Reviewerly/exHarmony huggingface dataset:
Effects of Algorithmic Transparency in Bayesian Knowledge Tracing on Trust and Perceived Accuracy

Knowledge tracing algorithms such as Bayesian Knowledge Tracing (BKT) can provide students and teachers with helpful information about their progress towards learning objectives. Despite the popularity of BKT in the research community, the algorithm is not widely adopted in educational practice. This may be due to skepticism from users and uncertainty over how to explain BKT to them to foster trust. We conducted a pre-registered 2x2 survey experiment (n=170) to investigate attitudes towards BKT and how they are affected by verbal and visual explanations of the algorithm. We find that ostensible learners prefer BKT over a simpler algorithm, rating BKT as more trustworthy, accurate, and sophisticated. Providing verbal and visual explanations of BKT improved confidence in the learning application, trust in BKT and its perceived accuracy. Findings suggest that people's acceptance of BKT may be higher than anticipated, especially when explanations are provided.
Knowledge-Grounded Dialogue with Reward-Driven Knowledge Selection

Knowledge-grounded dialogue is a task of generating a fluent and informative response based on both conversation context and a collection of external knowledge, in which knowledge selection plays an important role and attracts more and more research interest. However, most existing models either select only one knowledge or use all knowledge for responses generation. The former may lose valuable information in discarded knowledge, while the latter may bring a lot of noise. At the same time, many approaches need to train the knowledge selector with knowledge labels that indicate ground-truth knowledge, but these labels are difficult to obtain and require a large number of manual annotations. Motivated by these issues, we propose Knoformer, a dialogue response generation model based on reinforcement learning, which can automatically select one or more related knowledge from the knowledge pool and does not need knowledge labels during training. Knoformer is evaluated on two knowledge-guided conversation datasets, and achieves state-of-the-art performance.
Learning a perceptual manifold with deep features for animation video resequencing

We propose a novel deep learning framework for animation video resequencing. Our system produces new video sequences by minimizing a perceptual distance of images from an existing animation video clip. To measure perceptual distance, we utilize the activations of convolutional neural networks and learn a perceptual distance by training these features on a small network with data comprised of human perceptual judgments. We show that with this perceptual metric and graph-based manifold learning techniques, our framework can produce new smooth and visually appealing animation video results for a variety of animation video styles. In contrast to previous work on animation video resequencing, the proposed framework applies to wide range of image styles and does not require hand-crafted feature extraction, background subtraction, or feature correspondence. In addition, we also show that our framework has applications to appealingly arrange unordered collections of images.
Customer data privacy protection method based on singular value decomposition clustering algorithm

As the information technology develops rapidly, the large-scale personal data such as sensors or IoT (Internet of Things) equipment is kept in the cloud or data centers. Sometimes, the data owner in cloud center needs to publish the data. Therefore, in the face of the risk of personal information leakage, how to take full advantage of data has become a hot research topic. When data is published many times, personal privacy is also disclosed. Thus, this paper puts forward a new clustering algorithm based on singular value decomposition to finish the clustering process. The ideas of distance and information entropy are considered to flexibly adjust data availability and privacy protection in this way. Secondly, this paper also puts forward a dynamic update mechanism to ensure that personal data will not be leaked after multiple releases and minimize information loss. Finally, the effectiveness and superiority of this method are verified by experiments.
Construction of the Network Online Teaching System Based on the Internet Technology

Under the environment of the reform of education informationization, teaching gradually as diversification, personalization, intelligent transformation, education concept of constantly updated, prompting the advanced technique such as multimedia, computer is widely used in the field of teaching, network teaching this breakthrough the limitation of time and space of the teaching mode, be used in colleges and universities. This paper mainly studies the online teaching system based on Internet technology. This paper analyzes the technical architecture and application deployment architecture of the system in detail, describes the overall functional modules of the system as a whole, and analyzes the display layer, business layer and data layer in the system hierarchically. And the system performance test and application evaluation, from the test results can be known that the system is very good to achieve the needs of users, its overall performance to achieve the design goal of the system.
Design of an Authorized Digital Signature Scheme for Sensor Network Communication in Secure Internet of Things

With the rapid development of Internet of Things technology and sensor networks, large amount of data is facing security challenges in the transmission process. In the process of data transmission, the standardization and authentication of data sources are very important. A digital signature scheme based on bilinear pairing problem is designed. In this scheme, by signing the authorization mechanism, the management node can control the signature process and distribute data. The use of private key segmentation mechanism can reduce the performance requirements of sensor nodes. The reasonable combination of timestamp mechanism can ensure the time limit of signature and be verified after the data is sent. It is hoped that the implementation of this scheme can improve the security of data transmission on the Internet of things environment.
A Semi-automatic Data Extraction System for Heterogeneous Data Sources: a Case Study from Cotton Industry

