Reviewerly/exHarmony huggingface dataset:
An Empirical Study of Two Bitcoin Artifacts Through Deep Learning

Human artifacts like technical papers and computer programs often carry the individual styles of their creators. If retrieved properly, such style information from the artifacts can be used to categorize the artifacts, compare the relative “similarities” among artifacts, and may even be used for tracing the authorship of a new artifact. Bitcoin is a peer-to-peer cryptocurrency and its author(s) goes/go by the pseudonym of Satoshi Nakamoto. In this article, we use deep learning to study the styles of two Bitcoin artifacts: the first version of Bitcoin’s source code, v0.1.0, which was released in early 2009, and the original Bitcoin white paper, which is dated Oct. 2008. Both studies use the deep learning technique, which first utilizes extensive computing power to generate a neural network model from labelled training data and then uses the model to predict the authorship of unknown data. For the Bitcoin source code artifact, the data set is a set of cryptography software that were built around 2008/2009 and it has 16 known labels. Our model achieves $$89.1\%$$ validation accuracy and our prediction results show that the Bitcoin source code is likely produced by multiple authors and Hal Finney is not one of them. For the Bitcoin white paper, we compiled a second data set of financial cryptography papers that are in the same knowledge domain. This data set has 436 known labels. Our model achieves $$55.1\%$$ validation accuracy and it has identified four technical papers that are “similar” to the Bitcoin white paper.
Contingent payments from two-party signing and verification for abelian groups

The fair exchange problem has faced for a long time the bottleneck of a required trusted third party. The recent development of blockchains introduces a new type of party to this problem, whose trustworthiness relies on a public ledger and distributed computation. The challenge in this setting is to reconcile the minimalistic and public nature of blockchains with elaborate fair exchange requirements, from functionality to privacy. Zero-knowledge contingent payments (ZKCP) are a class of protocols that are promising in this direction, allowing the fair exchange of data for payment. We propose a new ZKCP protocol that, when compared to others, requires less computation from the blockchain and less interaction between parties. The protocol is based on two-party (weak) adaptor signatures, which we show how to instantiate from state of the art multiparty signing protocols. We improve the symbolic definition of ZKCP security and, for automated verification with Tamarin, we propose a general security reduction from the theory of abelian groups to the theory of exclusive or.
Active Jamming Signal Recognition based on Residual Neural Network

The problem how to effectively identify the type of active jamming signal has important practical significance for the accurate perception of radar anti-jamming system. Therefore, a radar active jamming identification method based on fractional Fourier transform and residual neural network is proposed. Before the jamming signal pattern recognition, the time-frequency structure model of the signal is established, and the influence factors such as radar technical system and jamming signal processing cycle are comprehensively considered. The constraints of the time-frequency analysis kernel function and processing on the jamming signal type, the cross term of the composite modulation signal and the effectiveness of the distorted signal characteristics are analyzed. Then, according to the requirements of availability and recognition rate of subsequent signal classifiers, the recognition model based on residual neural network (RESNET) is used to solve the problem. The simulation results show that the recognition effect of multiple active jamming patterns under different interference to signal ratio is higher than 90%, which verifies the effectiveness and rationality of the method.
Virtual Reality Technology based Simulation Training System Research of Pan Business English Activities

Business English proficiency makes great demand on an authentic and information-based environment, especially for non-native English speakers. Therefore, this study was conducted to develop a simulation training system based on virtual reality technology for Business English learners. Virtual Reality technology, characterized by immersion, interaction, inspiration, pertinence, dominance, and openness, can provide a comprehensive and superior training scene for practicing various Business English activities. Hence, this study explores the construction and operation of a virtual reality simulation training system for Pan Business English activities and the application of virtual reality technology in Pan Business English activities. To conclude, although virtual reality technology exploitation is still in the primary stage, the system can significantly improve the learning interest and effect of Business English practitioners.
Adapted PBFT Consensus Protocol for Sharded Blockchain

