Reviewerly/exHarmony huggingface dataset:
Key Agreement in the Lightning Network Protocol

The Lightning Network is a decentralized bidirectional payment solution using the Bitcoin blockchain. In an earlier paper, we analyzed the secrecy and authenticity properties of the four subprotocols of the network and found that the key agreement protocol does not guarantee authenticity wrt. the responder. In this paper, we continue the analysis of the key agreement protocol using ProVerif and amend the protocol such that authenticity holds.
Research on Key Technologies of Digital Multimedia Passive Forensics

In recent years, the median filtering operation is increasingly used as a commonly used image post-processing method to hide the traces of other tampering operations. The detection of image tampering with median filtering has gradually become an important branch in the field of digital image forensics. However, the existing median filter detector field lacks a high-precision method to detect whether the image has undergone a median filter operation in terms of low-resolution images compressed with low quality factors. This paper combines the characteristics of median filter and proposes a median filter detection method based on convolutional neural network (CNN), which can automatically extract features directly from the image and build a high-precision detector. The first layer of the CNN Network in this paper adopts median filtered residuals (MFRs) of original and median filtered images. Then, the hierarchical representation is learned by alternating convolutional layers and pooling layers, and multiple features are extracted for further classification. At the same time, based on the detection of the median filter algorithm by the convolutional neural network, in order to further increase the accuracy of the algorithm detection, this paper supplements the method of wavelet high-frequency coefficients of the median filtered residual to extract the feature vector. Finally, extensive experiments show that algorithm can obtain a better performance by CNN and wavelet high-frequency coefficients algorithm, which is of importance in practical applications.
Real-time probing of control-flow and data-flow in event logs

Traditional Process Mining offers batch analysis of business processes but does not transpose smoothly into online environments due to specific design constraints. Techniques adapted to support online analysis require peculiar adjustments that inherently restrict their focus to a single task. In this work, we extend the Concept Drift in Event Stream Framework (CDESF) tool to handle multiple attributes simultaneously. Our extension promotes CDESF to analyze both control-flow and data-flow characteristics in online event streams. Experiments used real and synthetic data for concept drift and anomaly detections. Results show that additional perspectives should be considered as they contain valuable information about processes.
Two-Stage Pipelining Implementation of the Secure Hash Algorithm SHA-3 on Virtex-5 and Virtex-6 FPGAs

In recent years, many hardware implementations of the SHA-3 have been proposed to improve throughput, frequency, efficiency, or area. However, the increasing demand for hashing larger data requires other improved implementations of this function in terms of throughput. In this work, two different implementations of the Secure Hash Algorithm SHA-3 are proposed, the designs are based on the pipeline technique. Our main focus was to maximize the throughput of this function, also to analyze the impact of the use of the pipelining technique on this function The proposed designs are coded using VHDL language and implemented on two different devices Virtex 5 and 6 FPGA. The designs reach a maximum Throughput almost double 16.24 Gbps in the 2 stages external pipelining, and 17.01 Gbps in the internal pipelining. In contrast, it’s worth 8.12 Gbps on Virtex 5 for 512 bits output length in the case of one stage. However, this increase in the throughput will negatively affect the Area needed to implement the design.
A Simple and Effective Method to Defend Against Saliency Map Attack

The interpretability of the convolutional neural networks is related to the security and trustworthiness of many real-world applications, and has become a research hotspot in the field of deep learning in recent years. The saliency map based interpretation method is an effective way to understand the relationship between the input and output of the convolutional neural networks. However, the saliency map may be attacked by adversarial samples, resulting in incorrect interpretation effects. To this end, this paper proposes a defense method against the saliency map attack. First, a saliency map attack method based on adversarial samples is introduced, which obtains adversarial samples through optimization to attack the saliency map under the condition that the classification result of the model remains unchanged. Then, in order to defend against this attack method, a simple and effective defense method is proposed. It adds the random perturbation to the adversarial sample to restore the saliency map of it. To verify the effectiveness of the method in this paper, experiments are carried out on the ILSVRC 2012 dataset. The results show that the attack method can effectively attack the interpretation result of the saliency map. At the same time, the defense method can restore the saliency map of the adversarial sample effectively.
A New System to Construct Dense Map with Pyramid Stereo Matching Network and ORB-SLAM2

