;s key contribution is the creation of a novel fuzzy logic-based hybrid artifacts removal system that improves blocking artifacts efficiency. The suggested framework has its own influence on quality parameters to remove artifact from a picture.
Blockchain as a Reliable Security Framework

Over the past several years, Bitcoin (the pioneer of Blockchain) has harnessed the cutting edge Blockchain software technology to securely decentralize financial transactions so control can be passed democratically to anyone who wishes to participate in the peer-to-peer network. It does this entire task with a mere shared chain of transaction history utilizing blocks of 5-tuples (transaction ID, transaction fee, transaction size, sender(s) private key(s) and amount(s), recipient(s) public key(s) and amount(s)) that get added to the distributed ledger across all nodes in the peer-to-peer network around once every ten minutes. With Bitcoin&#x2019;s market capitalization at nearly a billion dollars, and many rising competitors taking advantage of Blockchain for financial transactions, it is not hard to see that this new way to transcribe digital information is sweeping the financial world at a blistering rate. For reasons explained further in this paper, we will explore why Blockchain financial ledgers tremendously increase security, transparency and privacy in comparison to traditional database financial ledgers.
DESIGN OF ADDER AND SUBTRACTOR HAVING LESS POWER DISSIPATION

With the advancement in technology in the field of digital electronics, reversible logical has become a powerful tool in wide variety of areas like in designing of low power VLSI circuits, DNA computing, quantum computing and optical computing. Circuits made using reversible logic are more energy efficient and consumes less power. In these modern times we need designs of circuits that are speed efficient and should also consume less power. This paper emphasizes on presenting the improved reversible combinational circuit of eight – bit parallel adder and subtractor. These circuits are designed and implemented using Feynman, Double Feynman and MUX gates. Three designs of reversible eight – bit parallel adder and subtractor is proposed. Performance of all three designs is analyzed based number of reversible gates required, garbage input and output, quantum cost.
Research on the status of industrial sewage equipment based on clustering ensemble

The collected industrial data are usually unlabeled, and generally use clustering algorithm for data analysis. However, a single clustering algorithm can not guarantee a good analysis of industrial datasets, and there are many limitations for users. By fusing multiple results obtained by the analysis, clustering ensemble can get more effective results. Therefore, we use density-based selective clustering ensemble method to analysis the collected datasets. The main steps are as follows: (1) use three density based clustering algorithms to analyze the datasets; (2) in order to remove the negative influence of noise, zero the elements in the obtained co-association matrix multiple times, and obtain corresponding results by hierarchical clustering; (3) set the internal validity index to select the best result. Through the experiment and the feedback of relevant factories, the effectiveness of the experiment is proved.
Personalized English Lexical Simplification for Chinese

Current English lexical simplification (LS) methods adopt one simplification scheme for all users, regardless of individual users&#x0027; language proficiency. To solve the problem, we propose a personalized English LS method for Chinese based on their English certification. Specifically, our method only simplifies these words that need to be simplified and utilizes both pertained language modeling BERT and Thesaurus to generate the best substitutes. To our knowledge, this is the first work that focuses on personalized English LS for Chinese. Experimental results show that our method obtains the most suitable simplification compared with the baselines.
LogicGAN–based Data Augmentation Approach to Improve Adversarial Attack DNN Classifiers

This paper presents an innovative algorithmic approach in order to improve adversarial attack classifiers, based on data augmented by minor modifications generated by a logicGAN. Therefore, the paper addresses a particular type of mitigation against adversarial attacks, which consists of training the "attacked" classifier with initial and adversarial data already known by the defender. Accordingly, we propose an algorithm that improves the training of the classifier: (1) by generating complementary adversarial data which instead of coming from the known adversarial attack, comes directly from minor modifications resulting from the already known adversarial data, and (2) by generating these minor modifications using a specific kind of generative adversarial network named logicGAN. By using an xAI system, this derivative of GAN has the particularity of yielding more substantial corrective feedback from the discriminator to the generator and, thereby, making the mitigation of adversarial attacks faster.
Neural Network Optimization with Biologically Inspired Low-Dimensional Manifold Learning

Neural Networks learn to recognize and leverage patterns in data. In most cases, while data is represented in a high-dimensional space, the patterns within the data exist along a manifold in a small subset of those dimensions. In this paper, we show that by using a biologically inspired algorithm called Geometric Multi-Resolution Analysis (GMRA), these low-dimensional manifolds can be computed and can be used to convert datasets into more useful forms for learning. We also show that, thanks to the lower-dimensional representation of the converted datasets, that smaller networks can achieve state-of-the-art performance while using significantly fewer parameters.
A Fine-grained volley gesture recognition method with Direction Independence

