Reviewerly/exHarmony huggingface dataset:
Cubical methods in homotopy type theory and univalent foundations

Abstract Cubical methods have played an important role in the development of Homotopy Type Theory and Univalent Foundations (HoTT/UF) in recent years. The original motivation behind these developments was to give constructive meaning to Voevodsky’s univalence axiom, but they have since then led to a range of new results. Among the achievements of these methods is the design of new type theories and proof assistants with native support for notions from HoTT/UF, syntactic and semantic consistency results for HoTT/UF, as well as a variety of independence results and establishing that the univalence axiom does not increase the proof theoretic strength of type theory. This paper is based on lecture notes that were written for the 2019 Homotopy Type Theory Summer School at Carnegie Mellon University. The goal of these lectures was to give an introduction to cubical methods and provide sufficient background in order to make the current research in this very active area of HoTT/UF more accessible to newcomers. The focus of these notes is hence on both the syntactic and semantic aspects of these methods, in particular on cubical type theory and the various cubical set categories that give meaning to these theories.
Answering to 5W Using Digital Forensics Data

In recent years the increasing capacity of storage devices, the advent of the cloud and the quantity of email, messages, chats, pictures and files exchanged, led the data to be analyzed and correlated by the digital investigators to be hundreds of times bigger than 15 years ago (Darren Quick, K.K.R.C., 2014). To permit a fast visualization of the data, applicable to different types of data and understandable even to non-expert people, we propose a proof of concept of a framework, defined Digital Forensics 5W (DF5W), that create a visualization of the data as answers to the 5W questions: Who, What, When, Where and Why. In order to test this idea, we developed a small prototype and tested it on two case studies: the ENRON dataset and a kidnap case. The results have revealed that answering to 5W using Digital Forensics Data is feasible and their use can display useful information in a narrative way. It is allowing the quick identification of possible groups of data that could need to be analyzed in deep.
A Systematic Security Design Approach for Heterogeneous Embedded Systems

Security has become a significant factor of Internet of Things (IoT) and Cyber Physical Systems (CPS) wherein the devices usually vary in computing power and intrinsic hardware features. It is necessary to use security-by-design method in the development of these systems. This paper focuses on the security design issue about this sort of heterogeneous embedded systems and proposes a systematic approach aiming to achieve optimal security design objective.
Comparison Of Clustering Algorithm In Employee Training Management Recommendations

The implementation of training is one way to develop ahar employees who have the ability according to the times and the organization need. Right now, the training process is inefficient on employee needs and hasn't used the results of employees carried out every year. This condition results in a discrepancy between the capabilities of the employees and the expectations of the organization. With the use of the clustering method for grouping employees based on the assessment criteria each year, it is hoped that it can assist in determining the appropriate type of training. The algorithms used in this study are the K-Means Clustering algorithm and the K-Medoids Clustering algorithm. The attribute components used are discipline, loyalty, work performance, responsibility, obedience, honesty, cooperation, initiative, and leadership. The results of this study indicate that the K-Means and K-Medoids algorithms show the same cluster results for the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{k}=3$</tex> value with the K-Means DBI value being slightly better than the K-Medoids.
The Design of Sensor and Transducer Learning Media based on Augmented Reality

Abstract This study aimed at determining the design stage of the augmented reality-based learning media for sensors and transducers course. This study employed the Research and Development method with the ADDIE model with the stages of analysis, design, development, implementation, and evaluation. This article focuses on the design stage during the process of media development. The developed learning media can be used among vocational students by visualizing the sensor and transducer components like in the real laboratory setting. The design of the developed learning media contained application storyboards and animation storyboards for practicing the six sensors. This augmented reality learning media is designed to support online learning and students’ autonomous learning in the practical course of sensors and transducers.
Blockchain Primer

Blockchain technology has the potential to revolutionize several industries including finance, supply chain and logistics, healthcare, and more. This primer introduces readers to basic development skills to blockchain foundations including blockchain cryptography, the consensus algorithm, and smart contracts. Further, this primer explains stepwise how to implement and deploy basic data stores using blockchain with Python. The primer serves as a succinct introductory guide to blockchain foundations by relying on a case study illustrated with visuals together with instructions on implementation. This primer is intended for educators, students, and technology enthusiasts with foundational computer science and Python development skills.
Improving Performance of CNN Based Vehicle Detection and Tracking by Median Algorithm