As the foundation of a blockchain, consensus algorithm significantly affects the blockchain system’s performance. To a consortium blockchain, Practical Byzantine Fault Tolerance (PBFT) has been widely believed as a good candidate consensus due to its many advantages. However, PBFT is not particularly designed for a consortium blockchain. Thus, there is still a large improvement space to implement the PBFT algorithm in a sharded blockchain. Based on network sharding, we aim to address the problems incurred by the traditional PBFT algorithm. Because when there are large number of nodes in a P2P network, PBFT can lead to a significant performance degradation. Even worse, Byzantine nodes cannot be found timely in a large-scale blockchain network where the PBFT algorithm is adopted. In this paper, we propose an adapted version of BFT consensus for the sharded blockchain. The proposed cross-shard BFT consensus mainly consists of a two-phase consensus mechanism after performing network sharding. In the first phase, Raft consensus is first adopted within each shard, in which a leader is elected. In the second phase, those leaders from all shards form a committee and perform a committee-wise PBFT consensus. Through introducing anchor nodes within each shard, the security of the proposed two-phase consensus is guaranteed. We analyze the security of the cross-shard BFT consensus based on a committee-wise monitoring framework. Through simulations, we find out that the proposed cross-shard BFT consensus yields a higher throughput, lower latency than the original PBFT. The fault-tolerance ability of the proposed consensus is around 1.5 $$\times $$ to 2 $$\times $$ of PBFT.
An Underwater Saliency Detection Method Based on Grayscale Image Information Fusion

The complex underwater environment and poor underwater image quality lead to existing saliency detection results with unclear edges and uneven display of internal structures. This seriously affects the quality of underwater image saliency detection. In order to solve the above problems, this paper proposes an underwater saliency detection method based on grayscale image information fusion. Firstly, the interactive dual-stream encoder is constructed to extract the feature information on the grayscale images and RGB images respectively. Secondly, a multi-level fusion strategy is used to obtain salient features to satisfy the encoding and decoding stages to be able to fully utilize the image features. Finally, the mixed distance loss function is proposed to keep the prediction results pixel-wise and overall-wise accuracy. The experimental results show that: The results on 6 public datasets show that the proposed method generates sharper edges and more complete interiors of salient objects.
A Conceptual Model for Digitalising Bette-Bendi Oral Traditions

In the wake of colonisation, Africans generally replaced uncritically and almost wholesale their own pre-colonial indigenous values and cultures with those of their western colonial masters. As a result, there has been progressive deracination which has resulted in the creation of the modern African youth who is detached quite disdainfully from the enviable values that nurtured and nourished a morally healthy pre-colonial African world. Blessedly, these social values and mores were transmitted from generation to generation largely through the agency of the people’s oral heritage, remnants of which can still be recovered from elders and other knowledgeable persons in African communities. This Chapter, hence, re-echoes the urgent mission for documenting aspects of our oral heritage and transmitting same through modern tools and techniques, for wide consumption by our African youths. A conceptual model is proposed to encourage community-government/industry-and-university partnership, and domesticate the curation of community collections; transforming them through technology into enhanced digital collections for efficient learning and dissemination of oral traditions. The choice of digitalisation can be best understood in the context of the statement in Chinua Achebe’s Things Fall Apart, where nneke the bird is said to have reasoned that since hunters have learnt to shoot without missing, he too has learnt to fly without perching. In our situation, since the African youth of today largely yoked to all manner of social media, it is only strategic to encapsulate such value-loaded aspects of oral heritage in the form of avatars (cartoons, animations) and other categories of social media for the wide consumption of our youth. This is one way the present western content of these media can be supplanted by the typical African ones.
An Improved A* Algorithm for Roadless Networks Based on Triangular Mesh

In the path search problem under the roadless environmen such as the wild or remote areas, the existing path planning algorithms still have shortcomings such as slow speed and long planning path; The A* algorithm is efficient and optimal when dealing with node search problems, so it is widely used, but if you want to use the A* algorithm in a roadless environment, you need to divide the environment into a series of square grids or triangular grids. Compared with square grids, triangular grids can better adapt to various terrains and make full use of obstacle data; therefore, this paper proposes an improved A* algorithm based on triangular grids (referred to as TIA* algorithm), hope it can solve the dilemma of the current roadless algorithm. The experimental results show that the TIA* algorithm can realize the path planning under the roadless environmen, and compared with the traditional square grid-based A* algorithm and the RRT algorithm, it can shorten the planning time and the length of path under the premise of ensuring a high success rate.
FPGA implementation of canny edge detection algorithm for X-ray images

In the domain of medical imaging, X-ray imaging is the most frequent and commonly used technique for diagnosing diseases of various types. So, quick and exact diagnosis can be critical to the success of any given treatment. For this purpose a hardware accelerator for Canny edge detection algorithm is presented in this work with its implementation on Spartan (3E-XC3S1600) kit and programmed using VHDL. To simplify the hardware implementation some assumptions are made on the traditional algorithm like the intermediate values of Canny operator are calculated with integer values instead of floating point or fixed point values so that the available resources of (Spartan 3E-XC3S1600) platform are sufficient for this purpose in terms of the use of BLOCK RAMs. Also a modified 18-bit VGA port is built for the purpose of displaying the original and result images clearly and accurately instead of the original 3-bit VGA port available on the used Spartan3E kits. A speedup of about were achieved with the proposed design over the software implementation using Mat-Lab on the art personal computer with a maximum area occupation 100%. Although Canny operator produces best results when compared with other edge detection operators because Canny yields thin lines for its edges by using non-maximal suppression, but this is influence on the silicon area occupation.
Personalized Hashtag Recommendation with User-level Meta-learning

