Reviewerly/exHarmony huggingface dataset:
Testing the Data Protection Through Implementation of Secure Communication

Abstract The security of using applications in cloud services and on the Internet is an important topic in the field of engineering. In this paper, two laboratory tests for data transmission protection, specifically designed for different security analysis techniques, are presented and explained. During lab tests on public Wi-Fi networks from the MIDM (“Man in the Middle”) attacks, various monitoring techniques were applied, using a special lab test scenario with Kali Linux penetration tools by creating an SSH tunnel on an Android mobile device. These test benches allow easy data capturing, and the captured data is processed using available software programs. Expected outcomes, practical improvement and security performance assessment are presented in detail, and considered in terms of their value in security engineering. The aim of this paper is to detect and overcome some of the weaknesses of the application of security protocols in a Wi-Fi network environment.
Training intervention as a means for the implementation of mobile learning

Abstract In this research, tools of the Ontosemiotic Approach (OSA) were used to analyse the formative experience of two teachers in Primary Education service, guided towards the design and implementation of tasks for mobile learning, based on the meaning of the natural number and the use of Kahoot. The experience was developed during a workshop on the design of interactive lessons, where teachers were trained on the use of Kahoot for the design of mathematical content and its implementation in a real class context. Didactic engineering in the general sense of OSA was used as a methodological element. Specifically, the case of the design and implementation of an interactive lesson on the meaning of number sequence was analysed, where some results of the retrospective analysis of the formative experience indicated that: 1) the reference meaning of the natural number is a tool that allows to guide the design of tasks towards the partial meanings, the identification of primary objects, and the Ontosemiotic analysis of them; 2) the use of mobile devices and Kahoot together motivated the teachers. With these results, two training actions are proposed, aimed at teachers: 1) the design of mobile content from the reference meaning of the natural number, and 2) the assessment of the didactic suitability of teaching experiences mediated by mobile devices.
Learning from Humans for Adaptive Interaction

Robots that will cooperate (or even compete) with humans should understand their goals and preferences. Humans leak and provide a lot of data, e.g., they take actions to achieve their goals, they make choices between multiple options, they use language or gestures to convey information. And we, as humans, are usually very good at using all these available information: we can easily understand what another person is trying to do just by watching them for a while. The goal of my research is to equip robots with the capability of using multiple modes of information sources. For this, I propose using a Bayesian learning approach, and show how it is useful in a variety of applications ranging from exoskeleton gait optimization to traffic routing.
A Blockchain-Based Mutual Authentication Protocol for Smart Home

Smart home is an emerging paradigm that facilitates the user to control remotely appliances and devices from anywhere through the internet connection using a networked device. The privacy-preserving authentication protocol is an important way for the security and privacy of remote access in the smart home. Recently, Lin et al. proposed a blockchain-based mutual authentication (HomeChain) protocol to achieve reliable auditing and anonymous authentication. Their work has drawn wide attention and continues to be widely cited. However, we propose two universal attacks against HomeChain and demonstrate that in Homechain, a malicious home gateway can break the anonymity of users and a malicious authorized user can break the traceability. Meanwhile, to address the above problems, we design a new secure and privacy-preserving blockchain-based authentication protocol by leveraging a secure short group signature scheme. Finally, we conduct extensive experiments and compare our performance with that of HomeChain. The experiment results show that our protocol achieves stronger security and privacy at the expense of a slightly higher computation and communication cost.
A Channel Pruning Optimization With Layer-Wise Sensitivity in a Single-Shot Manner Under Computational Constraints

In the constrained computing environments such as mobile device or satellite on-board system, various computational factors of hardware resource can restrict the processing of deep learning (DL) services. Recent DL models such as satellite image analysis mainly require larger resource memory occupation for intermediate feature map footprint than the given memory specification of hardware resource and larger computational overhead (in FLOP) to meet service-level objective in the sense of hardware accelerator. As one of the solutions, we propose a new method of controlling the layer-wise channel pruning in a single-shot manner that can decide how much channels to prune in each layer by observing dataset once without full pretraining. To improve the robustness of the performance degradation, we also propose a layer-wise sensitivity and formulate the optimization problems for deciding layer-wise pruning ratio under target computational constraints. In the paper, the optimal conditions are theoretically derived, and the practical optimum searching schemes are proposed using the optimal conditions. On the empirical evaluation, the proposed methods show robustness on performance degradation, and present feasibility on DL serving under constrained computing environments by reducing memory occupation, providing acceleration effect and throughput improvement while keeping the accuracy performance.
Predicting cross-barrier communication disruption using adaptive Support Vector Machines

