Reviewerly/exHarmony huggingface dataset:
The development of computer communication technology and its application in electronic information engineering

The rapid development of science and technology and the comprehensive application of information technology have brought electronic information engineering into the fast lane of development, and has also continuously improved the overall level of my country's electronic information engineering. Computer communication technology is an indispensable and important part of electronic information engineering, and there is a close relationship between the two in mutual influence and coordinated development. Strengthening the comprehensive application of computer communication technology in electronic information engineering can significantly improve the stability and efficiency of data and information transmission, and greatly improve people's daily life. It can have a positive meaning for the overall construction and development of society. To this end, it must be set out from a global perspective, consider how to improve the application effect of computer communication technology in electronic information engineering. Based on this, this article briefly analyzes computer communication technology and electronic information engineering, explores the development history, development status and development trend of computer communication technology, and analyzes the application ideas of computer communication technology in electronic information engineering.
Hybrid Residual Block Time-Delay Neural Network Embeddings for Speaker Recognition

Current speaker recognition systems are based ei-ther on time-delay neural network (TDNN) x-vectors or ResNet embedding speaker representations. Both architectures have their advantages and this paper aims to benefit from their prominent and complementary features. In contrast to what has been already proposed in the literature, we investigate the impact of using only one residual neural network block named ResBlock on x-vectors instead of the several blocks used in conventional sys-tems. Four ResBlock variants are integrated at the TDNN frame-level layer of x-vectors. The obtained hybrid One-ResBlock-TDNN architectures are evaluated using Speaker In The Wild (SITW) and Voices Obscured in Complex Environmental Settings (VOiCES) evaluation sets. The experimental assessment reveals that compared to conventional x-vectors' encoder, a noticeable accuracy improvement of all proposed hybrid One-ResBlock-TDNN variants has been achieved on both SITW and VOiCES standards' datasets.
Evaluating Machine Learning Classifiers for Data Sharing in Internet of Battlefield Things

The most widely used method to prevent adversaries from eavesdropping on sensitive sensor, robot, and war fighter communications is mathematically strong cryptographic algorithms. However, prevailing cryptographic protocol mandates are often made without consideration of resource constraints of devices in the internet of Battlefield Things (IoBT). In this article, we address the challenges of IoBT sensor data exchange in contested environments. Battlefield IoT (Internet of Things) devices need to exchange data and receive feedback from other devices such as tanks and command and control infrastructure for analysis, tracking, and real-time engagement. Since data in IoBT systems may be massive or sparse, we introduced a machine learning classifier to determine what type of data to transmit under what conditions. We compared Support Vector Machine, Bayes Point Match, Boosted Decision Trees, Decision Forests, and Decision Jungles on their abilities to recommend the optimal confidentiality preserving data and transmission path considering dynamic threats. We created a synthesized dataset that simulates platoon maneuvers and IED detection components. We found Decision Jungles to produce the most accurate results while requiring the least resources during training to produce those results. We also introduced the JointField blockchain network for joint and allied force data sharing. With our classifier, strategists, and system designers will be able to enable adaptive responses to threats while engaged in real-time field conflict.
Robustifying DARTS by Eliminating Information Bypass Leakage via Explicit Sparse Regularization

Differentiable architecture search (DARTS) is a promising end to end NAS method which directly optimizes the architecture parameters through general gradient descent. However, DARTS is brittle to the catastrophic failure incurred by the skip connection in the search space. Recent studies also cast doubt on the basic underlying hypotheses of DARTS which are argued to be inherently prone to the performance discrepancy between the continuous-relaxed supernet in the training phase and the discretized finalnet in the evaluation phase. We figure out that the robustness problem and the skepticism can both be explained by the information bypass leakage during the training of the supernet. This naturally highlights the vital role of the sparsity of architecture parameters in the training phase which has not been well developed in the past. We thus propose a novel sparse-regularized approximation and an efficient mixed-sparsity training scheme to robustify DARTS by eliminating the information bypass leakage. We subsequently conduct extensive experiments on multiple search spaces to demonstrate the effectiveness of our method.
Experimental Study of Swarm Migration Algorithms on Stochastic and Global Optimisation Problem

Complex computational problems are occurrences in our daily lives that needs to be analysed effectively in order to make meaningful and informed decision. This study performs empirical analysis into the performance of six optimisation algorithms based on swarm intelligence on nine well known stochastic and global optimisation problems, with the aim of identifying a technique that returns an optimum output on some selected benchmark techniques. Extensive experiments show that, Multi-Swarm and Pigeon inspired optimisation algorithm outperformed Particle Swarm, Firefly and Evolutionary optimizations in both convergence speed and global solution. The algorithms adopted in this paper gives an indication of which algorithmic solution presents optimal results for a problem in terms of quality of performance, precision and efficiency.
Classification of Gurumukhi month’s name images using various convolutional neural network optimizers

