Reviewerly/exHarmony huggingface dataset:
Radar Signal Modulation Recognition Based on Improved Instantaneous Autocorrelation

Radar signal recognition is one of the kernel aspects of the radar emitter recognition process. In order to realize the fast and efficient recognition of intra-pulse modulation of radar signal, this paper introduces a method based on improved instantaneous autocorrelation. Firstly, the signal is roughly divided into phase modulation signals and frequency modulation signals according to the bandwidth. And then the instantaneous autocorrelation feature of the signal is extracted by the improved method. Finally, the frequency modulation and phase modulation signal types are subdivided based on the amplitude and phase characteristics of instantaneous autocorrelation. Simulation results show that the method has low computational complexity and good recognition success rate.
VIEW: Optimization of Image Captioning and Facial Recognition on Embedded Systems to Aid the Visually Impaired

Recent advancements in object recognition, face recognition, and optical character recognition allows for obtaining detailed descriptions of an environment. However, they present computational challenges when run on low-powered, embedded devices. The system described in this paper, Visual Interpreter of Environment Wizard (VIEW), combines these techniques in a computationally efficient manner designed to run on portable, low-powered devices. VIEW can assist people with visual impairment by providing natural language descriptions of their surroundings, information about the relative movement of nearby objects, and navigational utilities. For object recognition, we have implemented a modified deep neural network architecture that consists of purely convolutional layers with quantization and does not contain any fully connected layers to reduce the size of the model to provide optimal performance. The model is trained on the Common Objects in Context (COCO) dataset for multiple objects of various classes and processes the image during inference only once which enables faster image processing. All features of the system integrate voice commands for hands-free use. On a Raspberry Pi 4, the system achieves near real-time image captioning performance.
Integrative Psychological and Pedagogical Disciplines at an Engineering University

The paper focuses on the theoretical and practical aspects of teaching psychological and pedagogical disciplines at Kazan National Research Technological University at various levels of education (bachelor’s, master’s, postgraduate, retraining, and advanced training of teachers). The paper considers the results of a survey that allows assessing the value of such disciplines for various groups of students at an engineering university. It reveals the common and different views of different groups of respondents about the importance of studying psychological and pedagogical disciplines. The author proposes an integrative approach and identification of interdisciplinary connections as a theoretical basis for teaching such disciplines in the system of engineering education. We understand psychological and pedagogical disciplines as a necessary component in building professional competence, including that in technical education. These disciplines are the basis for the three blocks of key competencies: socio-personal, personal-activity, and cognitive. All these competencies are necessary for both the teacher and the future engineer.
Comparison Analysis for Pruning Algorithms of Neural Networks

Deep learning has been widely applied in many fields, such as big data analysis, natural language processing, and image processing, etc. However, the number of parameters in the deep learning model is often too large that the model would be difficult to apply portable devices. Therefore, the researches about pruning algorithms for optimizing the deep learning model has great significance. This paper conducts an in-depth analysis of the pruning algorithm in the model compression method. Besides, we classify and summarize the structure, scheduling, and scoring of the pruning algorithms. And then, the compression effects of some mainstream pruning algorithms on some deep neural networks including accuracy are compared and analyzed. Finally, the future development of the pruning algorithm is promising. Regardless of the different pruning approaches, the mainstream pruning algorithms can all effectively compress the size of the network model while maintaining basic accuracy. However, one pruning algorithm has a different performance when it is applied to different network models and datasets. The future pruning algorithm can be designed in terms of network sparsity structure, scheduling, and parameter ranking criteria. Moreover, the datasets and models are also required to be considered when the corresponding high-performance pruning strategies are used.
Effect of Geometric Complexity on Intuitive Model Selection

Occam’s razor is the principle stating that, all else being equal, simpler explanations for a set of observations are to be preferred to more complex ones. This idea can be made precise in the context of statistical inference, where the same quantitative notion of complexity of a statistical model emerges naturally from different approaches based on Bayesian model selection and information theory. The broad applicability of this mathematical formulation suggests a normative model of decision-making under uncertainty: complex explanations should be penalized according to this common measure of complexity. However, little is known about if and how humans intuitively quantify the relative complexity of competing interpretations of noisy data. Here we measure the sensitivity of naive human subjects to statistical model complexity. Our data show that human subjects bias their decisions in favor of simple explanations based not only on the dimensionality of the alternatives (number of model parameters), but also on finer-grained aspects of their geometry. In particular, as predicted by the theory, models intuitively judged as more complex are not only those with more parameters, but also those with larger volume and prominent curvature or boundaries. Our results imply that principled notions of statistical model complexity have direct quantitative relevance to human decision-making.
Silver bank: vulnerability and risks during cyberattacks