With the recent developments in digitisation, there are increasing number of documents available online. There are several information extraction tools that are available to extract information from digitised documents. However, identifying precise answers to a given query is often a challenging task especially if the data source where the relevant information resides is unknown. This situation becomes more complex when the data source is available in multiple formats such as PDF, table and html. In this paper, we propose a novel data extraction system to discover relevant and focused information from diverse unstructured data sources based on text mining approaches. We perform a qualitative analysis to evaluate the proposed system and its suitability and adaptability using cotton industry.
WITHDRAWN: Healthcare 4.0: Recent advances and futuristic research avenues

This article has been withdrawn at the request of the editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
Multi-faceted cloud portability with a TOSCA-based orchestrator

As cloud growth and migration to cloud computing accelerate, the number of competing cloud providers also increases. With the proliferation of heterogeneous cloud resources and services, moving applications between cloud providers can be beneficial as well as necessary, though achieving cloud portability is far from straightforward due to incompatible proprietary interfaces, platforms and services. This paper proposes the adoption of the TOSCA standard to promote cloud portability in TORCH, a framework that leverages TOSCA-compliant Application Description Templates (ADTs) as an abstraction layer for cloud provisioning and application deployment. The generic design of these templates, along with the extensible, modular architecture of TORCH, enables portability between different container platforms and cloud providers. Validation tests conducted on a small-scale test-bed prove the viability of the proposed approach.
A Low-resolution infrared thermal dataset and potential privacy-preserving applications

This paper presents a low-resolution infrared thermal dataset of people and thermal objects, such as a working laptop, in indoor environments. The dataset was collected by a far infrared thermal camera (32x24 pixels), which can capture the position and shape information of thermal objects without privacy issues that enable trustworthy computer vision applications. The dataset consists of 1770 thermal images with high-quality annotation collected from an indoor room with around 15°C. We implemented a privacy-preserving human detection method and trained a multiple object detection (MOD) model based on the dataset. The human detection method reaches 90.3% accuracy. On the other hand, the MOD model achieved 56.8% mean average precision (mAP). Researchers can implement interesting applications based on our dataset, for example, privacy-preserving people counting systems, occupancy estimation systems for smart buildings, and social distance detectors.
Voronoi-based Multi-robot Collision Avoidance with Localization Uncertainty

Multi-robot collision is a subject about different robots in the same environment and this environment has some border. This article introduces a multi-robot collision prevention strategy in the light of the V oronoi Diagram. Specifically, the proposed distributed algorithm incorporates the buffered V oronoi diagram with deadlock avoidance scheme to allow a team of robots to navigate to their goals safely and efficiently even when robots suffer from localization error in real-world scenarios. We validate our method based on simulations to demonstrate that the robots can effectively avoid collision and deadlock while navigating. These results shed light for increasing the safety in Muti-robot Collision with location uncertainty problem.
Secure k-Anonymization over Encrypted Databases

Data protection algorithms are becoming increasingly important to support modern business needs for facilitating data sharing and data monetization. Anonymization is an important step before data sharing. Several organizations leverage on third parties for storing and managing data. However, third parties are often not trusted to store plaintext personal and sensitive data; data encryption is widely adopted to protect against intentional and unintentional attempts to read personal/sensitive data. Traditional encryption schemes do not support operations over the ciphertexts and thus anonymizing encrypted datasets is not feasible with current approaches. This paper explores the feasibility and depth of implementing a privacy-preserving data publishing workflow over encrypted datasets leveraging on homomorphic encryption. We demonstrate how we can achieve uniqueness discovery, data masking, differential privacy and k-anonymity over encrypted data requiring zero knowledge about the original values. We prove that the security protocols followed by our approach provide strong guarantees against inference attacks. Finally, we experimentally demonstrate the performance of our data publishing workflow components.
Why One Should Learn Ethical Hacking

This chapter presents the importance of learning hacking techniques by each and every person dealing with cyber operations. The chapter explains various basic terminologies used in the ethical hacking domain and also provides step-by-step instructions for setting up an ethical hacking lab. The chapter also reveals the legal issues with the ethical hacking domain by providing details of existing cyber laws, acts, and regulations framed by various countries in order to deal with the harmful hacking activities and cybercrimes.
A visual tool for reducing returns in e-commerce platforms

