Reviewerly/exHarmony huggingface dataset:
Improved ant colony algorithm for path planning of mobile robots based on compound prediction mechanism

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and “reward or punishment” update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the “deadlock” problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved.
Recurrent neural network based sentiment analysis of social media data during corona pandemic under national lockdown

The Corona virus pandemic has affected the normal course of life. People all over the world take the social media to express their opinions and general emotions regarding this phenomenon. In a relatively short period of time, tweets about the new Corona virus increased by an amount never before seen on the social networking site Twitter. In this research work, Sentiment Analysis of Social Media Data to Identify the Feelings of Indians during Corona Pandemic under National Lockdown using recurrent neural network is proposed. The proposed method is analyzed using four steps: that is Data collection, data preparation, Building sentiment analysis model and Visualization of the results. For Data collection, the twitter dataset are collected from social networking platform twitter by application programming interface. For Data preparation, the input data set are pre-processed for removing URL links, removing unnecessary spaces, removing punctuations and numbers. After data cleaning or preprocessing entire particular characters and non-US characters from Standard Code for Information Interchange, apart from hash tag, are extracted as refined tweet text. In addition, entire behaviors less than three alphabets are not assumed at analysis of tweets, lastly, tokenization and derivation was carried out by Porter Stemmer to perform opinion mining. To authenticate the method, categorized the tweets linked to COVID-19 national lockdown. For categorization, recurrent neural method is used. RNN classify the sentiment classification as positive, negative and neutral sentiment scores. The efficiency of the proposed RNN based Sentimental analysis classification of COVID-19 is assessed various performances by evaluation metrics, like sensitivity, precision, recall, f-measure, specificity and accuracy. The proposed method attains 24.51%, 25.35%, 31.45% and 24.53% high accuracy, 43.51%, 52.35%, 21.45% and 28.53% high sensitivity than the existing methods.
Designing an Augmented Experience for a Music Archive: What does the Audience Need Beyond the Sense of Hearing?

The ARIA project aims to create an engaging visitor experience for archives of music Cultural Heritage that targets the wider public. The challenge is to conceptualize an experience design where the intangible seamlessly meets the tangible, bridging the gap between obscure archival material and the direct and emotional experience of listening to music. The project will apply innovative technologies, such as eXtended Reality (XR) and collaborative digital storytelling, to augment the reception of the musical experience through establishing an affective connection of the visitor with the music composer. In this work we present the results of a user study with 198 participants, designed to assess user expectations and identify concrete needs to inform a multimodal experience design about the composer N. Skalkottas, The results of this study guided the design of an experience which combines a single user immersive VR application to promote reflection on the music piece, with a group participatory digital storytelling mobile app to establish an affective connection with the composer. We conclude the paper with our insights on the effectiveness of the approach as well as how the experience design could contribute to further studying and understanding in a holistic way the factors that play a role in feeling moved by or connected with a composer and his work.
Practical Saccade Prediction for Head-Mounted Displays: Towards a Comprehensive Model

Eye-tracking technology has started to become an integral component of new display devices such as virtual and augmented reality headsets. Applications of gaze information range from new interaction techniques that exploit eye patterns to gaze-contingent digital content creation. However, system latency is still a significant issue in many of these applications because it breaks the synchronization between the current and measured gaze positions. Consequently, it may lead to unwanted visual artifacts and degradation of the user experience. In this work, we focus on foveated rendering applications where the quality of an image is reduced towards the periphery for computational savings. In foveated rendering, the presence of system latency leads to delayed updates to the rendered frame, making the quality degradation visible to the user. To address this issue and to combat system latency, recent work proposes using saccade landing position prediction to extrapolate gaze information from delayed eye tracking samples. Although the benefits of such a strategy have already been demonstrated, the solutions range from simple and efficient ones, which make several assumptions about the saccadic eye movements, to more complex and costly ones, which use machine learning techniques. However, it is unclear to what extent the prediction can benefit from accounting for additional factors and how more complex predictions can be performed efficiently to respect the latency requirements. This paper presents a series of experiments investigating the importance of different factors for saccades prediction in common virtual and augmented reality applications. In particular, we investigate the effects of saccade orientation in 3D space and smooth pursuit eye-motion (SPEM) and how their influence compares to the variability across users. We also present a simple, yet efficient post-hoc correction method that adapts existing saccade prediction methods to handle these factors without performing extensive data collection. Furthermore, our investigation and the correction technique may also help future developments of machine-learning-based techniques by limiting the required amount of training data.
A Multi-type Classifier Ensemble for Detecting Fake Reviews Through Textual-based Feature Extraction

