Reviewerly/exHarmony huggingface dataset:
Optimizing Cabin Wireless Network Signal Coverage with Ray Tracing Method

Wireless networks are widely used in cabin networks due to their advantages of low cost and high flexibility. However, the complex electromagnetic environment in cabins can lead to path and fading loss effects, making it difficult to achieve full coverage of signal. Therefore, this paper proposes a simulation optimization model for cabin wireless channel propagation based on ray tracing algorithms. The propagation path of wireless signals in the cabin is searched using the mirror method in the reverse ray tracing algorithm, and the total signal intensity is calculated based on the principle of signal superposition to generate the cabin wireless network signal coverage thermal map. The experimental results demonstrate that our simulation optimization model effectively verifies the rationality, feasibility, and reliability of deploying wireless networks in cabins.
Software Trusted Startup and Update Protection Scheme of IoT Devices

Existing trusted compute schemes designed for complex devices are hard to deploy on IoT devices with limited memory, low power, and weak processing capability. We propose a startup and update protection scheme for IoT devices that builds a Trusted Cryptography Module (TCM) on Zynq platform. SM3 and SM4 cryptography algorithms are deployed on the TCM to accelerate the hash and symmetry algorithm, ensuring integrity and confidentiality. We implement trusted startup and firmware update algorithms through software to adapt heterogeneous IoT devices and make update process security. We add hash verification and digital signature in the communicating process between the server and the TCM to resist integrity destruction and impersonation attack. Our scheme ensures the security of IoT devices running process and updates through TCM and solves the problem that existing models are difficult to deploy on IoT devices.
Entanglement maintenance of classically pumped qubits and calibration aspects with the resonator tomography under the Stark shift effect inside a coherent resonator

We present a scheme for the investigation of entanglement of a similar qubits-pair along with the coherent resonator tomography under two-photon processes. Our discussion is focusing on the effect of the classical power amplitude along with Stark shift width and the conditions under which they have a constructive role in protecting the initial-state entanglement and the corresponding field tomography evolution. We observe that the entanglement between the qubits does maximally peak below a critical value and can be determined by suitable measurements on the resonator tomography. Interestingly, above the critical value, the tomography profile either shows an annular structure or exhibits quantumness which could be largely diminished by controlling the driving power intensity as well as the shift width. Particularly and for a detuned interaction, the critical values can be largely exceeded for sufficiently large values of the driving intensity and Stark shift, hence, more regions in the plane of parameters can be occupied by the qubits correlation. In this case, the pronounced fluctuations exhibited by entanglement dynamics due to phase variations could be highly interrupted. Additionally, preserving the field tomography showing a multi-component Schrödinger cat state via field-field detuning, robust, periodical and close to unity entanglement between the qubits could be highly possible. Moreover, in particular situations, the field tomography dynamics show the Fourier transform of the original field wave packet one-quarter period after initial, accordingly, entanglement maxima are reached frequently per a quarter period of rotation.
Poster: Software Fault Localization as a Service (SFLaaS)

Many tools for enabling developers locating faults in their programs have been proposed in the literature. The majority of the programs they target are those created in the C/C++ and Java languages. In this paper, we offer a tool named "SFLaaS" for locating faults in programs written in Python, a popular programming language, and is provided as a service rather than as a plugin or a command-line tool to be installed. Thus, our tool can be accessed anytime and from anywhere. The tool employs Spectrum-based fault localization (SBFL) to help Python developers automatically analyze their programs and generate useful data at run-time to be used to produce a ranked list of potentially faulty program elements (i.e., statements). Our proposed tool supports different important features in fault localization such as supporting about 80 SBFL formulas, different tie-breaking methods, showing code elements with different colors, ranging from most suspicious (red) to not suspicious (green) based on their suspicious scores, allowing the user to define his/her own formula, etc. Using our tool could help developers to efficiently find faults in their programs.
Blockchain in Emission Management: Opportunities and Trends

The emergence of blockchain technology in different use cases is remarkable. Blockchain has the potential of transforming global business, particularly in the field of emission management. The unique features of blockchain technology encouraged scholars and startups to invest in utilizing blockchain within emission management, however, there is a need to review the literature related to blockchain and its potential to advance emission management and climate change. Hence, this paper provides an overview of the current trends as well as future opportunities that can be pursued in this space.
Analysis of RAISA (Robotics, Artificial Intelligence &amp; Service Automation) and Dubai Tourism

