Reviewerly/exHarmony huggingface dataset:
Ensuring Safety and Security in Blockchains: A Private Capital Markets Example

The defining and a critical characteristic of blockchains powering the token economy is their potential to forge a strong ecosystem of disparate and widespread partners in a loosely coupled matrix of trusting relationships and dependencies. As blockchains increase their footprint in the business world, their ability to provide credible assurance of the safety and security of transactions will play an increasingly important decision factor for adoption and trust. In this chapter, we illustrate several risk mitigation principles involved in developing a safe and secure system with a live blockchain-enabled digital ecosystem called KoreConX designed to serve the needs of private capital market participants. We begin by describing the characteristics and challenges of modern digital ecosystems. Next, we explore the main components of safety and security, ranging from the purely technical security to the broader issues of safety through validations, governance, and distribution of control. We address the need of the various participants to maintain their individuality, value proposition, brand, and privacy, while also sharing data on a need-to-know basis and power on a need-to-act basis.
An Improved Automatic Modulation Classification Scheme Based on Adaptive Fusion Network

Due to the over-fitting problem caused by imbalance samples, there is still room to improve the performance of data-driven automatic modulation classification (AMC) in noisy scenarios. By fully considering the signal characteristics, an AMC scheme based on adaptive fusion network (AFNet) is proposed in this work. The AFNet can extract and aggregate multi-scale spatial features of in-phase and quadrature (I/Q) signals intelligently, thus improving the feature representation capability. Moreover, a novel confidence weighted loss function is proposed to address the imbalance issue and it is implemented by a two-stage learning scheme. Through the two-stage learning, AFNet can focus on high-confidence samples with more valid information and extract effective representations, so as to improve the overall classification performance. In the simulations, the proposed scheme reaches an average accuracy of 62.66% on a wide range of SNRs, which outperforms other AMC models. The effects of the loss function on classification accuracy are further studied.
Correction to: Intelligent Tutoring Systems

Due to an oversight, the originally published version of this paper did not include the acknowledgement “We acknowledge the support of CRIAQ, the Natural Sciences and Engineering Research Council of Canada (NSERC), CAE, Bombardier, and BMU.” This has now been included.
A modified logisticMF Method based on experts and data characteristics for electric power applications

Based on the electric power data of Pudong Electric Power station in Shanghai, the model training and experiment are carried out with one million magnitude negative control data. Based on the logisticMF algorithm as the theoretical support, the abnormal data recommendation model is established on the basis of experts and data characteristics. The existing traditional recommendation method is automatic distribution methods, which has problems of chaotic distribution and low recommendation accuracy. Based on the recommendation model of logisticMF, this paper integrates the characteristics of expert attributes, user preferences and data labels, and puts forward the ABDR-LogisticMF model (Abnormal Data Recommend logisticMF) suitable for power data recommendation service. Experimental results show that the accuracy of the proposed ABDR-LogisticMF model is higher than that of automatic distribution, which can fully meet the recommended business requirements of the current number of abnormal data, and also provides support for subsequent distribution mechanisms of other abnormal energy data.
Use of teacher's language style in the grade 3 learning process of Ciheuleut 1 Elementary School Bogor

Background: the communication pattern applied by the teacher becomes a vital thing to be discussed.&#x0D; Purpose: This study aims to find the use of the language style used by the teacher.&#x0D; Design and methods: The research approach used is a qualitative approach. The research method used is content analysis. The data analysis of this research was carried out through the Miles and Huberman flow model consisting of: 1) identification, 2) data reduction, 3) data presentation, 4) conclusion drawing and verification, and 5) final conclusions and recommendations.&#x0D; Results: third grade teachers at SD Negeri Ciheuleut 1 in communicating with students tend to prioritize conveying messages over other things. What is meant here is close communication so that messages from the teacher will be easier and faster to be understood by students.
Comprehensive Evolution of Pharmaceutical Industries by Pioneering Blockchain Technology with 6G Wireless Networks Amalgamation

When it comes to the use of new technology, the pharmaceutical industry has undergone many transformations. Operational issues and counterfeit drugs are two of the most commonly encountered challenges in the pharmaceutical industry; the solution is to provide authenticate vigilance to track pharmaceutical products from starting of production to consumption for avoiding further financial losses. Due to blockchain's streamlined nature for keeping total confidentiality and efficiency to the traditional method; it has the potential to resolve it and speed up the process by emerging 6G wireless network. Despite the fact that blockchain-based systems offer many benefits across the pharmaceutical industry, the adoption rate of blockchain technology remains somewhat low due to the lack of knowledge, awareness, and the lack of understanding that why, how, and where blockchain technology with 6G is required in their domains. This chapter explains blockchain technology, the benefits of a blockchain-based system and how it enhances the overall efficiency of pharmaceutical industries by integrating 6G wireless networks.
Global Interpretable Calibration Index, a New Metric to Estimate Machine Learning Models’ Calibration