Learning outcomes The learning outcome of this paper is to identify and interpret the risks linked to cyber-security and their impact on the organization. Analyze business management regarding cyber-security and information technology (IT) risk management. Evaluate and propose decision-making strategies for IT projects. Case overview/synopsis Silver Bank is a financial entity with broad national coverage. Its growth was directly related to its investments in customer service. The entire organization is focused on satisfying its clients’ needs, improving their experience and making them loyal to the company. However, it did not pay enough attention to a threat that, with time, had become more pronounced: cyber-attacks. Its efforts to fight against this threat were only temporary solutions, as gaps in its IT system made it an easy target for criminals until the arrival of Iván Ramírez, who proposes a holistic solution to decrease the probability and severity of these attacks. However, past experiences, ignorance and budget constraints make it a difficult task to convince the bank’s board of directors to implement the proposed solution. Complexity academic level The case can be used as teaching material in upper-level undergraduate and graduate management courses: –undergraduate courses: information technology management, IT project analysis and management – MBA or graduate courses: information technology management, strategic management and security governance. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.
Vicinity-based Consensus: A Fast in-Neighborhood Convergence Consensus Mechanism for Blockchain

Private blockchains tend to apply deterministic con-sensus mechanisms as a more efficient alternative to the proof-based consensus. Deterministic mechanisms tolerate two types of failures, byzantine and crash-fault. Byzantine fault-tolerant consensus assumes restrictive assumptions of time and number of failures to guarantee validity, while the termination depends on message broadcasting among nodes. Crash-Fault tolerant consensus is less rigorous to ensure termination and higher throughput while sacrificing agreement. This paper proposes a lightweight consensus mechanism based on vicinity voting with confirmed message broadcasting. Formation rules in the neighborhoods of the peer-to-peer network relax the trade-off between agreement and termination. Experimental results show that the proposal guarantees agreement and termination in case of more permissive formation rules. Besides, the cost of achieving consensus is reduced by up to 46&#x0025; in more rigorous formation rules with limited impact on termination and agreement.
Research on Blockchain Reshaping Integrated Enterprise Financial Information System in Digital Era

In the era of fast-developing digital economy, the management systems and models of existing manufacturing enterprises are facing many challenges. As an emerging underlying technology of the Internet, blockchain has been widely used in many business fields. Based on the digital transformation of manufacturing enterprises, this paper discusses how to reshape the production and operation processes of manufacturing enterprises, build internal and external value chain networks, and improve the financial integration information systems by taking advantage of the characteristicss of blockchain technology from the firm value delivering and transferring perspective. The study provides a theoretical basis and empirical reference for the application of blockchain technology in manufacturing enterprises.
The Temple University Digital Pathology Corpus: The Breast Tissue Subset

The Neural Engineering Data Consortium (NEDC) is developing the Temple University Digital Pathology Corpus (TUDP), an open source database of high-resolution images from scanned pathology samples [1], as part of its National Science Foundation-funded Major Research Instrumentation grant titled &#x201C;MRI: High Performance Digital Pathology Using Big Data and Machine Learning&#x201D; [2]. The long-term goal of this project is to release one million images. We have currently scanned over 100,000 images and are in the process of annotating breast tissue data for our first official corpus release, v1.0.0. This release contains 3,505 annotated images of breast tissue including 74 patients with cancerous diagnoses (out of a total of 296 patients). In this poster, we will present an analysis of this corpus and discuss the challenges we have faced in efficiently producing high quality annotations of breast tissue.
Photography vs. visualisation. Technical images in archaeological research

The aim of the paper is to compare photographs and images created with the use of digital graphic software, the so-called synthetic or technical images. 3D visualisation imitates photography. In terms of appearance and composition it resembles the reflection of reality captured with a camera. Graphic software which is used for creating spatial reconstructions works on algorithms of a photographic lens and a camera. The paper focuses on the significance of modern methods of imaging in archaeological research. New digital technologies have made it possible to create images that exist only through the use of 3D graphics programs. The depictions do not have their counterparts in reality. The problem draws the attention to the questions raised by philosophers regarding the existence and limits of cognition. Synthetic images, as fictitious depictions, have been endowed with great authority of “probability”. Using graphic software to create a new kind of images resulted in the change of relation of representation to simulation.
Using Holt-Winters and IF-XGBoost model to detect the abnormal condition of PV system