The Gurumukhi script has a complex structure for which text recognition based on an analytical approach can misinterpret the script. For error-free results in text recognition, the author has proposed a holistic approach based on classification of Gurumukhi month’s name images. For this, a new convolutional neural model has been developed for automatic feature extraction from Gurumukhi text images. The proposed convolutional neural network is designed with five convolutional, three polling layers, one flatten layer and one dense layer. To validate the results of the proposed model, the dataset was self-created from 500 distinct writers. The performance of the model has been analyzed with 100 epochs, 40 batch sizes and different optimizers. The various optimizers that have been used for this experimentation are SGD, Adagrad, Adadelta, RMSprop, Adam, and Nadam. The experimental results show that the proposed CNN model performed best with Adam optimizer in terms of accuracy, computational time, F1 score, precision and recall.
Analysis of Learning Components of Quantitative Reasoning in the Core Curriculum

Quantitative Reasoning (QR) requires students to think critically and to apply basic mathematics and statistics to interpret data, draw conclusions, and solve problems within a disciplinary or interdisciplinary context. In this study, the learning goal, learning content, learning method, and learning evaluation of QR were identified and analyzed. In other words, the learning components and characteristics of QR as a type of general education were examined. As a result, QR can be defined as follows:&lt;/br&gt;1. a habit of mind and one of the practical intellectual abilities that should be developed as a core curriculum in general education.&lt;/br&gt;2. integrated learning in an interdisciplinary context across the curriculum.&lt;/br&gt;3. a problem-centered, active participatory approach to learning that focuses on the process of inquiry rather than the learning of fixed knowledge.&lt;/br&gt;4. appropriate for process-oriented evaluation using a rubric.&lt;/br&gt;5. allowing us to be critically informed as citizens of a democratic society.&lt;/br&gt;QR thus takes on an over-arching status across the curriculum similar to effective communication skills. QR is an interdisciplinary subject that can be studied within various fields of learning, and can be opened in several departments with a focus on mathematics. Indeed, the call for QR to ‘permeate the curriculum’ and to become a centerpiece for interdisciplinary learning, linking a wide range of subjects to a common methodological framework, is highly recommended.
Forensics Studies based on Docker Containers

For now, there is little research on forensic methods in docker environments at home and abroad, and there are no forensic tools for such environments on the market. In order to improve the efficiency of forensic work in such environments, this article explores the differences and implementation methods of forensic work in Docker environments. Based on the characteristics and principles of docker file management system Overlay2, this essay gives feasible solutions for the extraction and statistics of file hash values, recovery of deleted files, record of container history process, keyword analysis search and index, etc. And based on this research work, an electronic forensics system for docker containers is designed.
Crypto Experts Advise What They Adopt

Previous studies have shown that developers regularly seek advice on online forums to resolve their cryptography issues. We investigated whether users who are active in cryptography discussions also use cryptography in practice. We collected the top 1 % of responders who have participated in crypto discussions on Stack Overflow, and we manually analyzed their crypto contributions to open source projects on GitHub. We could identify 319 GitHub profiles that belonged to such crypto responders and found that 189 of them used cryptography in their projects. Further investigation revealed that the majority of analyzed users (i.e., 85 %) use the same programming languages for crypto activity on Stack Overflow and crypto contributions on GitHub. Moreover, 90 % of the analyzed users employed the same concept of cryptography in their projects as they advised about on Stack Overflow.
Neural Metric Learning using Contrastive Anchors to Enforce Feature Separability

Deep metric learning is a paradigm to organize feature space such that dataset classes have small intra-class distance and large inter-class distance. While this domain has produced state-of-the-art results for challenging problems like facial recognition, one major limitation is the complexity of training these techniques. Methods like pairwise and triplet learning require heavy micromanagement with respect to intelligent sampling. Furthermore, methods like center loss and SphereFace require precise parameter selection to consistently ensure algorithm convergence. Herein, we propose Contrastive Anchor Network (CAN), a deep neural network that uses stationary anchors to enforce feature separability. CAN does not require intelligent sampling, can be trained with minimum parameter tuning, and uses a generalization of contrastive learning as its objective function. Experiments provide insight to CAN through qualitative feature embeddings and quantitative performance on popular benchmark datasets.
Why Not More Virtual Reality in Higher Ed Teaching and Learning?