Personalized hashtag recommendation aims to au-tomatically recommend user-specific hashtags to annotate the posts (e.g., tweets). It is actually an unwieldy procedure because only a small number of posts contain hashtags, which can hinder the quality of recommendation results. Moreover, users always have their own preferences while choosing the hashtag to describe the post contents. Thus, solving label scarcity and user bias both have raised researchers' attention toward hashtag recommendation. The existing methods can be generally divided into two groups: The first group models the relationship between hashtags and posts taking only post contents into account, without considering the user preferences. The second group models the relationship between users and posts assuming that each user has adequate posts tapped with hashtags. In this paper, we propose a Meta-learning based Personalized Hashtag Recommendation (MetaTag) framework to address above mentioned challenges at once. Our method can leverage the prior experience learned from other users and quickly adapt to a new user. In this framework, model is trained in an episodic manner with user-specific historical posts to learn semantic embeddings that can distinguish different hashtags even they have not been seen before. The results of experiments on the data collected from Twitter demonstrated that our method outperforms state-of-the-art approaches by a large margin.
Improved Compression of Artificial Neural Networks through Curvature-Aware Training

Artificial neural networks achieve state-of-the-art performance in many branches of engineering. As such they are used for all kinds of tasks and nowadays are desired to be used on mobile devices like smartphones. Due to limited hardware resources or limited channel capacity on mobile devices, compression of neural network models to reduce storage or transmission costs is desired. Furthermore, reduced complexity is of interest. This work investigates introducing the curvature of the loss surface in the training of artificial neural networks and analyzes its benefit for the compression of neural networks through quantization and pruning of its weights. As proof-of-concept, three small LeNet-based neural networks were trained using a novel loss function consisting of a weighted average of the cross-entropy loss and the Frobenius norm of the hessian matrix. That way both, the loss as well as the local curvature, are minimized concurrently. Using the proposed method, mean test accuracies on the MNIST and FashionMNIST datasets after quantization were considerably improved by up to about 47.6 % for 1 bit quantization on MNIST and about 27.8 % on FashionMNIST compared to quantization after training without curvature information. Additionally, pruning was found to benefit from introducing curvature in the training as well with an increase of up to about 14.6 % mean test accuracy compared to pruning after training without curvature except for isolated cases. Training the artificial neural networks first without curvature information and subsequent training by only one epoch using curvature information allowed to increase the mean test accuracy after quantization at 1 bit by about 16 %. The proposed method can potentially improve the accuracy after compression irrespective of the compression method applied.
Designing Parents-Adolescents Relationship in Future Mobility in China

Following the rapid growth of private vehicles in China, in-car parents-adolescents communication has become a prominent issue in recent years. Due to the physical also mental challenges of the adolescent period, the existing in-car environment has demonstrated evidenced limitations to support parents-adolescents communications during car rides. Existing solutions are mostly games and virtual agents designed for young children, which focus on entertainment. However, no related study was found in the Chinese context. Thus, how to design novel experiences supporting in-car communications between parents with adolescents in China is still one of the main challenges in the field. Building upon a fundamental understanding of the current parents-adolescents communication situation in China, this study aims to explore how the vehicle’s interior space could help promote positive interactions and improve communication challenges. We conducted the survey studies in semi-structured interviews and a cultural probing study with five Chinese families, as well as a storytelling workshop with college students. Based on the discussion of design implications learned from user research and concept development, the design proposal in this study intends to support both parents and adolescents to be able to express positive and empathetic intentions through embedded in-car interactivity and suggest future research directions.
An Improved Wiener Filter Based on Adaptive SNR MRI Image Denoising Algorithm

Aiming at the disadvantages of traditional Wiener filtering, a new adaptive noise ratio wiener filtering method is proposed in this paper. The method can identify the noise type according to its histogram distribution type, calculate the mean and variance of noise, and construct the corresponding point spread function.At the same time, the image denoising algorithm based on the improved Wiener filter is realized by estimating the adaptive SNR of the image. Especially for the medical images with different background and foreground, the denoising algorithm proposed in this paper has remarkable effect. The experimental results show that the adaptive SNR wiener filter can achieve better results than the traditional wiener filter by combining the main visual effect and objective PSNR value (the larger the PSNR is the better). The algorithm in this paper can directly find the optimal signal-to-noise ratio of wiener filters, which solves the problem that traditional Wiener filters need to estimate the signal-to-noise ratio continuously.
A Triplet Deviation Network Framework: Boosting Weakly-supervised Anomaly Detection By Ensemble Learning