The financial impact of online reviews has prompted some fraudulent sellers to generate fake consumer reviews for either promoting their products or discrediting competing products. In this study, we propose a novel ensemble model—the Multi-type Classifier Ensemble (MtCE) —combined with a textual-based featuring method, which is relatively independent of the system, to detect fake online consumer reviews. Unlike other ensemble models that utilise only the same type of single classifier, our proposed ensemble utilises several customised machine learning classifiers (including deep learning models) as its base classifiers. The results of our experiments show that the MtCE can adequately detect fake reviews, and that it outperforms other single and ensemble methods in terms of accuracy and other measurements for all the relevant public datasets used in this study. Moreover, if set correctly, the parameters of MtCE, such as base-classifier types, the total number of base classifiers, bootstrap, and the method to vote on output (e.g., majority or priority), can further improve the performance of the proposed ensemble.
Quantum optical communication in the presence of strong attenuation noise

Is quantum communication possible over an optical fibre with transmissivity $\lambda\leq 1/2$ ? The answer is well known to be negative if the environment with which the incoming signal interacts is initialised in a thermal state. However, in [PRL 125:110504, 2020] the quantum capacity was found to be always bounded away from zero for all $\lambda>0$, a phenomenon dubbed "die-hard quantum communication" (D-HQCOM), provided that the initial environment state can be chosen appropriately (depending on $\lambda$). Here we show an even stronger version of D-HQCOM in the context of entanglement-assisted classical communication: entanglement assistance and control of the environment enable communication with performance at least equal to that of the ideal case of absence of noise, even if $\lambda>0$ is arbitrarily small. These two phenomena of D-HQCOM have technological potential provided that we are able to control the environment. How can we achieve this? Our second main result answers this question. Here we provide a fully consistent protocol to activate the phenomena of D-HQCOM without directly accessing the environment state. This is done by sending over the channel "trigger signals", i.e. signals which do not encode information, prior to the actual communication, with the goal of modifying the environment in an advantageous way. This is possible thanks to the memory effects which arise when the sender feeds signals separated by a sufficiently short temporal interval. Our results may offer a concrete scheme to communicate across arbitrarily long optical fibres, without using quantum repeaters. As a by-product of our analysis, we derive a simple Kraus representation of the thermal attenuator exploiting the associated Lindblad master equation.
Clustered federated learning multi-classifier for non-IID scenario in drone devices

Existing machine learning technology lacks effective support for practical drone devices in target recognition. The Clustered Federated Learning Algorithm (C-FLA) was proposed to enhance the joint training in drone groups. The algorithm considered the path fading and Rayleigh fading when the user side performs upstream data transmission. And it designed a federation learning method based on cluster-based training. The results showed that the traditional federal learning algorithm and C-FLA can converge to similar values under the same training conditions in the case of good channel state and small user transmit power constraints, and C-FLA converges faster. C-FLA can reduce the convergence value of the loss function by 10%-50% compared to the traditional centralized algorithm. It can be seen that C-FLA is more helpful for model training in multi-classifier scenarios.
SWEBOK Matters: Report and Reflection of a SEKE Panel on the Educational and Professional Implications of SWEBOK

This paper provides a summary of the proceedings of a panel on the Guide to the Software Engineering Body of Knowledge (SWEBOK) held under the auspices of the Thirty-Fourth International Conference on Software Engineering and Knowledge Engineering (SEKE 2022), as well as the discussion that ensued thereafter among the panelists. In this regard, a synopsis of the underlying motivation and structure of SWEBOK is given, and the means for integrating SWEBOK in software-intensive organizations and educational institutions offering software engineering-related courses are highlighted and illustrated by pedagogically-oriented examples.
Co-location and air pollution exposure