Virtual reality (VR) is a promising tool for the field of training and education due to its potential in making the process more engaging and interactive. This study explored students' and academics' perceptions of why there is a lack of VR usage in the higher education teaching and learning (T&L) process. A qualitative approach was taken for this research where a number of focus groups sessions were arranged for volunteer participants to provide insights. Most participants were interested in the prospect of virtual technology; however, they diverge in agreement when it comes to the actual applicability and usefulness for T&L. There were five key themes uncovered for the lack of the practical usage of virtual reality in higher education based on the thematic analysis of the participants' response. Applicability to curriculum, Cost, Fear and familiarity, Comfort and practicality, and Content availability were themes of factors shared by the participants. There was also a mix of enthusiasm for the future adaptation of the technology on campus. Some participants were more optimistic about the presence of the technology in the future classroom but had cautioned on the need to prepare for it and overcome the challenges.
Zero-shot Key Information Extraction from Mixed-Style Tables: Pre-training on Wikipedia

Table, widely used in documents from various vertical domains, is a compact representation of data. There is always some strong demand to automatically extract key information from tables for further analysis. In addition, the set of keys that need to be extracted information is usually time-varying, which arises the issue of zero-shot keys in this situation. To increase the efficiency of these knowledge workers, in this study we aim to extract the values of a given set of keys from tables. Previous table-related studies mainly focus on relational, entity, and matrix tables. However, their methods fail on mixed-style tables, in which table headers might exist in any non-merged or merged cell, and the spatial relationships between headers and corresponding values are diverse. Here, we address this problem while taking mixed-style tables into account. To this end, we propose an end-to-end neural-based model, called Information Extraction in Mixed-style Table (IEMT). IEMT first uses BERT to extract textual semantics of the given key and the words in each cell. Then, it uses multi-layer CNN to capture the spatial and textual interactions among adjacent cells. Furthermore, to improve the accuracy on zero-shot keys, we pre-train IEMT on a dataset constructed on 0.4 million tables from Wikipedia and 140 million triplets from Ownthink. Experiments with the fine-tuning step on 26,869 financial tables show that the proposed model achieves 0.9323 accuracy for zero-shot keys, obtaining more than 8% increase compared with the model without pre-training.
Semantic Segmentation of Urban Street Scenes Using Deep Learning

Scene classification is essential conception task used by robotics for understanding the environmental. The outdoor scene like urban street scene is composing of image with depth having greater variety than iconic object image. The semantic segmentation is an important task for autonomous driving and mobile robotics applications because it introduces enormous information need for safe navigation and complex reasoning. This paper introduces a model for classification all pixel’s image and predicates the right object that contains this pixel. This model adapts famous network image classification VGG16 with fully convolution network (FCN-8) and transfer learned representation by fine tuning for doing segmentation. Skip Architecture is added between layers to combine coarse, semantic, and local appearance information to generate accurate segmentation. This model is robust and efficiency because it efficient consumes low memory and faster inference time for testing and training on Camvid dataset. The output module is designed by using a special computer equipped by GPU memory NVIDIA GeForce RTX 2060 6G, and programmed by using python 3.7 programming language. The proposed system reached an accuracy 0.8804 and MIOU 73% on Camvid dataset.
Design of Multi-channel Payload Data Simulator Based on TLK2711

A high-speed payload data simulator based on TLK2711 is designed and implemented, which is used to simulate the interface and data of multi-channel high-speed payload for remote sensing satellites. The simulator uses PCIe Gen3 8-bit high-speed interface to receive simulated payload data, uses high-speed FPGA, DDR to schedule and process multi-channel simulated payload data in parallel, and uses six TLK2711 interfaces to transmit simulated payload data. The designed simulator has the characteristics of high speed, large storage capacity and high integration. The internal loop-back test results show that the system can realize the high-speed parallel simulation and transmission of six channel payload data, the code rate of each channel reaches 1.6 Gbps. The simulator is stable, reliable, and can be widely used in satellite test.
Crowd Safety Sensing (CroSS) for the Post Pandemic Era