Weakly-supervised anomaly detection refers to extracting the information to identify anomalies from the limited labeled anomalies. Existing work has used the deviation network to learn the anomaly score end-to-end, which treats unlabeled data as normal data, and makes the output score of labeled anomalies deviate greatly from the normal data. However, due to the diversity of anomaly types, the model trained by limited labeled anomaly is one-sided, and can not be generalized to identify other types of anomalies. Simply treating unlabeled data as normal data can not extract features from unlabeled data to improve the generalization and accuracy of the model. In this paper, a triplet deviation network framework(TDNF) is proposed. Compared with the original deviation network, it adds a potential anomalies filtering module and a prior anomaly score generation module. The potential anomalies filtering module ensemble multiple unsupervised methods to evaluate data and filter potential anomalies. Labeled anomalies, potential anomalies, unlabeled data compose multiple triplets, and input to deviation network to improve the ability of the model to identify different types of anomalies. The prior anomaly score generation module uses one unsupervised method to generate normalized prior anomaly scores. The prior anomaly scores as prior knowledge of deviation network, which help to fine-tune the model's learning of unlabeled data to optimize the model's ability of anomaly ranking for unlabeled data. We give a triplet deviation network instance TDN-IHSC and carry out extensive experiments on multiple real-world datasets. The results show that our method is effective and performs better than the other four advanced competitive methods.
Comparing the Accuracy of Horizon Shade Modelling Based on Digital Surface Models Versus Fisheye Sky Imaging

Fisheye sky imaging is a well established and accu-rate method for estimating shading in photovoltaic (PV) applications, however, it requires physical presence and measurements at the studied location. On the other hand, Digital Surface Models (DSM) can be used to estimate horizon shading remotely, and presents a more scalable method for evaluating potential PV installation site efficiently. Nowadays, high resolution DSM data is readily available in many countries, especially for urban environments. This has opened up new potential applications for more efficient shading estimation for aiding the deployment of PV in urban environments where horizon and nearby shading is significant. In this work, the accuracy of DSM-based methods to characterize horizon shading is assessed and compared to that obtained with fisheye imaging based methods. DSMs with different resolutions are studied and results show that high resolution DSMs are comparable. However, the accuracy of lower resolution DSMs will be highly influenced by the characteristics of the studied location.
Horizontal Correlation Analysis without Precise Location on Schoolbook Polynomial Multiplication of Lattice-based Cryptosystem

Most cryptographic systems are secure in theory; however, the implementation of cryptographic system on embedded devices can be attacked by analyzing the power consumption of specific operation to reveal the key. The classic vertical correlation power analysis (CPA) attack requires a large number of power traces for analysis. Using transient secret-key scheme significantly weakens such an attack as insufficient data could be obtained. On the other hand, the horizontal CPA requires at least a single power trace and can make full use of multiple intermediate values to analyze the correlation of power consumption. In this work, we devised a horizontal CPA attack on schoolbook polynomial multiplication of hardware-implemented lattice-based cryptosystem without precise location. The accuracy of correctly recovering any one sub secret-key using only a single trace is 99.90%, and the accuracy of correctly recovering the secret-key is 76.41%. The powerful attack capability of horizontal CPA exposes the vulnerability of unprotected schoolbook polynomial multiplication against the attack of side-channel analysis (SCA).
Efficiency and Effectiveness of CRM Solutions in Public Sector: A Case Study from a Government Entity in Dubai

The customer relationship management (CRM) system has been adopted by many organizations to get better understanding of their customers’ needs and efficient utilization. The aim is to help organizations draw their strategies and enhance the quality of their products and services. Both public sector and government entities can use CRM systems to understand citizens needs and enhance services provided accordingly. This paper presents a case study measuring the efficiency and effectiveness of CRM systems in public sectors. The case study is conducted through an anonymous survey that is applied to one of the Dubai’s government entities. This survey focuses on eight questions which was answered by users who are using a CRM system in a governmental entity in Dubai. The results showed that 21% of respondents are extremely satisfied with the outcomes of the CRM. Moreover, 67% of the users showed moderate satisfactions, and the remaining respondents tends to be slightly unsatisfied. The in-depth gap analysis suggested that enhancements are essential to improve user’s satisfactions with CRM usage and expected outcomes.