Building large-scale dense maps with stereo cameras directly is considered to be ineffective. Since neural network has a good effect on restoring image depth, a mapping system combining neural network with SLAM is proposed in this paper. Firstly, the image depth is extracted by PSMNet from the original image, and the accurate depth information is obtained through nonlinear changes. Then, the pose information of the stereo camera is obtained by ORB-SLAM2. Finally, the original image, depth information and pose data are combined to obtain a large-scale dense point cloud map through the mapping module. Experimental results show that the constructed map can reflect the true distribution of the scene structure, and the system performs well in the standard KITTI datasets.
Mislabeled Samples Adjustment Based on Self-paced Learning Framework

Human have ability to detect and correct the mistakes in life events. For instance, it could be easy for a child to find a cat image incorrectly labeled with a dog label. However, supervised learning network directly learns the mapping between features and labels even though some labels are obviously wrong. Label noise surely increases the complexity of model and undermine the model performance. Enlightened by self-paced learning (SPL) framework which can learn samples in the order of complexity, we propose one iteration-based framework called MSASL which can discriminate and correct the possibly mislabeled samples. The approach can not only achieve the data cleansing task, but also ensure the performance of the model.
Skills Named-Entity Recognition for Creating a Skill Inventory of Today’s Workplace

To trace the skills of the employees and to link them to their responsibilities, organizations need support from methods that automatically support such kind of activities. Even more, the COVID-19 pandemic completely transformed organizations’ workplace and culture, so project managers should take care of the skills of their team members in order to successfully realize a project. For this purpose, we have introduced rule- based named-entity recognition methods that are able to extract soft and technical skills using textual data such as job postings, curricula vitae (CVs), shout-outs, performance reviews, etc. The best model using a fusion of several skills-related dictionaries provides an F1 score of 59%, which can be further used as a base for creating an annotated silver corpus consisting of textual data and all skills found there. Further, we demonstrated the application of the developed model to link the required skills for a certain job title. Being able to trace the skills in an automatic way, we can further link them to tasks and actions to fully understand their economic value.
Improve the security of Hyperledger Fabric by dynamically selecting endorsing peers

With the rapid development of blockchain technology, related applications have developed from the fields of finance and digital asset transactions to more industries. Because it can meet the needs of different companies on the blockchain, the permission-based blockchain Hyperledger Fabric has gradually become the most widely used enterprise-level blockchain project. The consensus mechanism of Hyperledger Fabric is different from the commonly used Proof of Work (POW) and Proof of Stake (POS) in public chains. Instead, the consensus is innovatively divided into three processes of endorsement, order and validation, making transaction processing faster. But it brings security issues. In this paper, we focus on the security issues caused by the fixation of endorser during the endorsement process. We propose to dynamically select endorser to realize the hidden and unpredictable identity of endorser, thereby reducing the probability of an attacker successfully controlling the transaction. Analysis and experiments show that, compared with the original consensus mechanism, the optimized consensus mechanism reduces the probability of successfully controlling the transaction by an attacker by nearly half, and has higher security.
What Refactoring Topics Do Developers Discuss? A Large Scale Empirical Study Using Stack Overflow

Due to the growing complexity of software systems, there has been a dramatic increase in research and industry demand on refactoring. Refactoring research nowadays addresses challenges beyond code transformation to include, but not limited to, scheduling the opportune time to carry refactoring, recommending specific refactoring activities, detecting refactoring opportunities and testing the correctness of applied refactorings. Very few studies focused on the challenges that practitioners face when refactoring software systems and what should be the current refactoring research focus from the developers’ perspective. Without such knowledge, tool builders invest in the wrong direction, and researchers miss many opportunities for improving the practice of refactoring. In this paper, we collected data from the popular online Q&A site, Stack Overflow, and analyzed posts to identify what do developers ask about refactoring. We clustered these questions to find the different refactoring related topics using one of the most popular topic modeling algorithms, Latent Dirichlet Allocation (LDA). We found that developers are asking about design patterns, design and user interface refactoring, web services, parallel programming, and mobile apps. We also identified what popular refactoring challenges are the most difficult and the current important topics and questions related to refactoring. Moreover, we discovered gaps between existing research on refactoring and the challenges developers face. To the best of our knowledge, this paper represents the first Stack Overflow study to identify the refactoring topics discussed by developers. Our study can help researchers to focus on practical refactoring problems, practitioners know more about current challenges and build better refactoring tools, and educators revise curriculum to target current needs on refactoring.
Scalable Data Management on Hybrid Memory System for Deep Neural Network Applications