The development of wireless sensing technology makes it possible to control various IoT devices with gestures. In the current severe international epidemic situation, the use of non-contact gesture recognition methods can greatly reduce direct or indirect contact between people and effectively curb the spread of the virus. However, since the direction, position, and motion range of each person&#x0027;s gesture are different, it brings great challenges to the accurate recognition of gestures. To solve this problem, this paper proposes a direction-independent fine-grained volley gesture recognition method CSI-VGR based on Wi-Fi signal sensing technology. CSI-VGR first converts the CSI signal of a gesture into a Doppler shift. In particular, CSI-VGR uses a temporal convolutional network (TCN) to segment the gesture action and extract the features of each gesture. Finally, the Echo State Network (ESN) is introduced, and the gesture feature is input to obtain the classification result of the gesture. This paper verifies the performance of CSI-VGR in multiple dimensions in two real scenarios. Experimental results show that the gesture recognition accuracy of the CSI-VGR method can reach 96.3&#x0025;, and it shows satisfactory robustness in both scenarios.
BOFSanitizer: Efficient locator and detector for buffer overflow vulnerability

Buffer overflow is one of the most serious and common vulnerability that using memory beyond the allocated memory. It can cause many severe threats, including system crash, arbitrary code execution and data corruption. Buffer overflow vulnerability has two main characteristics, one is that buffer overflow vulnerability may not be discovered after its path has been executed. The other is that not every buffer overflow vulnerability could cause crash. Therefore, existing program analysis tools have more or less deficiencies in detecting buffer overflow vulnerability. This paper presents a BOF-Sanitizer method for buffer overflow vulnerability detection. We proposed a pre-process method that combine vulnerability metric and rank, which can effectively avoid the exploration of unnecessary paths and quickly locate potential bug positions. We present a custom memory model of concolic execution and a boundary detection engine based on a low fat pointer, which not only ensure that the executed path is safe, but also effectively reduces the system performance overhead through the real time interacting with the analysis engine and solver in the process of dynamic detection. We implement a prototype system of BOFSanitizer to detect buffer overflow in different application, the result show that BOFSanitizer accurately discovers 88&#x0025; buffer overflow target points and the detection rate improve over 50&#x0025; with compare to simple combination of existing program analysis tools.
Applying of Graph Neural Network in Relationship Prediction in Knowledge Graph Reasoning

In the existing knowledge graph reasoning method, a large number of the use of embedded knowledge reasoning method, this method of reasoning can through knowledge graph the entities and relationships in vectorization, the node information in the knowledge graph vectorization and relational information in vector space after projection is a common knowledge graph embed mode (TansE). The inference method based on embedded knowledge graph is also based on the learning characteristics of neural network to carry out link prediction inference. However, all algorithms based on neural network have the problem of inexplicable principle, and in the information acquisition of knowledge graph, due to the existence of convolution algorithm, the information can only be obtained mechanically or by obtaining the information of the entity data of receptive field corresponding to the convolution centre. Due to the particularity of the knowledge graph, the complex structural relationship between the target and the surrounding entities will lose information in the convolution operation, so there is a problem that only fixed range information and characteristic information will be lost. We propose to solve this problem by using graph neural network, and the evaluation indexes MRR and N of the improved algorithm on NELL-995 dataset are increased by 1.1&#x0025;, 2.9&#x0025; and 2.8&#x0025; respectively.
Sustainable and digital education during and beyond the Covid-19 crisis.

Abstract The pandemic crisis has coincided with a time of global economic crisis. The education system has suffered from this double impact: the simultaneous closure of schools and universities and a significant worsening of the business and productivity system. Digitalisation in the education process started in the far past, but it has become a priority with the pandemic. The use of digital tools in the educational process ensured that the latter did not come to a complete standstill during the pandemic. Although digitalisation is seen as a possible solution to the problems facing schools, it can also be seen as a factor in widening the gap between rich and developing countries. The article proposes a reflection about education during the pandemic and the digitalisation process of schools in all its facets and at alle education level.
Recognize Illegal Transactions in the Bitcoin Network Using Graph Attention with DIKW