Photovoltaic (PV) power generation is one of the primary forms of electricity generation. This paper proposes the Holt-Winters(HW) and IF-XGBoost model based on Bayesian optimization to identify the abnormal condition of PV systems. HW is used to predict the expected output power, and multidimensional features are constructed using the predicted power and time series. The study construct the IF-XGBoost model with isolation forest(IF) and XGBoost, adopting the Bayesian optimization to optimize the hyperparameters in HW and IF-XGBoost. The proposed methods are validated by comparing the accuracy of different models. The result shows that the IF-XGBoost model is better than other models, whose F1-score is 0.981 on the test set. Furthermore, IF-XGBoost demonstrates better performance on the test set with the white Gaussian noise. This paper provides a scheme for early abnormal condition detection and early warning in large-scale PV systems.
Taxonomy grooming algorithm ‐ An autodidactic domain specific dimensionality reduction approach for fast clustering of social media text data

Abstract Social media being the most eminent source toward the growth of big data is important for information retrieval‐based applications to improve the efficiency in proportional to the volume it must deal with. One way to achieve better performance is to upgrade the processing capacity and the alternative option is to improve the processing methodology. The latter can be achieved using smarter processing techniques and/or better algorithms. Reducing the data volume that needs to be processed is a good strategy and it can be achieved by extracting only the relevant information via user segmentation by adopting an appropriate clustering technique. However, while dealing with text content, clustering algorithms do suffer due to the very high dimensions to be dealt with. Since the domain‐specific aspects are getting lost while applying traditional dimensionality reduction approaches, it is important to device an alternate strategy. This work proposes a taxonomy grooming algorithm (TGA), an autodidactic domain‐specific dimensionality reduction approach, for fast clustering of social media text data. Our experiment results are very promising and the dimensionality reduction using TGA resulted in better results in comparison with the traditional dimensionality reduction approaches.
Evaluation of Distance Learning from the Perspective of University Students - A Case Study

The COVID-19 pandemic has had a significant impact on education. Almost overnight, it basically halted teaching at most educational institutions, and has gradually affected all educational levels. This situation was unprecedented. Educational institutions had to switch to a new form of education urgently. The pandemic affected the means as well as the methods of learning. It affected all stakeholders, i.e., teachers, students/pupils and institutions. The aim of this paper is to present the research results of a questionnaire that was focused on the distance learning experience of students. The questionnaire provided student feedback about the distance form (remote modality) of teaching they had experienced. The research was carried out at the Faculty of Informatics and Management (FIM) at the University of Hradec Králové. The surveyed students studied during the 2020/2021 academic year using distance learning. Overall, 122 students took part in the research. Results show that online tools such as Microsoft Teams and BlackBoard are most often used at FIM. Students indicated that the BlackBoard Learning Management System (LMS) was the most beneficial tool. Overall, respondents are equally satisfied with distance learning and face-to-face learning. It cannot be unequivocally determined whether students prefer distance learning or face-to-face learning.
DML: Differ-Modality Learning for Building Semantic Segmentation

This work critically analyzes the problems arising from differ-modality building semantic segmentation in the remote sensing domain. With the growth of multimodality datasets, such as optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR), and the scarcity of semantic knowledge, the task of learning multimodality information has increasingly become relevant over the last few years. However, multimodality datasets cannot be obtained simultaneously due to many factors. Assume that we have SAR images with reference information in one place and optical images without reference in another; how to learn relevant features of optical images from SAR images? We refer to it as differ-modality learning (DML). To solve the DML problem, we propose novel deep neural network architectures, which include image adaptation, feature adaptation, knowledge distillation, and self-training (SL) modules for different scenarios. We test the proposed methods on the differ-modality remote sensing datasets (very high-resolution SAR and RGB from SpaceNet 6) to build semantic segmentation and to achieve a superior efficiency. The presented approach achieves the best performance when compared with the state-of-the-art methods.
Behavicker: Eavesdropping Computer-Usage Activities Through Acoustic Side Channel