Nowadays, the number of people who prefer to make online purchases on e-commerce platforms is constantly increasing. Online shopping turns out to be fast and cheap but it also involves some risks. In fact, it sometimes happens that a product, once received, is smaller or bigger than expected, or does not comply with what is described on the purchase site. Typically this happens because the information provided by retailers is not detailed and, moreover, the images usually show the product on a white background without any reference nearby to understand its true dimensions. To cope with this problem, we developed CompareDimensions, a software tool that helps customers during their online purchases, by allowing them to understand the correct dimensions of the product they are about to buy. Through this tool, a customer can see the images of the product and compare it with a series of reference images known to all, such as a man, a chair, a door or a child. The experimental evaluation, conducted on Amazon, shows that the use of the proposed tool helped people to figure out the real dimensions of products, with respect to the case in which only cutout images and textual descriptions are provided. In particular, according to our experiments, people who used the tool were able to understand the real dimensions of a product with 17.5% greater accuracy than those who did not use the tool. Focusing on critical products, average accuracy increased by 24%, with an improvement of 27% and 11% for products that proved to be smaller or larger than expected. Therefore, the adoption of CompareDimensions could help customers to understand the real dimensions of the products they want to buy, and consequently reduce the likelihood of a return.
SA-HAVE: A Self-Attention based Hierarchical VAEs Network for Abstractive Summarization

Abstract The abstractive automatic summarization task is to summarize the main content of the article with short sentences, which is an important research direction in natural language generation. Most abstractive summarization models are based on sequence-to-sequence neural networks. Specifically, they encode input text sequences by Bi-directional Long Short-Term Memory (bi-LSTM), and decode summaries word-by-word by LSTM. However, existing models usually did not consider both the self-attention dependence during the encoding process using bi-LSTM, and deep potential sentence structure information for the decoding process. To tackle these limitations, we propose a Self-Attention based word embedding and Hierarchical Variational AutoEncoders (SA-HVAE) model. The model first introduces self-attention into LSTM to alleviate information decay of encoding, and accomplish summarization with deep structure information inference through hierarchical VAEs. The experimental results on the Gigaword and CNN/Daily Mail datasets validate the superior performance of SA-HVAE, and our model has a significant improvement over the baseline model.
Conceptual Model for a Shared Cybersecurity Operations Center for ICS

The article offers an analysis of the scope of a conceptual model for a Shared Industrial Control Systems (ICS) Cybersecurity Operations Center (SCOC). It ensures continuous proportional ICS protection against malicious actors and in accordance with regulations and standards. The focus of the research is on the three basic functions of the cybersecurity cycle – proactive functions, security operations management, and cybersecurity incident response. The aim of the study is to increase the effectiveness of cybersecurity for all Shared SOC subscribers, to overcome the problem of staff shortages, and allow each company to focus on its core business. The proposed analysis provides reference points for synthesizing a private solution for multiple clients of Shared SOC, a basis for the concept of operations and preparation for technical design. Shared SOC provides a powerful tool for meeting the requirement for multi-layered cyber protection, which is already a fact in many regulatory measures. Shared SOC creates conditions for all subscribers with ICS to increase their level of maturity in cybersecurity. Our contribution is in determining the meta scope of operations performed by the SCOC team.
An Improved Method for Correcting the Eccentricity Error of Circular Projection in the Binocular Visual System

To correct the eccentricity error in the circular projection, this paper proposes an improved algorithm to compensate for various eccentricity errors in the circular projection by using geometric features with the projection model of the binocular vision system. The experiment indicates that our method performs better than the traditional method.
A Power-efficient Implementation of SHA-256 Hash Function for Embedded Applications

SHA-256 is a well-known algorithm widely used in many security applications. The algorithm provides a sufficient level of safety and can be performed efficiently by FPGA devices due to its high parallelism level. This paper presents a high-throughput, low hardware resources usage, and power-efficiency architecture of the SHA-256 algorithm targeting FPGA-based embedded platforms. The SHA-256 computing core takes advantage of the specific architecture of FPGA to achieve high performance. We implement the SHA-256 computing core with hardware description languages so that the computing core is technology-independent. Therefore, the computing core is suitable for building applications with various FPGA-based platforms. We conduct several experiments with both simulation and SoC boards. The experimental results show that the core achieves the same functionality, performance, and power consumption when implemented on different FPGA families. The implemented system with our SHA-256 computing core can function at 139.04 MHz, achieving a bandwidth of up to 1.04 Gbps. The SHA-256 computing core is power-efficient when consuming only 0.072 W with the minimum configuration.
Human Activity Recognition based on Smartphone Sensors- A Comparative Study