Certain applications may exist where it is necessary to transmit data between two sides of a physical barrier without compromising the structure of the barrier itself. This may be the case when the barrier separates personnel from dangerous conditions or when sensitive electronics are hermetically sealed. One solution is to use mechanical waves to transmit data across the barrier directly. However, this solution is susceptible to new environmental disruptions that can compromise the data stream, such as mechanical vibrations from components in contact with the barrier. Comprehensively evaluating the entire range of expected environmental disruptions can be prohibitive. This work will present the application of Support Vector Machines (SVMs) to adaptively predict environments in which data disruption is expected to occur. The problem is cast as a binary classification problem, for which SVMs are one of the leading machine learning algorithms. The adaptive SVM approach is able to accurately predict disruptive environmental conditions while exploring only a small subset of the entire range. Knowledge of the environmental conditions that lead to data disruption will then enable the design of experiments for validation.SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525
A Cost-Effective and Multi-Source-Aware Replica Migration Approach for Geo-Distributed Data Centers

Geographically distributed data centers have been de-ployed for different purposes, such as minimizing the transmission and response time and the amount of data exchanges throughout the networks. Beyond fault tolerance purposes, data replication has been a popular solution to increase data availability by bringing data closer to the end users. Most existing studies migrate replicas to the desired destinations from a single source, and few of such solutions are cost-aware. By having a multi-source and cost-aware approach, we can accelerate the transmission time resulting in a better quality of service for the end users. Towards that end, this paper introduces a cost-effective and deadline-aware replica migration approach for geo-distributed data centers. The proposed model discovers the appropriate source(s) and paths to transmit the replicas to a desired destination cost-effectively. This problem, which jointly considers cost and deadline, is formulated into a mixed-integer linear programming optimization model. Extensive evaluation against two of the most recent approaches shows significant improvement in meeting the deadlines and reducing the cost incurred to the customers.
Efficient Verilog implementation of Neural Networks for Handwritten Character Recognition

Field Programmable Gate Arrays (FPGAs) have been playing an extremely important role in various industries as it has highly parallel architecture, consumes low power, and is flexible in custom algorithms. For training and implementing the NN (Neural Networks) and also machine learning algorithms, efficiency in terms of power and a parallel architecture is of great help. Instead of having an entire CPU setup ready, a compact FPGA unit consumes less area as well and comes in handy when it comes to compactness. Keeping the importance and benefits of FPGA over a dedicated Computing unit the paper mainly focuses upon the Verilog implementation of Neural Networks for the application in Character recognition system. The system takes inputs from 0 to 9 and tries to detect the character based on the trained neural network. This Neural Network is trained using MNIST dataset in such a way that it should be able to detect the digit from 0–9 with high efficiency.
Building an Escape Room to Raise Awareness of Bullying and Cyberbullying

Aggressive conflicts are part of the natural social behavior of students in schools, which can lead to bullying when there is a form of discrimination or abuse of power, either because of their characteristics or their way of life (sexual orientation, gender identity, nationality, immigration status, ethnicity, sex, socioeconomic status, health, disability, religious beliefs, pregnancy, among others). In the face-to-face mode, the aggressors could be identified and in some way the conflicts were mediated by teachers or authorities of the institution. However, since education changed to a virtual modality caused by the global coronavirus pandemic, new forms of aggressive behavior have emerged with cyberbullying. In this new scenario teachers, parents and law enforcement should take an active role in raising awareness about bullying and cyberbullying. The main objective of this paper is to describe a proposal based on gamification strategy to prevent and raise awareness of aggressive behaviors among students in a virtual environment. The study focuses on three objectives: a) to study the use of visual methods based on microlearning and the techniques to engage students; b) to identify game scenarios and strategies to design an interactive Escape Room; and c) to evaluate the design and content with three experts in psychology. The results indicate that the Escape Room is a different alternative to understand the problems derived from Bullying and cyberbullying. In the future, we are planning to carry out an intervention with students from Ecuador and Colombia to evaluate the impact.
Design of Online Teaching Mode Recognition System for Ideological and Political Curriculum Based on Hash Algorithm