Memory resource is a critical bottleneck for large-scale Deep Neural Network (DNN) applications. Hybrid Memory System (HMS) provides a promising solution to increase memory capacity in an affordable way. However, to release the powerful performance of HMS, data migration plays an important role. A typical DNN application has a couple of execution layers, and each requires distinct data objects. Deploying DNN on HMS imposes enormous challenges on data migration strategy and inspires us to pursue smart solutions. To tackle the data migration problem on HMS for DNN applications, we propose a runtime system for HMS that automatically optimizes scalable data migration and exploits domain knowledge on DNN to decide data migrations between the fast and slow memories in HMS. To achieve a better performance in data migrations for DNN training, we introduce a reference distance and location based data management strategy (ReDL) that treats short-lived and long-lived data objects with Idle and Dynamic migration methods, respectively. Using ReDL, DNN training on HMS with a smaller fast memory size can achieve similar performance to the fast memory-only system. The experimental results demonstrate that with configured the size of fast memory to be 20% of each workload’s peak memory consumption, our work achieves a similar performance (at most 9.6% performance difference) to the fast memory-only system. It further achieves an average of 19% and 11% improvement in data locality against the state-of-the-art solutions.
Speaker Recognition by Combining Features for Myanmar Weather Forecast Domain

Nowadays, speaker recognition has become one of the important application area of digital signal processing. Speech corpus is important in developing the speech processing and the development of the corpus is essential for low-resourced languages. Burmese (Myanmar language) can be recognized as a low-resourced language because of lack of available resources for speech processing research. Speaker Recognition (SReg) is an approach used to automatically recognize a speaker from their speech utterance. The main concept of SReg is to recognize the information of the speaker’s identity. In SReg, various features have been extracted to reflect the characteristics of the speakers. In this paper, an effective multi-feature combination and comparison of performance between the different size of data are proposed. In this work, weather news from Department of Meteorology and Hydrology, Myanmar is collected. The total size of the implemented Burmese speech corpus is over 10 hours and it contained 13 females and 3 males. The dataset is split into training data and testing data in 4:1 ratio. The experimental results on 16 speakers show that the proposed Burmese speaker recognition based on multi-feature combination achieved 99.16% accuracy and high applicability.
Image Encryption-Compression Method Based on Playfair, OTP and DWT for Secure Image Transmission

Digital image cryptography has the greatest priority with the rise and authenticity of the multimodal data. Encryption is known to be a very critical step in the security of multimedia applications. This paper proposes the Playfair, and OTP based image encryption-compression method using integer Haar transform. Initially, the proposed method encrypts the image using integer Haar transform with the One Time Pad (OTP) algorithm and the Playfair encoding. Finally, Inverse Discrete Wavelet Transform (IDWT) is applied to get the ciphered encoded image. The decoding part is driven automatically by inverting the operations of the encoding part. The quality of encrypted-decrypted images is assessed using Histogram Analysis, NPCR, UACI, PSNR, SSIM, GLCM correlation coefficient and Entropy. It is evident from visual inspection of the images and quality assessment parameters that the proposed method works effectively.
Remarks on Feedforward–Feedback Controller Using a Trained Quaternion Neural Network Based on Generalised ℍℝ Calculus and Its Application to Controlling a Robot Manipulator

In this study, a gradient–descent method extended to quaternion numbers, in which the generalised Hamiltonian– Real calculus is used to calculate derivatives of a real function with respect to quaternion variables, is explored. It was used to derive a training algorithm of a quaternion neural network that functions as an adaptive–type controller in control systems applications. A feedforward–feedback controller is designed based on the quaternion neural network. Furthermore, computational experiments on trajectory tracking control of a three–link robot manipulator are conducted to verify the learning capability and the characteristics of the quaternion neural network–based controller. The experimental results showed the quaternion neural network’s feasibility and capability for a control systems application.
Construction of Text Emotion Classification Model Based on Convolutional Neural Network

Text emotion analysis transforms a text sequence of indefinite length into text category which is one of the key research problems in the field of natural language processing. With the wide application of deep learning technology in natural language processing the text emotion analysis model based on deep learning has made a new breakthrough. This paper builds a basic framework of text emotion analysis and describes it from two aspects data preprocessing and design of network structure of revolutionary neural network. Data preprocessing mainly involves word segmentation model and word embedding. Design of network structure of revolutionary network is the basic structure of the revolutionary network (CNN) including input layer convolution layer pool layer full connection layer and output layer. Finally the feasibility of the method is verified by experiments.
Digital Literacy in Language Education