Location privacy has long been studied, in order to protect users' location data from untrusted servers. While existing research analyzed location privacy methods with generic utility measures, the lack of application-oriented perspectives imposes challenges for adopting location privacy. This study fills the gap by putting application utility front and center, studying the impacts of location privacy in two concrete case studies. We conduct empirical evaluations with real-world datasets from two large cities, and provide in-depth analysis on the obtained results. Furthermore, we examine the relationship between generic utility and application utility as well as the trade-off between privacy and utility in specific application settings. Our results point out interesting behaviors of the studied privacy methods and can help applications with location privacy decision-making.
Prediction of motion sickness degree of stereoscopic panoramic videos based on content perception and binocular characteristics

The immersive viewing environment of Virtual Reality (VR) system inevitably brings motion sickness to users, and restricts the development of VR system to a certain extent. In order to solve the problem of motion sickness when users view, it is necessary to predicate the motion sickness degree of the visual content. However, the methods based on subjective questionnaires or physiological signals will become cumbersome and infeasible with the growth of demand and supply of VR content. Most of the existing prediction methods for motion sickness are designed for 2D panoramic video. In this paper, a motion sickness prediction method based on content perception and binocular characteristics is proposed for stereoscopic panoramic video. The proposed method mainly consists of two modules: feature extraction based on content analysis and time pooling. The feature extraction module takes into account the attention mechanism of human visual system and the multi-channel characteristics of retina, and simulates the sensory conflict and the three-stage process of binocular stereoscopic perception in virtual environment. The time pooling module takes into account the time memory effect of human eyes. The proposed method has achieved excellent prediction performance on stereoscopic panoramic video comfort database SPVCD, its predication results have a good correlation with the subjective scores, which shows that the proposed method can effectively predict the motion sickness degree of VR content.
Building Knowledge Graphs and Recommender Systems for Suggesting Reskilling and Upskilling Options from the Web

As advances in science and technology, crisis, and increased competition impact labor markets, reskilling and upskilling programs emerged to mitigate their effects. Since information on continuing education is highly distributed across websites, choosing career paths and suitable upskilling options is currently considered a challenging and cumbersome task. This article, therefore, introduces a method for building a comprehensive knowledge graph from the education providers’ Web pages. We collect educational programs from 488 providers and leverage entity recognition and entity linking methods in conjunction with contextualization to extract knowledge on entities such as prerequisites, skills, learning objectives, and course content. Slot filling then integrates these entities into an extensive knowledge graph that contains close to 74,000 nodes and over 734,000 edges. A recommender system leverages the created graph, and background knowledge on occupations to provide a career path and upskilling suggestions. Finally, we evaluate the knowledge extraction approach on the CareerCoach 2022 gold standard and draw upon domain experts for judging the career paths and upskilling suggestions provided by the recommender system.
Lightweight and Secure IoT-Based Payment Protocols from an Identity-Based Signature Scheme

After the great success of mobile wallets, the Internet of Things (IoT) leaves the door wide open for consumers to use their connected devices to access their bank accounts and perform routine banking activities from anywhere, anytime, and with any device. However, consumers need to feel safe when interacting with IoT-based payment systems, and their personal information should be protected as much as possible. Unlike what is usually found in the literature, in this paper, we introduce two lightweight and secure IoT-based payment protocols based on an identity-based signature scheme. We adopt a server-aided verification technique to construct the first scheme. This technique allows to outsource the heavy computation overhead on the sensor node to a cloud server while maintaining the user’s privacy. The second scheme is built upon a pairing-free ECC-based security protocol to avoid the heavy computational complexity of bilinear pairing operations. The security reduction results of both schemes are held in the Random Oracle Model (ROM) under the discrete logarithm and computational Diffie–Hellman assumptions. Finally, we experimentally compare the proposed schemes against each other and against the original scheme on the most commonly used IoT devices: a smartphone, a smartwatch, and the embedded device Raspberry Pi. Compared with existing schemes, our proposed schemes achieve significant efficiency in terms of communication, computational and storage overheads.
Correlation operation in the Fresnel domain and truncation operations applied to the simultaneous encryption of two images