The concept of calibration is key in the development and validation of Machine Learning models, especially in sensitive contexts such as the medical one. However, existing calibration metrics can be difficult to interpret and are affected by theoretical limitations. In this paper, we present a new metric, called GICI (Global Interpretable Calibration Index), which is characterized by being local and defined only in terms of simple geometrical primitives, which makes it both simpler to interpret, and more general than other commonly used metrics, as it can be used also in recalibration procedures. Also, compared to traditional metrics, the GICI allows for a more comprehensive evaluation, as it provides a three-level information: a bin-level local estimate, a global one, and an estimate of the extent confidence scores are either over- or under-confident with respect to actual error rate. We also report the results from experiments aimed at testing the above statements and giving insights about the practical utility of this metric also to improve discriminative accuracy.
Analysis and prediction of web proxies misbehavior

The need for anonymity and privacy has given a rise to open web proxies that act as gateways relaying traffic between web servers and their clients, allowing users to access otherwise not accessible content. As the open web proxy ecosystem continues to grow, research studies point out the extent of content alteration on the Internet. While the previous studies focused on detection and analysis of content manipulation by proxies, we focus on the feasibility of predicting these manipulations. In this work, we present a new approach for predicting the types of content alterations that might be silently introduced by open proxies. Our approach is designed to proactively indicate changes without a need to fetch the data through a proxy first. We explore the feasibility of the approach on a website content of 1028 domains fetched through 1293 proxies. We leverage our approach to proactively and accurately identify various content manipulations with 87% - 92% accuracy. Our study reveals an important observation that the majority of proxies manipulate website content based on technical information of the website and its web server.
Research on Information Leakage Tracking Algorithms in Online Social Networks

An online social network is a platform where people can communicate with friends, share information, speed up business development, and improve teamwork. A large amount of user privacy information existing in real social networks is leaked from person to person, and this issue has hardly been studied. With the rapid expansion of the network, the issue of privacy protection has received increasing attention. So far, many privacy protection methods including differential protection algorithms, encryption algorithms, access control strategies, and anonymization have been researched and applied. Information leakage means that the information shared by the user is disseminated or downloaded by his friends without the user's consent, and the transmission of private information will not be recorded. In order to track and find out the ways and methods of information leakage, this article adopts an unusual method, namely, the probability judgment based on trust. By screening the similarities between users, past information exchanges, and the topology of social networks, a trust model is established to evaluate and estimate the degree of trust between users. According to the rating information privacy of friends' trust, an information dissemination system is established, which can be applied to online social networking platforms to reduce the risk of information leakage, thereby ensuring the security of users' private information. At the same time, this paper expands the transmission system model without user authorization and proposes a fingerprint-based deterministic leak tracking algorithm.
Experimental validation of model-based digital holographic imaging using multi-shot data

Imaging through deep turbulence is a hard and unsolved problem. There have been recent advances toward sensing and correcting moderate turbulence using digital holography (DH). With DH, we use optical heterodyne detection to sense the amplitude and phase of the light reflected from an object. This phase information allows us to digitally back propagate the measured field to estimate and correct distributed-volume aberrations. Recently, we developed a model-based iterative reconstruction (MBIR) algorithm for sensing and correcting atmospheric turbulence using multi-shot DH data (i.e., multiple holographic measurements). Using simulation, we showed the ability to correct deep-turbulence effects, loosely characterized by Rytov numbers greater than 0.75 and isoplanatic angles near the diffraction limited viewing angle. In this work, we demonstrate the validity of our method using laboratory measurements. Our experiments utilized a combination of multiple calibrated Kolmogorov phase screens along the propagation path to emulate distributed-volume turbulence. This controlled laboratory setup allowed us to demonstrate our algorithm’s performance in deep turbulence conditions using real data.
Convolution neural network based degenerated mode decomposition via near-field images from linear and circular polarizers