The performance of object tracking algorithms depends upon the results of object detectors. If object detection algorithm fails to detect the objects in the image due to occlusion, field of view, and low contrast, then the object tracking algorithm relying on detection cannot generate tracking details for those objects. Thus, we need an additional scheme that can quickly predict tracking details. We can store the previous tracking details (unique ID) and corresponding labels of each object in the history buffer. In case when object detection fails, we can use this history data to predict future labels and minimize tracking errors. In this work, we propose a median based label estimation scheme that predicts detection labels for the objects in the current frame using a median of history labels stored in the previous frames. Median has the property that it does not affect by outliers, which gives a stable value of the past and provides better estimation. Along with the predicted labels, our scheme also alleviates the problem of unstable labels showing flickering bounding boxes, and thus significantly improves the visual quality of object detectors.
Fine-grained Tourist Satisfaction Prediction Based on Deep Learning

Tourist satisfaction analysis is a hot issue in text language processing. This paper analyzes the six dimensions of “ location, service, cost performance, environment, facilities and others” of the destination, divides the six main dimensions into several fine-grained dimensions, transforms the score of tourists on the destination into a fine-grained classification problem, establishes a fine-grained tourist satisfaction prediction based on deep learning, and uses the labeled “AI Challenger 2018” training of the data set has achieved high accuracy. For different main dimensions of comments and fine-grained classification results, the ridge regression and multiple linear regressions are used to fit the scores, and the score results predicted by the model are obtained. The mean square error is used for evaluation, and the predicted scores are basically consistent with the real scores. In order to enhance the Robust of the regression model, the Leave-One-Out is used to evaluate the two regressions. The results show that ridge regression has better prediction results and stronger Robust.
Resource Allocation and Task Offloading in Blockchain-Enabled Fog Computing Networks

The rapid growth of Internet of Things (IoT) applications poses a great challenge to the computation capability of smart mobile equipments (SMEs). Fog Computing, as a promising technology, provides fast computing services for resource-limited SMEs in various applications. In this paper, we consider a blockchain-based fog computing network consisting of SMEs, fog nodes (FNs) and the cloud server. To optimize the delay and energy consumption of processing computation-intensive tasks, two offloading models are introduced, namely, task offloading to the device-to-device (D2D) cooperation group and to a nearby FN. Additionally, the blockchain technology is enabled to prevent malicious nodes from modifying with transaction information by maintaining a continuous tamper-proof ledger database. To reduce the delay and energy consumption of the traditional consensus mechanism, we propose the voting-based delegated proof of stake consensus mechanism, in which the FNs with the top half of votes will form a verification set, and the FNs will take turns being the manager to generate new blocks. Furthermore, to minimize the network cost, we jointly optimize task offloading decision, transmission rata allocation and computing resource allocation under various constraints. Finally, the effectiveness of the proposed scheme is demonstrated.
Modeling and Verifying Ticket-Based Authentication Scheme for IoT Using CSP

Internet of Things (IoT) connects various nodes such as sensor devices. For users from foreign networks, their direct access to the data of sensor devices is restricted because of security threats. Therefore, a ticket-based authentication scheme was proposed, which can mutually authenticate a mobile device and a sensor device. This scheme with new features fills a gap in IoT authentication, but the scheme has not been verified formally. Hence, it is important to study the security and reliability of the scheme from the perspective of formal methods.In this paper, we model this scheme using Communicating Sequential Processes (CSP). Considering the possibility of key leakage caused by security threats in IoT networks, we also build models where one of the keys used in the scheme is leaked. With the model checker Process Analysis Toolkit (PAT), we verify four properties (deadlock freedom, data availability, data security, and data authenticity) and find that the scheme cannot satisfy the last two properties with key leakage. Thus, we propose two improved models. The verification results show that the first improved model can guarantee data security, and the second one can ensure both data security and data authenticity.
Characterizing network performance of single-node large-scale container deployments

Cloud services have shifted from complex monolithic designs to hundreds of loosely coupled microservices over the last years. These microservices communicate via pre-defined APIs (e.g., RPC) and are usually implemented on top of containers. To make the microservices model profitable, cloud providers often co-locate them on a single (virtual) machine, thus achieving high server utilization. Despite being overlooked by previous work, the challenge of providing high-quality network connectivity to multiple containers running on the same host becomes crucial for the overall cloud service performance in this scenario. For that reason, this paper focuses on identifying the overheads and bottlenecks caused by the increasing number of concurrent containers running on a single node, particularly from a networking perspective. Through an extensive set of experiments, we show that the networking performance is mostly restricted by the CPU capacity (even for I/O intensive workloads), that containers can largely suffer from interference originated from packet processing, and that proper core scheduling policies can significantly improve connection throughput. Ultimately, our findings can help to pave the way towards more efficient large-scale microservice deployments.
Vision and Crowdsensing Technology for an Optimal Response in Security: Project results