Computers are widely used for business and entertainment purposes throughout our modern lives. Computer kits provide a variety of services such as text processing, programming, shopping, gaming, etc. Computers have greatly enhanced the quality of our lives; however, we discover an often-overlooked fact that engaging in computer-related activities may be eavesdropped upon by an attacker by sniffing the emitted acoustic signals from keyboard and mouse. The activity of eavesdropping via acoustic sidechannel has lower requirements in terms of hardware instrumentation and is easier to implement in real-world applications than other side-channel attacks that have been presented in previous work. In this paper, we design and implement a system, namely Behavicker, to validate the feasibility of this kind of attack. Unlike conventional activity recognition, Behavicker infers high-level computer-usage activities with a semantics-preserving multi-scale learning scheme, based on the recognition of basic keyboard and mouse events including left-click, right-click, middle-click, scrolling up, and scrolling down. Real-world experiments show that Behavicker can recognize six interaction events with an accuracy of 88.3% and infer computer-usage activities with an accuracy of 82.7% in an indoor environment.
Dual Attention Based Uncertainty-aware Mean Teacher Model for Semi-supervised Cardiac Image Segmentation

Recently, many fully supervised deep learning based methods have been proposed for automatic cardiac segmentation. However, it is very expensive and time-consuming to annotate data for the task. In this paper, we present a novel dual attention based uncertainty-aware mean teacher semi-supervised framework (DA-UAMT) for cardiac image segmentation. The framework consists of a teacher model and a student model with the same structure and the student model learns from the teacher model by minimizing a segmentation loss generated from labeled images and a consistency loss generated from unlabeled images with respect to the targets of the teacher model. To enable the student model learn from more reliable targets, we introduce the Monte Carlo Dropout which estimates target uncertainty, and a novel dual attention mechanism which helps the network to focus on information in shape and channel dimension. To evaluate the proposed method, we conducted experiments on MICCAI 2017 Automated Cardiac Diagnosis Challenge (ACDC) dataset. Experiments show that our proposed DA-UAMT model is effective in utilizing unlabeled data to obtain considerably better segmentation of cardiac.
Distributed Unmanned Aerial Vehicle Positioning for Multitarget Tracking with Bearing-Only Information

This paper proposes a distributed positioning algorithm for a swarm of unmanned aerial vehicles (UAVs) to track multiple moving targets with bearing-only measurements. An approximate performance metric is first derived that can be used in position determination based on the properties of multisensor joint probabilistic data association (JPDA) filter. A fully distributed position planning algorithm using incremental optimization strategy is then proposed for tracking multiple moving targets. Simulation examples with comparison results validate the effectiveness of the proposed algorithm.
Federated Fine-Tuning Performance on Edge Devices

In this work, we introduce and evaluate federated fine-tuning (FFT) toward developing decentralized systems for IoT applications using edge computing. We demonstrate a deployed, off-grid, FFT network composed of embedded hardware and assess the system and its performance. The federated averaging algorithm has become the popular approach in decentralized systems with multiple nodes due its low computational costs and simplicity. However, it is commonly implemented using a workstation or a cloud as its server node, typically demonstrated with unrealistic (small) neural network models and may have high communication cost for embedded applications. To address these challenges, we present two main contributions by: (1) Improving the federated averaging algorithm’s weight initialization step and by limiting percentage of weights being averaged to enhance system security and model performance, and (2) Demonstrating the proposed system’s effectiveness via deployment of realistic model using an edge device as the server node (first time for a federated system). For our evaluation, we use a centrally pre-trained MobileNetV2 model on the CelebA dataset. We record the transmitted model parameters across the network with the modified federated averaging algorithm and FFT, and capture metrics related to memory, power consumption, CPU load, and communication on the device. Overall, results demonstrate that FFT can improve federated system performance and model accuracy while providing stronger privacy, protection of intellectual property, and security against adversarial attacks on federated learning.
A fast parameter optimization approach based on the inter-cluster induced distance in the feature space for support vector machines

This paper focuses on the problem of optimizing the kernel and penalty parameters for SVM classifiers with Gaussian kernel. To reduce the computational overhead of inter-cluster distance in the feature space (ICDF) with a large number of candidate discretized values in a large interval in previous researches, in this paper, the new inter-cluster induced distance in the feature space (ICIDF) is proposed to guide the kernel parameter selection of SVMs, and the theorem that the ICIDF is a positive strictly unimodal function about Gaussian kernel parameter is firstly presented. Then, a fast parameter optimization approach including two stages is presented for SVMs according to this theorem. In the first stage, a modified golden section algorithm (MGSA) is proposed to obtain a shrunk value interval for kernel parameter in small amount of ICIDF calculations. In the second stage, a differential evolutionary algorithm (BBDE or SADE) is applied to select the best parameter combination for SVM in the shrunk interval of kernel parameter obtained by MGSA and a given interval of penalty parameter. Experiments for benchmark datasets illustrate that the training time of SVM models can significantly shortened by our approach, while the testing accuracy of the trained SVMs is competitive.