Mode decomposition (MD) method for degenerated modes has been studied. Convolution neural network (CNN) has been applied for image training and predicting mode coefficients. Four-fold degenerated LP11 series has been the target to be decomposed. Total of seven different images, including full original near-field image, and images after linear polarizers of four directions (0°, 45°, 90°, and 135°), and images after two circular polarizers (right-handed and left-handed) has been considered for training, validation, and test. The output label of the model has been chosen as the real and imaginary components of mode coefficients, and the loss function has been selected to be root-mean-square (RMS) of labels. The RMS and mean-absolute-error (MAE) of the label, intensity, phase, and field correlation between actual and predicted values have been selected to be the metrics to evaluate the CNN model. The CNN model has been trained with 100,000 three-dimensional images with depths of three, four, and seven. The performance of trained model was evaluated via 10,000 test samples with four sets of images - images after three linear polarizers (0°, 45°, 90°) and image after right-handed circular polarizer - showed 0.0634 of label RMS, 0.0292 of intensity RMS, 0.1867 rad of phase MAE, and 0.9978 of average field correlation. The performance of 4 image sets showed at least 50.68% of performance enhancement compared to model considering only images after linear polarizers.
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network

To improve robustness of deep reinforcement learning agents, a line of recent works focus on producing disturbances of the dynamics of the environment. Existing approaches of the literature to generate such disturbances are environment adversarial reinforcement learning methods. These methods set the problem as a two-player game between the protagonist agent, which learns to perform a task in an environment, and the adversary agent, which learns to disturb the dynamics of the considered environment to make the protagonist agent fail. Alternatively, we propose to build on gradient-based adversarial attacks, usually used for classification tasks for instance, that we apply on the critic network of the protagonist to identify efficient disturbances of the dynamics of the environment. Rather than training an adversary agent, which usually reveals as very complex and unstable, we leverage the knowledge of the critic network of the protagonist, to dynamically increase the complexity of the task at each step of the learning process. We show that our method, while being faster and lighter, leads to significantly better improvements in robustness of the policy than existing methods of the literature.
The Calibration Method of Time Code Equipment

Time code is widely used in television broadcasting systems, power, and aerospace. Time synchronization accuracy is an important indicator of each time code equipment, and the measurement and calibration of time code equipment is mainly the measurement and calibration of the time synchronization accuracy and time information of each time code. Based on the portable time code measuring instrument, the working principle and basic characteristics of the portable time code measuring instrument are analyzed, the measurement method and measurement principles of 1PPS, B(DC) code, B(AC) code and other time codes are introduced, and the calibration method of 1PPS, B(DC) code and B(AC) code are discussed.
Hate Words Detection Among Sri Lankan Social Media Text Messages

The number of Sri Lankan social media users have been increased with the rapid growth of 23% between 2020 and 2021, reaching 7.9 million in 2021 January. Social media platforms became more popular when they started supporting native languages. The problems with social media also evolved as popularity grows. Social media platforms were banned for Sri Lankan users in 2019 to prevent the spreading of hate messages and incorrect information among citizens. The lack of automatically recognizing tools for hate messages in Sinhala and Romanized Sinhala was reported as the reason for the ban. It’s also a waste of time and money to manually identify them. Many studies have been conducted to identify hate messages in both English and Sinhala separately. Users in Sri Lanka tend to combine Sinhala, Romanized Sinhala, and English phrases while expressing their opinions." Mama job ekakata apply kara," for example. To train, an open-source data set which consists of 2500 comments, was used. And the comments were categorized as either hateful or non-hateful. To pre-process the data set, an Open-source stop word corpus and stem word corpus in Sinhala were utilized, and two corpus were manually converted into Romanized Sinhala stop word corpus and Romanized Sinhala stem word corpus to identify stop words and stem words in Romanized Sinhala. All English words were recognized using an open- source English word corpus, and a library was utilized to obtain stop word corpus and stem English words. As a result, doing research to identify hate speech in all of the languages indicated above will be more effective in reaching Sri Lankan users. The bag of words and term frequency-inverse document frequency were compared for feature engineering. Linear Support vector classifier, Random Forest Classification, SGD classifier, Logistic Regression, XGBoost classifier and multinomial Naive Bayes classifier are used as classification algorithms and evaluated. Using the SGD classification using TF-IDF with uni&bi-gram, the highest accuracy was determined to be 74.2%.
Extraction of Sentiments in Tamil Sentences Using Deep Learning