In the practical application process, there are many defects in the online teaching of ideological and political courses, leading to the inability to accurately identify the teaching content. In order to avoid this situation, this paper designs and analyzes the ideological and political course online teaching mode recognition system based on the hash algorithm. In the system hardware section, Design main control circuit and PCB board of teaching mode identification system; In the software part, the teaching recognition function module adopts hash algorithm, designs the multi-core recognition database under the hash algorithm, and then completes the design process of the ideological and political course online teaching pattern recognition system. The final test results show that the recognition rate of the designed system is high, which is more than 90%, indicating that its recognition effect is good, has strong stability and certain practical significance.
Chapter 5: MAP Estimation with Gaussian Priors

Model-based signal processing is not just about accurately modeling data. It is about using these models to extract desired information from the available data. In this chapter, we will introduce the MAP estimator as a general approach to achieving this goal. In order to illustrate the concepts, we will consider the specific application of the restoration of a blurred and noisy image. However, the framework we introduce is quite general and can serve as the basis for solving any linear or nonlinear inverse problem.
Teaching cloud computing at The Technical University of Kosice - design, experiences, and extensions

Over the years, cloud computing has become one of the most dominant computing paradigms. As the market is still growing and even more companies are utilizing cloud computing, it is also necessary to adjust tertiary education to reflect this fact. In this article, the course called “The fundamentals of cloud technologies” is presented. The articles go in-depth on lectures, labs, and projects. The possible extensions to the course and students' feedback are also included. The course has been taught at The Technical University of Kosice for three years.
Overview of Attack Graph Generation For Automotive Systems

Modern vehicles of today integrate new services and advanced functionalities to bring comfort and safety features to the driver and passengers. As these vehicles are connected to external networks, they are similar to an Internet of Things device (IoT). The legacy definition of automotive security focuses on the safety of the vehicle, preventing physical attacks on the vehicle, but like an IOT device, the connected vehicle now has a wide attack surface that enables new, even remote attacks with serious consequences on the safety and security of the driver. Attack graphs are an efficient tool to automatically analyze security vulnerabilities in a system, it’s widely used in the IT domain but not so popular in the Automotive domain because of the high complexity of the automotive network.Currently defining assets and threat scenarios is done manually by experts in the automotive domain, which is a tedious and error-prone process. Increasing the automation of this process is necessary for efficient vehicle security assessment. In this paper, we will discuss The idea of an automated attack graph generation tool for Threat analysis in the automotive domain. We evaluate open-source attack analysis methodologies and frameworks from the IT domain that already provide automation to this critical task, define a general model, and map the concepts to the automotive domain.
Private Data Preprocessing for Privacy-preserving Federated Learning

Privacy-preserving federated learning can accomplish model aggregation without leaking to a local model and avoid the problem of sensitive data exposure caused by model leakage. Even though it protects privacy in the training process, any data analysis task proposed by the initiator and the type of data required for the task contains the research or trade secrets of the organization. Being intercepted in the transmission process or known by other data providers, the disclosure of essential research secrets occurs, leading to the theft of research or business ideas. Therefore, it is a critical issue to achieve data matching between the initiator and the participant under the premise of privacy protection. In this study, we propose a federated learning framework that considers the above security issues. A privacy-preserving federated learning architecture based on homomorphic encryption is designed to protect each participant's data and local model. In addition, encrypted query technology is used in this architecture to provide data privacy matching. The data provider searches the data in ciphertext, finds the encrypted data that meets the conditions, and completes the training process without disclosing any requirements of the task initiator.
Evaluation of numerical earthquake forecasting models

Evaluation of numerical earthquake forecasting models needs to consider two issues of equal importance: the application scenario of the simulation, and the complexity of the model. Criterion of the evaluation-based model selection faces some interesting problems in need of discussion.
Text Classification of Medical Transcriptions using N-Gram Machine Learning Approach

Medical domain is in a data rich environment that a variety of knowledge can be extracted for positive outcomes. This research work will show multiclass classification of medical transcriptions using a real dataset. The objective of this paper is to classify medical transcriptions based on the medical specialty labels, namely Discharge Summary, Neurosurgery and ENT. Text normalisation has performed followed by extracting five different n-gram feature representations are. Moreover, three supervised learning classifiers were trained on each of the n-gram feature representations, namely K-Nearest Neighbours, Decision Tree, and Random Forest. The classification performance was evaluated by the metric score of macro F1. The best score achieved was 0.93 macro F1 on testing set using tuned Random Forest and unigram feature vectors.
Low Bitrate Underwater Video Coding for Object Recognition