Travel and tourism are crucial global economic growth and development engines because they generate employment opportunities and add to economic output in various ways. Travel abroad might be for pleasure, business, medical, or other reasons. The tourism sector has benefited greatly from technological development. This study provides a systematic review of RAISA on the tourism sector in Dubai and studies its effects on service quality. Robotics (R), artificial intelligence (AI), and service automation (RAISA) are all technologies that are widely used in the travel and tourism sector around the world (SA). The research aims to understand how far along the travel sector is in adopting RAISA.. This paper elaborates on preceding evaluations and conducts a systematic literature examination to discuss the pros and cons of using RAISA in Dubai’s tourism industry.The review-based analysis suggests that AI and SA technologies adopted in Dubai have positively impacted Dubai tourism, and the robot is a good fit for tourism and hospitality in the Dubai sector to some extent. However, for the new RAISA technologies to be fully utilized, those working in the tourism and hospitality industries need to upgrade their level of digital literacy. Trained personnel and resources are required, which can be challenging to enact RAISA Also, the primary obstacles to enacting RAISA are a lack of resources and a dearth of qualified personnel. The findings of the review discuss that RAISA’s most vital points were productivity and efficiency, the program’s ability to improve service quality, streamline processes, reduce human error, and work on things that can enhance the psychological well-being of people. There are a few disadvantages. However, human intervention is always required and recommended in the service industry. More investigation is needed in Dubai’s hospitality and tourism industry.
Distance Data Recovering from Faulty HDDs: Preliminary Investigation

With the term <<Distance Data Recovering>> is meant that the process of recovery of data from a faulty Hard Disk Drive (HDD) in a workshop is performed without the physical presence of the disk. To this end, the faulty disk of a remote user is proposed to be connected to the workshop via the internet. In this preliminary investigation, experience gained by our workshop in data recovery using actual faulty HDDs will be used to assess whether this is possible and point out the most common problems likely to be encountered depending on the nature of the fault of a disk.
A New Method Based on Particle Swarm Optimization Algorithm to Improve Statistical Randomness Properties

The aim of optimization algorithms is to find the best options demanded under the specified conditions. Having a quality source of entropy is an important requirement, as the need for randomness also plays a critical role in the foundation of many applications. The main obejective of this study is to address this need with an innovative approach. Comprehensive analysis tools have been developed in the literature to measure the quality of randomness source. One of the most widely known of these analysis tools is the NIST testing tool, which includes fifteen different hypothesis testing. The generated entropy source provides ideal statistical properties because NIST tests are used as objective function in the optimization algorithm. The expected output from the proposed new approach is to decide what configurations of the LFSR generator will produce ideal randomness. The successful results obtained using the proposed approach indicate that the outputs can be used as a basic building block in the future, especially in cryptology studies.
Academic Credential Authentication on Blockchain: DeLone and McLean model

Credentials, whether in printed versions or electronic versions, are frequently recognized objects in an educational journey. Conventional verification systems are time consuming, ineffective and susceptible to manipulation. Blockchain technology is regarded as a viable system to accelerate the traditional authentication processes and protect certificates from counterfeiting. This study examines the advantages of Blockchain for the issuance of Digital Academic Certificates employing a case study approach. The modified DeLone and McLean model is implemented for unequivocally and methodically mapping the benefits of Digital Academic certificates on the Blockchain platform. The finding indicates Blockchain technology-based educational certificates lead to an improvement in system quality, information accuracy, and service efficiency. This results in the efficient authenticating of processes and facilitating informed decision-making for the stakeholders
Receding horizon navigation and target tracking for aerial detection of transient radioactivity

The paper presents a receding horizon planning and control strategy for quadrotor-type micro aerial vehicle (mav)s to navigate reactively and intercept a moving target in a cluttered unknown and dynamic environment. Leveraging a lightweight short-range sensor that generates a point-cloud within a relatively narrow and short field of view (fov), and an ssd-MobileNet based Deep neural network running on board the mav, the proposed motion planning and control strategy produces safe and dynamically feasible mav trajectories within the sensor fov, which the vehicle uses to autonomously navigate, pursue, and intercept its moving target. This task is completed without reliance on a global planner or prior information about the environment or the moving target. The effectiveness of the reported planner is demonstrated numerically and experimentally in cluttered indoor and outdoor environments featuring maximum speeds of up to 4.5–5 m/s.
Exploring the Effectiveness of Steganography Techniques: A Comparative Analysis