The abrupt shift to online learning and teaching shocked the majority of the language education community who had to become familiar with online tools, resources, and adjust their teaching approaches overnight. Beyond the state of alarm, it is now evident that a nexus between innovation and ICT exists. The pandemic has highlighted the critical role of technology in language education. As part of a larger study, this chapter focuses on language education in Cyprus and examines the issues facing language teachers' digital literacy and competence through the lens of complex and unpredictable adaptive systems. The data displayed and discussed in the chapter contradicts teachers' confidence of their digital literacy and competence of delivering language lessons online and what was applied in practice, and concludes by making specific recommendations at policy making, pedagogical, and research levels.
Secure Access Control to Cloud Resources using Blockchain

With the steady increase in complexity of cloud computing and other technologies, there has been a significant concern for providing privacy and data security. For secure access of cloud resources, protection of sensitive data is essential. The critical data that relates to resource provisioning in cloud environment includes key pairs and IP addresses. The user needs this critical data to access the resources which has been provisioned. In conventional systems, this data is directly provided to the user. This technique results in local storage and opens up a vulnerability of snooping by a malicious intruder. Furthermore, the user needs to repeatedly remember the key pairs and other information of all the created resources. Recently, Blockchain, with its decentralized and immutable features is being explored in the design access control mechanism. In this work, we design blockchain based access control mechanism to access cloud resources. We use OpenStack cloud to provision the resources, store the critical data of resources in Ethereum blockchain and use secure access control to cloud resources. We also carry out the scalability and performance analysis of the blockchain based system using Ethereum platform.
Increasing Accuracy of Recognition Systems using Decoding Algorithms

Handwriting recognition is a part of optical character recognition technology. Its main task is to convert handwritten text into the corresponding digital text. To accomplish this, handwriting recognition systems compare samples to identify different styles of handwriting. Although document recognition tools are quite popular, the ones for the handwritten text recognition are not accurate enough, they require a human to correct errors. This paper describes the process of recognizing lines of handwritten text in English. It also introduces modern approaches to constructing multilayer neural networks, analyzes the advantages and disadvantages of different decoding approaches and their influence on recognition accuracy as well as post-processing of output text. As a result, a neural network was developed, that recognizes lines of English handwriting up to 100 characters long and converts them into printed text.
Analogical Reasoning as an Inference Scheme

Abstract Despite its importance in various fields, analogical reasoning has not yet received a unified formal representation. Our contribution proposes a general scheme of inference that is compatible with different types of logic (deductive, probabilistic, non-monotonic). Firstly, analogical assessment precisely defines the similarity of two objects according to their properties, in a relative rather than absolute way. Secondly, analogical inference transfers a new property from one object to a similar one, thanks to an over-hypothesis linking two sets of properties. The belief strength in the conclusion is then directly related to the belief strength in this meta-hypothesis.
Learning by Making. 3D Printing Guidelines for Teachers

Abstract For many years now, and particularly since the 1930s, educational research has focused on the idea that all authentic education comes from experience. Nowadays, activism has found a natural affinity with the maker movement. Fab labs and creative ateliers have become more popular, especially for educational purposes, suggesting the coming of new types of “learning by doing.” However, these new forms of “learning by doing” must take account of the technologies already present in a particular creative space used by makers. These technologies are mainly: 3D printers, CNC milling machines, 3D scanners, laser cutters, etc. This short paper begins with a premise of educational ergonomics, to introduce teachers, media educators and animatori digitali (digital coordinators) to the didactic implications of introducing different human–machine interfaces (HMI) into their practices. In particular we describe the main features of SLA and SLS 3D printing. The impacts we discuss of 3D printing are resolution, types of printing materials, average printing times, post-processing, and cost. We have selected these criteria because it has been documented that their impact is very heavy in certain school subjects. For example, an FDM 3D printer can be useful in terms of the ease of printing an object, but it may not reach the necessary level of detail for a meticulous reproduction of art objects or precision mechanisms that an SLA 3D printer can achieve.
ConWST: Non-native Multi-source Knowledge Distillation for Low Resource Speech Translation

In the absence of source speech information, most end-to-end speech translation (ST) models showed unsatisfactory results. So, for low-resource non-native speech translation, we propose a self-supervised bidirectional distillation processing system. It improves speech ST performance by using a large amount of untagged speech and text in a complementary way without adding source information. The framework is based on an attentional Sq2sq model, which uses wav2vec2.0 pre-training to guide the Conformer encoder for reconstructing the acoustic representation. The decoder generates a target token by fusing the out-of-domain embeddings. We investigate the use of Byte pair encoding (BPE) and compare it with several fusion techniques. Under the framework of ConWST, we conducted experiments on language transcription from Swahili to English. The experimental results show that the transcription under the framework has a better performance than the baseline model, which seems to be one of the best transcriptional methods.