In submarine information acquisition, optical imaging is an important way for its ease of understanding by humans and machines. Among all optical pictures, underwater videos are friendly to complicated surveillance tasks such as object tracking and event analytics. However, the videos are still barely used in real-time oceanic surveillance tasks because the underwater video transmission is yet to be well addressed. The popular underwater acoustic channel provides a significantly lower capacity than wired or wireless channels. How to support high-resolution video transmission at such a limited capacity is an open issue. We address this issue by proposing a joint framework of front-end detection and video compression. First, we automatically employ deep neural networks ProposeReduce to automatically identify all target-aware regions. We also designed a mask compensation module to compensate missed areas for semantic consistency across object borders. Second, we deploy a multi-level background smoother on target-free regions to guarantee a low-bitrate background compression without impact on the integrity of targets. Third, we feed all processed frames to a unified video encoder for compression. Experimental results show that our method reduces the bitrate of underwater videos by 58.05%.
Hybrid metaheuristic approach for robot path planning in static environment

General Path Planning (GPP) is a challenging problem in the field of mobile robotics due to its complexity. The robots must selected their path from the starting point to the target point with the lowest possible distance, in the least possible time, and with the fewest possible turns and movements. The aim of this research is to achieve best path planning of a mobile robot using the hybrid algorithm. This paper proposed heuristic algorithms for determining the optimal pathway of the robot in a static environment. These algorithms are the Particle Swarming Optimization (PSO), the Ant Colony Optimization (ACO), and the hybrid approach of ACO&PSO. They used to obtain the perfect path for the robot as well as to avoid hitting obstacles that it encounters through its path. Initially, each of the two algorithms is implemented separately in a static environment, and then the hybrid one is implemented. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The results obtained for the hybrid algorithm were better than the PSO and ACO algorithms.
TIMS: A Novel Approach for Incrementally Few-Shot Text Instance Selection via Model Similarity

Large-scale pre-trained models' demand for high-quality instances forces people to consider how to select instances for annotation with limited resources. Nonetheless, little attention has been paid to the scenario where the number of instances that ultimately need to be annotated is agnostic. Meanwhile, the anisotropy of the sentence vector output by pre-trained models makes it hard to represent the instance itself well. Faced with the two challenges, we propose an incrementally few-shot instance selection approach (TIMS) based on model similarity and outlier detection, which suits the starting step of active learning well and serves as a better benchmark for few-shot learning. Specifically, TIMS determines the representative candidate set by calculating the similarity between changes in model parameters caused by each instance and by the full dataset. Meanwhile, Isolation Forest is adopted to select instances from the candidate set for annotation, which prevents selected instances from being too similar. Comprehensive experiments on WikiLingua & SQuAD show that TIMS outperforms other algorithms across almost every circumstance. It inspires us that the proper implementation of model similarity detection and outlier detection is of great help to select representative instances incrementally.
Unsupervised Domain Adaptive Image Semantic Segmentation Based on Convolutional Fine-Grained Discriminant and Entropy Minimization

Deep convolutional neural networks have made considerable progress in the field of semantic segmentation of images. However, due to inter-domain differences, even modern networks cannot segment test datasets from different domains very well. To reduce and avoid costly annotation of the source domain training data, unsupervised domain adaptation attempts to provide efficient information transfer from the source domain with detailed annotation to the target domain without annotation. However, most existing methods attempt to align the source and target domains from a holistic view, ignoring the underlying class-level structure in the target domain, along with large noise and ambiguity at the class junctions. In this work, we innovatively employ a fine-grained unsupervised domain adaptation semantic segmentation method with increased entropy certainty, and guide the model for finer-grained feature alignment by adversarial learning, while increasing the pixel certainty near the category boundaries. Our approach is easy to implement and we have achieved good results on both the urban road scene datasets GTA5->Cityscapes and SYNTHIA->Cityscapes.
Internal Masked Prefix Sums and Its Connection to Fully Internal Measurement Queries

We define a generalization of the prefix sum problem in which the vector can be masked by segments of a second (Boolean) vector. This problem is shown to be related to several other prefix sum, set intersection and approximate string match problems, via specific algorithms, reductions and conditional lower bounds. To our knowledge, we are the first to consider the fully internal measurement queries and prove lower bounds for them. We also discuss the hardness of the sparse variation in both static and dynamic settings. Finally, we provide a parallel algorithm to compute the answers to all possible queries when both vectors are fixed.