Abstract A new nonlinear encryption-decryption scheme for two images using: double random phase encoding (DRPE), random phase masks (RPMs), amplitude and phase encoding, correlation operation in Fresnel domain (FrD) and truncation operations (amplitude truncation, AT and phase truncation, PT), is proposed. AT and PT are operations that work on complex-valued functions (images) in a nonlinear way, with the purpose of selecting the information contained in a complex-valued image, respectively. The first step of the encryption scheme is encoding two original images in amplitude and phase. The encryption or decryption scheme utilizes the correlation operation in FrD, AT, PT and two RPMs in a specific way to obtain the encrypted or decrypted images. The use of the AT and PT operations allow a better security of the encryption and decryption schemes because the nonlinearities introduced by these operations and the generation of two new encryption keys. The encrypted image of this proposal is real valued and the correlation operation in FrD introduces two new keys improving the security of the encryption and decryption schemes. The keys of the proposed security schemes are six and the right values of these all keys have to be used in the decryption scheme to recover the two initial images used in the input of the encryption scheme.
Reliability Analysis of the Effect of Digital Literacy on Performance among Secondary School Students in Malaysia

This paper intends to classify the factors relating to digital literacy and their effect on student performance.School teachers have been chosen in preliminary studies and support from past literature and pilot tests via survey have been carried out with 30 secondary school students.The influence of three dimensions on student performance have been studied, which include individual factors, the learning environment (physical), and socio-cultural aspects.From the pilot test, the results show that self-efficacy has the lowest value of 0.584, and the highest value is educational materials with 0.875 and is considered as one of the main factors of digital literacy.
STBEAT: Software Update on Trusted Environment Based on ARM TrustZone

In recent years, since edge computing has become more and more popular, its security issues have become apparent and have received unprecedented attention. Thus, the current research concentrates on security not only regarding devices such as PCs, smartphones, tablets, and IoTs, but also the automobile industry. However, since attack vectors have become more sophisticated than ever, we cannot just protect the zone above the system software layer in a certain operating system, such as Linux, for example. In addition, the challenges in IoT devices, such as power consumption, performance efficiency, and authentication management, still need to be solved. Since most IoT devices are controlled remotely, the security regarding system maintenance and upgrades has become a big issue. Therefore, a mechanism that can maintain IoT devices within a trusted environment based on localhost or over-the-air (OTA) will be a viable solution. We propose a mechanism called STBEAT, integrating an open-source project with ARM TrustZone to solve the challenges of upgrading the IoT system and updating system files more safely. This paper focuses on the ARMv7 architecture and utilizes the security stack from TrustZone to OP-TEE under the STM32 board package, and finally obtains the security key from the trusted application, which is used to conduct the cryptographic operations and then install the newer image on the MMC interface. To sum up, we propose a novel software update strategy and integrated ARM TrustZone security extension to beef up the embedded ecosystem.
RecepNet: Network with Large Receptive Field for Real-Time Semantic Segmentation and Application for Blue-Green Algae

Most high-performance semantic segmentation networks are based on complicated deep convolutional neural networks, leading to severe latency in real-time detection. However, the state-of-the-art semantic segmentation networks with low complexity are still far from detecting objects accurately. In this paper, we propose a real-time semantic segmentation network, RecepNet, which balances accuracy and inference speed well. Our network adopts a bilateral architecture (including a detail path, a semantic path and a bilateral aggregation module). We devise a lightweight baseline network for the semantic path to gather rich semantic and spatial information. We also propose a detail stage pattern to store optimized high-resolution information after removing redundancy. Meanwhile, the effective feature-extraction structures are designed to reduce computational complexity. RecepNet achieves an accuracy of 78.65% mIoU (mean intersection over union) on the Cityscapes dataset in the multi-scale crop and flip evaluation. Its algorithm complexity is 52.12 GMACs (giga multiply–accumulate operations) and its inference speed on an RTX 3090 GPU is 50.12 fps. Moreover, we successfully applied RecepNet for blue-green algae real-time detection. We made and published a dataset consisting of aerial images of water surface with blue-green algae, on which RecepNet achieved 82.12% mIoU. To the best of our knowledge, our dataset is the world’s first public dataset of blue-green algae for semantic segmentation.