This paper describes the progress and work carried out during the execution of the VICTORY project (Vision and Crowdsensing Technology for an Optimal Response in Security). It describes both its motivation and the progress made in the field of managing violent attacks in buildings. It is assumed that these buildings have a WiFi infrastructure that allows indoor localisation through mobile applications and a closed-circuit television system that allows real-time images to be captured. The advances of this project have allowed 1) the design of a crowdsensing application that allows indoor localisation in a reliable way and with a low battery consumption as well as the detection of stampedes and falls; 2) the development of a multi-agent model that simulates human behaviour in the face of a violent attack and 3) new approaches to improve the state of the art in computer vision for the detection of weapons.
Some Words About Nature-Inspired Computing

The use of artificial intelligence (AI) in various domains has drastically increased during the last decade. Nature-inspired computing is a strong computing approach that belongs to AI and covers a wide range of techniques. It has successfully tackled many complex problems and outperformed several classical techniques. This chapter provides the original ideas behind some nature-inspired computing techniques and their applications, such as the genetic algorithms, particle swarm optimization, grey wolf optimizer, ant colony optimization, plant propagation algorithm, cuckoo optimization algorithm, and artificial neural networks.
Research on Machine Learning-Based Error Correction Algorithm for Spoken French

In order to overcome the problems of low error capture accuracy and long response time of traditional spoken French error correction algorithms, this study designed a French spoken error correction algorithm based on machine learning. Based on the construction of the French spoken pronunciation signal model, the algorithm analyzes the spectral features of French spoken pronunciation and then selects and classifies the features and captures the abnormal pronunciation signals. Based on this, the machine learning network architecture and the training process of the machine learning network are designed, and the operation structure of the algorithm, the algorithm program, the algorithm development environment, and the identification of oral errors are designed to complete the correction of oral French errors. Experimental results show that the proposed algorithm has high error capture accuracy and short response time, which prove its high efficiency and timeliness.
Zoom: A case study

The paper 'Zoom: a case-study' explores the process of the staging of a hybrid performance that took place in September 2021 in Malta as a response to the Covid scenario. This production was watched online and live, with actors performing, using both realities.&amp;nbsp; The project also explores the notion of space, whether it is the virtual, as opposed to the 'real', and the different spatial dynamics that this performance occurred in. In fact, the performance happened in two 'real' spaces, connected one to the other through an intricate use of screens and cameras, in such a way that whichever way the audience decided to watch the performance in, they could still understand the narrative of the play. The paper is analysed by juxtaposing literature on hybridity against the experiences of the different stakeholders involved in this production, namely, the co-producers, the director (the researcher of this paper), the technical director, the actors and members of the audience, with the aim of analysing and evaluating the dynamics of the production as a model of good practice and discern whether it can provide a framework to work in for the restricted reality that the local industry is in currently, as well as for the future.
Contextual BI-Directional Attention Flow With Embeddings From Language Models: A Generative Approach to Emotion Detection

Detection of Emotions from the text is a tedious task. Presently, existing models failed to detect the emotion in absence of the emotional word in the text. The cause phrase selection which gives a deep insight into emotions is considered to be a tough task. The proposed model for detecting emotions is developed through seven layers. Initially, the dataset is represented in the Topical documents using Adversarial Topic Modelling (ATM). Convolutional Neural Network (CNN) maps each phrase in the topical document to Higher-dimensional vectors, followed by the ELMo Model to obtain the fixed word Embeddings vectors. LSTM is responsible for making the interaction between the words in word embeddings and produces the context and query vectors. The bi-directional Attention flow layer determines the most relevant similarity between Context and Query. Finally, Robustly Optimized BERT (RoBERT) architecture is used to detect the Emotion. It is noted that the proposed multi-stage model detects better emotions than all the existing state-of-art models for detecting emotions.
Factors That Influence Challenges and Opportunities of Cryptocurrencies in Developing Countries