HAR has become a leading area of research because of its noteworthy contribution in applications that aim to improve the quality and standard of life. HAR system also contributes to health and safety in smart cities, privacy and security, etc., which directly or indirectly improves the quality of service towards society. In this study, we studied the different techniques used for the detection of human activities using built-in sensors in smartphones. In all these techniques raw data is collected using gyroscope and accelerometer sensors inbuilt in the smartphones and then different data preprocessing steps are implemented to clean the data. Important features are extracted using different feature extraction techniques. Finally, the “Machine Learning” or “Deep Learning” models are trained which can accurately recognise the activities. We analyze several modern deep learning techniques which provide excellent results due to their capability of learning deep features. Also, we have analyzed the research gaps in the current literature which provides a sound understanding to identify the future work required in this area of research.
Nobili on Mobile! When the scientific instruments exhibitions are at your fingertips

This paper presents a general view of a proposal for a digital reinterpretation of a collection of scientific instruments belonging to the Physics Cabinet of the Science Museum of the University of Coimbra. In this cataloging, the local and global aspects of each instrument are inventoried and represented by a semantic network of concepts, facts, ideas, and narratives, resulting in a knowledge base about scientific physics instruments. This knowledge base will be made available to students, researchers, and the general public through a mobile phone application. The article also offers a review of the transformations of the conceptual models of material culture studies related to scientific instruments and adds some contributions to this field of study.
Research of Network Traffic Classification Based on Ensemble Learning

In order to improve the accuracy and generalization ability of network traffic classification model, this thesis proposes an ensemble learning model which combining three different kinds of model classifiers namely Logistic Regression(LR), Support Vector Machine(SVM) and K-Nearest Neighbors (KNN). In the integration model, LR, SVM and KNN are used to classify the network traffic separately, and then the classification results of the three different kinds of classifier are used to make the final prediction of the traffic category through Majority Voting ensemble learning. Compared with the single classification model, the ensemble learning model has better accuracy and generalization ability.
Cross Reality in Crisis Management

The continuous technological advancement of computer simulation, display technology, and the internet of things leads to the opportunities to use cross-reality (XR) technologies in crisis management. XR emphasizes the compositions of different concepts in reality-virtuality continuum under a shared online virtual world, including virtual reality (VR), augmented reality (AR), and mixed reality (MR). It is touted as a promising tool to facilitate crisis management strategies in different stages, including prevention, onsite management, and recovery. This research contributes to the field of research in VR, XR, and crisis management (an essential component of healthcare) in the following four ways: (1) It proposes an idea to apply XR in crisis management. (2) It proposes a framework to connect VR, AR, and MR serving one purpose. (3) It presents a qualitative study to examine the user perception of the XR-based crisis management method. (4) It brings out the challenges and opportunities of using XR in crisis management.
N-Gram and K-Nearest Neighbor Algorithm for Sentiment Analysis on Capital Relocation

The plan to move the capital city of Jakarta to Kalimantan is an issue that has attracted public attention. Various opinions were expressed by the public to respond to the issue of the discourse of moving the capital city. For this reason, we conducted a sentiment analysis to find out public sentiment on the discourse of moving the capital city. The analysis is carried out by classifying public opinion on Twitter social media based on positive, negative, and neutral sentiments. The algorithm we use in this study is a k-nearest neighbor. We also use N-grams in this study as a selection feature. The results of this study show that using a system architecture consisting of the k-nearest neighbor algorithm with a value of k&#x003D;1 and the bigram section feature the highest accuracy is 66&#x0025; with the lowest error rate of 34&#x0025;. F-measure reaches 87&#x0025;, precision is 97&#x0025; and recall is 80&#x0025; compared to other parameters used.
Facial Emotion Recognition Based On Sobel-Resnet

In recent years, facial expression recognition has gained a growing attention in the field of pattern recognition research. Now many previous researches use deeper model or model integration to improve the recognition rate. For these models are more difficult to train and traditional methods have optimizable recognition accuracy, in this paper, we present a novel single network which combines the classic Resnet with the sobel features which can emphasize edge information effectively. We also adopt 10-crop testing to further improve the performance. The model was tested on the CK+ database and FER2013 database and we observed 95.96% and 71.89% classification accuracy respectively. The results showed that the method achieved better or comparable recognition rate with other state-of art methods.