This research paper explores the use of image steganography for data communication. It provides an overview of data communication and steganography and focuses on the specific area of image steganography. It surveys various algorithms, including traditional and recent techniques, such as least significant bit insertion and neural network-based approaches. The importance of security implementation and recent advancements in deep learning techniques, such as GANs and CNNs, are also discussed. The paper concludes with a discussion of future directions and open challenges in the field of image steganography for data communication.
Cloud Computing and Security using CloudSim

Cloud computing leverages wide range of services (storage, servers, databases, analytics, intelligence, networking, and software) via internet to speed up innovation, provide flexible resources, and forecast economic scalability. Examples include, Netflix (for streaming video) and Gmail (for backup needs). Cloud simulation utilizes computer services in simulation and is nothing more than infrastructure and software that researchers employ as a service. Data centres, virtual machines (VM), and other items can all be managed through CloudSim's administration interfaces. Cloud simulators are used to simulate many kinds of cloud applications. A product can be evaluated by using simulations and make unrestricted fixes to issues prior to the actual launch. Therefore, cloud simulator is an economical tool used to analyse the working of cloud components to operate under various conditions and workloads. Over the years, several cloud simulators have been created; this article provides an evaluation study of most of the existing techniques. This study discusses about the typical architecture in computer simulators. This study has covered cloud simulation operation of the Internet Simulator.
Comparative Analysis of Classification algorithms for Classifying Psychotypes

Machine learning is a subdomain of Artificial Intelligence that makes a machine learn with the help of data. Classification algorithms follow a supervised learning methodology which allows labels to be assigned to the observations so that unobserved data can be labelled based on the training data. This paper intends to study different classification (Supervised learning) algorithms with the help of the MBTI dataset. MBTI Test is a Meyers Briggs Type Indicator test which helps us to identify an individual based on one of the 16 personality types. 4 classification algorithms namely, k-nearest neighbours. Decision Tree, Support Vector Machine and Random Forest algorithm are implemented on the KPMI Dataset. The evaluation metries (accuracy, precision, recall and f1-score) related to each of the classification algorithms are measured. A comparison of the metrics is tabulated to throw light on the best algorithm for the given dataset. As per the MBTI test, an individual belongs to 1 of 16 personality types based on whether an individual is an extrovert(E)-introvert(I), sensing(S)-intuitive(N), thinking(T)-feeling(F) or judging(J)-perceiving(P). The MBTI dataset is visualized to know the psycho-type of the employees. The visualization helps to identify the highly and rarely found psycho type. It is visualized that in employees of the MBTI dataset, most of the psycho-types are satisfied with their jobs. In the future, this algorithm will help in identifying student personality type, related student behaviour analysis, and its predictions related to their career choice, and remedial measures for improvement in personality.
Extracting Information and Size Prediction of Objects in Underwater Images using Image Processing Technique

Underwater image analysis is the current research field since a lot of resources is available in ocean. The prediction about captured underwater images is not an easy task. So, far the prediction about underwater buried images have been done with the help of human being. To overcome this, in this paper, the prediction about buried/Sunken underwater object have been done using image processing technique with the concept of search and recovery method. The underwater images are considered as inputs and by using grab cut algorithm, the segmentation have been done. The Segmented image is compared with original object. So, it concluded by predicting the size of the object by using ratio between original object and segmented object. The same methodology is applied for predicting the size of the sub objects in the considered input image, and it works out.
Design and Implementation of IoT based Dual Axis Solar Tracking System

Web of Things (WoT) innovations, along economies of scale and advances in system, programming, and company improvements, have speeded up the blast of related items throughout the Internet. A related article may be managed on-line through an IoT level and may transfer, get, and procedure exclusive and modified parameters. This study has reported about the IoT improvements to advocate a primary and cost efficient IoT application to display and manipulate first rate single-pivot solar powered tracker to perform execution assessment. The proposed model includes pre-programmed alerts that can be sent to a remote customer via smartphone or e-mail. The proposed model is a low-cost and simple-to-use system and programming, as well as a web-based model. The design components of IoT include solar powered trackers. In addition, a version of IoT primarily based on fully solar powered trackers has been developed and tested. The results show how daylight-based tracker data can be sent successfully and correctly. This can be easily checked on the web, as well as how the solar powered trackers from the NodeMCU device. With the help of this proposed concept, the orientation of the solar power will be continuously monitored and tracked to improve the energy conversion efficiency.
Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion

Few-shot knowledge graph completion (FKGC) task aims to predict unseen facts of a relation with few-shot reference entity pairs. Current approaches randomly select one negative sample for each reference entity pair to minimize a margin-based ranking loss, which easily leads to a zero-loss problem if the negative sample is far away from the positive sample and then out of the margin. Moreover, the entity should have a different representation under a different context. To tackle these issues, we propose a novel Relation-Aware Network with Attention-Based Loss (RANA) framework. Specifically, to better utilize the plentiful negative samples and alleviate the zero-loss issue, we strategically select relevant negative samples and design an attention-based loss function to further differentiate the importance of each negative sample. The intuition is that negative samples more similar to positive samples will contribute more to the model. Further, we design a dynamic relation-aware entity encoder for learning a context-dependent entity representation. Experiments demonstrate that RANA outperforms the state-of-the-art models on two benchmark datasets.
Toward Explainable Recommendation via Counterfactual Reasoning

Recently, counterfactual explanation models have shown impressive performance in adding explanations to recommendation systems. Despite their effectiveness, most of these models neglect the fact that not all aspects are equally important when users decide to purchase different items. As a result, the explanations generated may not reflect the users' actual preferences. Furthermore, these models typically rely on external tools to extract aspect-level representations, making the model's explainability and recommendation performance are highly dependent on external tools. This study addresses these research gaps by proposing a co-attention-based fine-grained counterfactual explanation model that uses co-attention and aspect representation learning to directly capture user preferences toward different items for recommendation and explanation. The superiority of the proposed model is demonstrated through extensive experiments.
TCR: Short Video Title Generation and Cover Selection with Attention Refinement

With the widespread popularity of user-generated short videos, it becomes increasingly challenging for content creators to promote their content to potential viewers. Automatically generating appealing titles and covers for short videos can help grab viewers' attention. Existing studies on video captioning mostly focus on generating factual descriptions of actions, which do not conform to video titles intended for catching viewer attention. Furthermore, research for cover selection based on multimodal information is sparse. These problems motivate the need for tailored methods to specifically support the joint task of short video title generation and cover selection (TG-CS) as well as the demand for creating corresponding datasets to support the studies. In this paper, we first collect and present a real-world dataset named Short Video Title Generation (SVTG) that contains videos with appealing titles and covers. We then propose a Title generation and Cover selection with attention Refinement (TCR) method for TG-CS. The refinement procedure progressively selects high-quality samples and highly relevant frames and text tokens within each sample to refine model training. Extensive experiments show that our TCR method is superior to various existing video captioning methods in generating titles and is able to select better covers for noisy real-world short videos.
Dynamically-Scaled Deep Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. Several variants of CCA have been introduced in the literature, in particular, variants based on deep neural networks for learning highly correlated nonlinear transformations of two views. As these models are parameterized conventionally, their learnable parameters remain independent of the inputs after the training process, which limits their capacity for learning highly correlated representations. We introduce a novel dynamic scaling method for an input-dependent canonical correlation model. In our deep-CCA models, the parameters of the last layer are scaled by a second neural network that is conditioned on the model's input, resulting in a parameterization that is dependent on the input samples. We evaluate our model on multiple datasets and demonstrate that the learned representations are more correlated in comparison to the conventionally-parameterized CCA-based models and also obtain preferable retrieval results.
Interest Driven Graph Structure Learning for Session-Based Recommendation

In session-based recommendations, to capture user interests, traditional studies often directly embed item sequences. Recent efforts explore converting a session into a graph and applying graph neural networks to learn representations of user interests. They rely on predefined principles to create edges, e.g., co-occurrence of item pairs in the sequence. However, in practice, user interests are more complicated and diverse than manually predefined principles. Adjacent items in the sequences may not be related to the same interest, while items far away from each other could be related in some scenarios. For example, at the end of shopping, the user remembers to purchase items associated with the one purchased at the beginning. While using predefined rules may undermine the quality of the session graph, it is challenging to learn a reasonable one that is in line with the user interest. Sessions are diverse in length, the total number of interests, etc. Signals for supervision are not available to support graph construction. To this end, we explore coupling the session graph construction with user-interest learning, and propose a novel framework - PIGR. It recognizes items with similar representations learned based on sequential behavior and preserves their interactions. Related items reside in the same induced subgraph and are clustered into one interest. A unified session-level vector is retrieved from the different granularity of interests to guide the next-item recommendation. Empirical experiments on real-world datasets demonstrate that PIGR significantly outperforms state-of-the-art baselines.