Bitcoin has become the choice of many illegal transactions due to its anonymity. To fight crime and maintain the order of the financial market, it is necessary to identify illegal transaction activities in the Bitcoin network. On the basis of referring to the relationship between data, information, and knowledge in the DIKW architecture, this article proposes a new method called MP-GAT to convert discrete data in the Bitcoin network into usable information and knowledge to help identify illegal transactions. MP-GAT uses a combination of multi-layer perceptron and graph attention networks to build a model for identifying illegal transactions. Specifically, the concept of artificial intelligence is used to simulate the human reasoning system and learn from the data. Use the MP-GAT model to convert Bitcoin data into information and knowledge. The method of artificial intelligence is used to complete the transformation of data, information, and knowledge in the DIKW system to achieve the purpose of detecting and identifying illegal transactions in the Bitcoin network.
Enhancing the Performance of Liquid State Machine with Time-Division Sampling and Reservoir Reconstructing

Remarkable progress has been made over the past few years on the performance of Spiking Neural Network (SNN), which is mostly driven by its biological features and used for pattern classification. However, the existing works on SNNs are challenged by high network complexity and training cost. As motivated by neuromorphic computation and certain features of human brains, Liquid State Machine (LSM) is put forward as a particular form of SNNs. LSMs are characterized by its short-term memory and lightweight computing, which is mostly contributed by its recurrently and randomly connected structure. In this paper, we proposed an improved LSM model on speech recognition using two promising techniques: time-division sampling and reservoir reconstruction. First, we proposed that dividing the output state from the liquid layer through multiple time windows, this approach is proven to explore more detailed input signal information. Second, the reconstructed structure of LSMs is investigated to extend the classification ability of LSMs, and two kinds of topologies of LSMs are served on the basis of parallel assembled liquids and a hub topology. FSDD and N-TIDIGITS datasets are used to demonstrate the performance improvement of proposed methods. Specifically, the classification accuracy of FSDD and N-TIDIGITS are 92&#x0025;, 87&#x0025;, respectively.
Deep User Interest Mining Network Based on Attention Mechanism and Deep Learning

Click-through rate prediction plays an important role in recommendation systems and online advertising. Most CTR prediction models focus more on innovative research on feature interaction, but ignore the mining and application of user behavior sequences. There are many shortcomings and shortcomings in the in-depth mining of user interests, especially in the use of the user&#x0027;s historical behavior sequence characteristics, which can still be optimized and improved. In this article, we propose a new model: User Interest Mining Network (UIMN). This model innovatively designs the User Behavior Interest Network (UBIN), which is used to deeply mine user interests, and at the same time innovatively introduce User-Item Relational Network (UIRN). The model processed the user&#x0027;s behavior sequence in long and short term, and considered the influence of the target item on the sequence, and finally got the updated behavior sequence. In addition, the model introduces the relationship between users and target item, and combines DNN and LSTM networks to improve the accuracy of estimation. UIMN con-ducted experiments on Amazon commodity dataset and MovieLens dataset, and the AUC value of the model increased by 1.6&#x0025; to 2.4&#x0025; compared with the benchmark model, which proved the effectiveness of the model.
Review-Based Hierarchical Attention Model Trained with Random Back-Transfer for Cross-Domain Recommendation

Cross-domain recommendation aims to leverage the rich interaction information in the source domain to predict interactions between cold-start users and items in the target domain. Since reviews contain users' preferences and items' attributes, many review-based cross-domain recommendation methods are proposed. However, existing methods cannot either 1) select important words and reviews from multiple reviews of users/items, or 2) learn a unified representation space for different domains without enough overlapping users. To address these problems, we propose a Hierarchical Attention model trained with Random Back-Transfer for cross-domain recommendation (HARBT). Specifically, the hierarchical attention extracts text information related to a given user or item which leads to an accurate interaction prediction. The random back-transfer works as a data augmentation algorithm to utilize data of users and items which are in the same domain for better matching of representations in different domains. Extensive experiments on real-world datasets show that our approach outperforms state-of-the-art methods significantly.
Stacked Ensemble Deep Learning Technique to Detect Malaria Parasite in Blood Smear

Malaria Parasites are transferred from infected female mosquitos to humans which can lead to the death of the person. Malaria affects the majority of people each year, and most of these cases arise in remote areas. There has been lots of research in the field of Malaria Parasite detection using the automated technique, but these techniques require high computational power, and in remote areas, the availability of such systems is very unlikely. The proposed Ensemble Model can detect the presence of Malaria Parasite in the thick blood smear by taking the average of the output layers of ResNet50 and the custom CNN model. The models&#x0027; performance has been evaluated and results reveal that it achieved 0.97 Specificity, 0.98 Sensitivity, 0.97 Precision, and 0.972 Accuracy with an image size of 64x64x3. The overall file size of the model is under 15Mb that also makes it portable.