Due to its long incubation period, aggressive asymptomatic transmission, and new mutations of the virus, COVID-19 is causing multiple pandemic waves worldwide. Despite recent vaccination, social distancing, and social restriction efforts, false negatives, and dormant positives can make pandemics challenging to restrain. In addition to rapid vaccination, effective contact tracing, mask-wearing, and social distancing are critical for out-break containment and for achieving herd immunity. However, the existing technology solutions, such as contact tracing apps and social-distance sensing, have been met with suspicion due to privacy and accuracy concerns and have not been widely adopted. Without achieving a critical mass of individual users, these personal technologies have been rendered useless. On the other hand, large-scale policy efforts have been complicated, requiring the coordination of federal, state, and local governments and regulation enforcement logistics. However, local communities balance these approaches and are an unrealized, powerful resource to prevent future outbreaks.This paper proposes a novel Crowd Safety Sensing (CroSS) for building a sustainable safe community cluster against COVID-19 and beyond using affordable Internet of Things (IoT) technologies. CroSS monitors social distancing policies to small, focused communities for accommodating efficient technology penetration, greater accuracy, effective practices, and privacy policy assistance. We implemented a social distancing method and integrated it into an edge-based IoT system. The experimental results show that CroSS detects false-positive social distancing cases.
Enhanced Knowledge Graph Embedding for Multi-Task Recommendation via Integrating Attribute Information and High-Order Connectivity

Recently, knowledge graph (KG) has been introduced into recommender systems as side information to mitigate problems of sparsity and cold start, which attracts growing attention. Among these works, the multi-task learning methods that learn KG-related tasks and recommendation tasks have greatly enhanced the effectiveness of the recommendation. However, existing works tend to ignore attribute triples in the real knowledge graph, which makes the research affected by the sparsity of KGs. General knowledge graph embedding methods in multi-task recommendation barely take direct relations between entities into account, that leads to the ignorance of rich information in high-order relations. In addition, the lack of consideration on the user attribute information makes recommendation less than satisfactory. In order to relieve these issues, we propose a model of enhanced knowledge graph embedding for recommendation based on the multi-task learning, HMKR. Our method alternately trains recommendation task considering user-side information and enhanced knowledge graph embedding task integrating attribute information and high-order connectivity. The experiments on the real dataset MovieLens verify the effectiveness of HMKR. Even in the sparse data scenarios, the performance of our model is also satisfactory.
Physical Distance and Crowd Monitoring System using YOLOv3

The World Health Organization advised the public that physical distancing is one of the health protocols that can minimize the spread of coronavirus disease 2019 (COVID-19). The protocol requires people to adhere to a one-meter distance from each other in public areas, thus avoiding the possible crowd formation and further spread of the virus. A software was developed to monitor the physical distance and the crowd density of a specified area or the region of interest using You Only Look Once Version 3 (YOLOv3). Video recordings, captured using mobile phones, were extracted into frames. Each video frame is then processed to a YOLOv3 model for further object detection (here-human) and implementation of physical distancing monitoring. The selected area's crowd density is also computed while considering physical distancing guidelines. If the violations in physical distance or crowd density become alarming, an email will be sent to the authorities alerting them about the occurrence of health protocol violations. Based on careful evaluation, physical distancing and crowd density violation detection has an average of 0.86 for precision, 0.81 for recall, 0.83 for F1-score, and 0.83 for accuracy. The software also successfully alerted authorities via email of the exceeding violations. The efficiency and simplicity of this approach present possible solutions for the current pandemic situation.
A domain adaptive and continual learning method for skeleton behavior recognition in classroom environment

Student behavior in the classroom is an important part of the analysis of classroom process and effectiveness. Due to the different physiological and psychological states of the learners in the classroom environment, it is very common that there are differences between the same types of actions, and the diversity of the same types of actions will change over time. To solve the online recognition problem of the same kind of actions with high diversity in the classroom environment, a domain adaptive and continual learning method for skeleton-based behavior recognition is proposed, which achieves the online transfer ability of models between the same kind of actions. Experiments show that this method has better transferability and continuous learning performance on public datasets Florence, Kinetics and real classroom datasets, and the accuracy is improved by 2.3%,1.3% and 7.29% on the three datasets respectively, compared with the current state-of-the-art algorithms.
Model Validation and Selection

Chapter 4 discussed ERM as a principled approach to learning a good hypothesis out of a hypothesis space or model. ERM based methods learn a hypothesis $$\hat{h} \in \mathcal {H}$$ that incurs minimum average loss on some labeled data points that serve as the training set. We refer to the average loss incurred by a hypothesis on the training set as the training error. The minimum average loss achieved by a hypothesis that solves the ERM might be referred to as the training error of the overall ML method. ERM makes sense only if the training error of a hypothesis is a good indicator for its loss incurred on data points outside the training set. Whether the training error of a hypothesis is a reliable indicator for its performance outside the training set depends on the statistical properties of the data points and on the hypothesis space used by the ML method.