Sentiment analysis is the process of extracting information from the given text in which the text consists of various sensations such as happiness, perturbation, pride, worry, and so on about various functions, human beings, systems, and facts. Sentimental analysis or opinion mining uses data mining and natural language processing techniques to discover, retrieve and filter the information and opinions from the World Wide Web’s vast textual information. The sentiment analysers for European languages and some Indic languages are fully developed. However, Tamil, which is an under-resourced language with rich morphology, has not experienced these advancements. A few experiments have been conducted to determine the sentiments for Tamil text. An approach to doing the sentiment analysis for the Tamil language is proposed in this paper. The proposed approach uses Long Short-Term Memory, Convolutional Neural networks, and simple Deep Neural Network techniques. Test results show that the Long Short-Term Memory-based deep learning model performs well than the Convolutional Neural Network and simple Deep Neural Network for sentiment analysis of Tamil language with 94.10% accuracy.
Designing of a Voice-Based Programming IDE for Source Code Generation: A Machine Learning Approach

Humans are precise in recognizing natural languages and responding contextually unlike machines. However, speech recognition or Automatic speech recognition often refers to converting human speech or voice to textual information with the help of artificial intelligence algorithms. With the advancement of Artificial Intelligence technologies and extensive research being conducted in AI, speech recognition has received much attention and has emerged as a subset of Natural Language Processing where the advancement and accuracy in speech recognition will open many ways to provide a high standard of human-computer interaction. In this study, using the pre-trained transformer model with a transfer learning approach, the English to Python dataset was used to train the transformer model to produce syntactically correct source code in python. Additionally, the Word2Vec model was used to generate voice-to-text as input for the model. For the purpose of demonstration, a custom Python IDE is developed to generate source code from voice input. The results and findings suggest that in the transformer model, with the use of transfer learning, any dataset can be trained to produce syntactically correct source code. The model’s training loss and validation loss were below 5 and 2.1, respectively. Future research can focus on generating valid source code from any human spoken language without restricting it to English only.
A Lightweight Network for Semantic Segmentation of Road Images Based on Improved DeepLabv3+

Aiming at the problem that the existing semantic segmentation algorithms have a large number of model parameters and are difficult to be used on mobile devices, a lightweight network for semantic segmentation of road images based on improved DeepLabv3+ is proposed. The algorithm improves DeepLabv3+, uses the lightweight MobileNetv2 as the backbone network to reduce the complexity of the network model, and extracts low-level feature details at different depths; uses a Distinctive Atrous Spatial Pyramid Pooling (DASPP) to enhance the multiscale semantic information to obtain rich global contextual semantic information by feature extraction; after introducing an attention mechanism to enhance feature learning, a Detail-preserving Feature Fusion Network (DFFN) is used for feature fusion. Experiments are carried out on the Cityscapes dataset, and the model parameters of the improved DeepLabv3+ algorithm are 2.75M, which is 42.87M less than that of DeepLabv3+. The road image semantic segmentation model proposed in this paper can greatly reduce the number of model parameters while segmenting images well.
SDAN: Semantic-Driven Dual Attentional Network for Image Generation

The objective of pose-guided person image generation is to convert a given person’s pose into the desired pose. Exciting approaches employing the U-net architecture would result in the loss of the majority of fine-grained information, leading to over-smoothed clothing and missing details in the final results. In this paper, we propose a novel semantic-driven dual-attention network to address this issue. The whole framework contains two stages. In the first stage, the spatial misalignment is initially corrected by transferring the semantic parsing map to the target pose. In the second stage, we leverage a novel dual attention mechanism to better incorporate the spatial structure and texture style information into the image generation process. Extensive experiments demonstrate that our proposed method can generate images that are more consistent and photo-realistic than current methods in quantitative and qualitative evaluation.
Granularity Classification and Feature Fusion Methods in Traffic Sign Detection

Nowadays, deep learning based on detection algorithms have replaced the traditional manual feature extraction target algorithms and have achieved amazing results in many places with their powerful automatic feature extraction capabilities. However, the results are not ideal for the detection of small targets with low resolution and a lot of noise, such as traffic signs. To address the current problems of slow detection speed and low detection accuracy in small target detection, this paper adopts a feedback-driven mechanism to solve the image level imbalance of the input feature space under the original data distribution. At the same time, this paper designs a novel and flexible two-stage traffic sign recognition framework. The complex task of traffic sign detection and recognition is decomposed into two stages: 1) designing a superclass classifier to more accurately separate traffic signs in complex natural scene images; 2) The idea of similarity metric learning is used to design fine-grained classifiers to recognize traffic signs. Finally, to verify the effectiveness of the model, the model was first compared with Faster R-CNN and found to possess higher detection accuracy; then the model was experimented with the R-FCN model on TTIOOK dataset and CCTSDB dataset respectively, and the comparison of the experimental results revealed that the model improved over the R-FCN model in most of the metrics in the traffic sign detection task.