Blockchain is a technology that uses structure in storage. One of the products of blockchain technology is cryptocurrency. Cryptocurrency is an innovation in the fintech field where cryptography-based digital currency can provide various advantages for the community. The problem of cryptocurrencies in developing countries is scalability, security issues, and price volatility. The main advantage with cryptocurrency is low fees and can provide transparency and security of the identity of the owner. This paper is using a systematic literature review on conducting research. The main advantage of this paper is researching the development of blockchain technology that focuses on developing countries where most of the research are conducted for blockchain technology is in developed countries. Cryptocurrencies can help developing countries make various transactions using only smartphones without third parties. However, it is necessary to have sufficient knowledge in using cryptocurrencies because there is still no government regulation, high volatility, and security issues.
TPLI:A Traceable Privacy-preserving Logistics Information Scheme via Blockchain

The logistics information management system has been widely used, due to its high internal operation efficiency, the visualization of package transportation, and the real-time sharing of information between logistics companies and customers. However, personal and logistics privacy preservation is still an issue that needs to be resolved in the logistics process. In addition, the opacity and non-traceability of logistics information reduce the security and availability of the system. We propose a blockchain-based logistics privacy-preserving scheme to ensure that personal and logistics information is auditable and traceable. We implemented the prototype system with the Hyperledger Fabric platform and verified the scheme’s feasibility through performance evaluation.
Information and communications technologies in library facilities in disadvantaged rural schools in South Africa: Lessons from Limpopo province

Universally, information and communications technologies (ICTs) have revolutionised multiple ways of executing tasks in many sectors. In the education sector, ICTs provide a scaffold to enhance technology-driven teaching and learning information needs of the teachers and learners in a school environment. The aim of the study was to investigate the availability and use of ICTs in library facilities in primary schools in disadvantaged rural communities in Limpopo province, South Africa. The study targeted all 18 primary schools in Lebopo Circuit of Mankweng Cluster. Self-administered questionnaires were used to collect data from teacher-librarians who attended a school library workshop at a local high school. Purposive sampling was employed in the selection of the teacher-librarians and all 18 schools were represented by one teacher-librarian. The findings indicate that there are few ICTs used by the teachers to enhance teaching and learning in different library facilities in disadvantaged rural schools. The study recommends that additional ICTs should be procured by the Department of Basic Education (DBE) for distribution to all schools to mitigate technology-driven information needs of teachers and learners. Furthermore, as vandalism was cited as a challenge in all schools, security needs upgrading to protect the few available ICTs.
Physical and Physiological Data for Customizing Immersive VR Training

The assessment of Virtual reality (VR) applications and serious games often relies on measures of usability, engagement, motion sickness, and cognitive and user performance to determine how the experience was perceived and whether the learning outcomes were met. In addition, physical and physiological information is captured to develop an understanding of behavioral patterns that can help improve the user experience. However, the acquisition of physiological information requires high-end equipment typically exclusive to industry and research institutions, a scenario that is changing as consumer-level VR technology, open electronics, and Makerspace are becoming more readily available. However, VR technology remains exclusive as hardware and interactions assume a one-size- fits-all approach with little customization that accounts for the inherent high user variability. While efforts are currently underway to make VR more inclusive and accessible, the solutions focus on specific user needs. This paper presents the prototyping of a framework consisting of three subsystems for factoring of upper limb ergonomics, skin response, and muscle activity, and gaze tracking as metrics to assist in VR task completion. Due to the preliminary nature of this work, we present a discussion on what we have learned so far through the development of these subsystems applied in three use cases.
Construction of University Online Examination System Based on Cloud Computing Technology

With cloud computing's powerful computing power, many end users can create a variety of effective network applications using the cloud's services without having to worry about computing technology or access methods. Based on CC technology's on-demand service characteristics and unlimited dynamic expansion capability, this article designs and implements a shared network examination system. In the Web mode, the functions of receiving and distributing examination data, identity verification, online examination, and examination result collection can be realized using the SaaS deployment structure, MVC three-tier architecture, Java modeling language, XFIE, JSON, web service, DES, and other technologies combined with MySQL database. At the same time, the improved parallel genetic annealing algorithm (IPGAA) is proposed as a CC resource scheduling strategy. The IPGAA has better adaptability in the CC system with various cloud resources because it combines the fast global search ability of the genetic algorithm (GA) with the local search ability of the simulated annealing algorithm. Simulation tests show that the IPGAA is effective.