Reviewerly/exHarmony huggingface dataset:
Classification and Detection of Obstacles for Rover Navigation

Abstract In this research project, the author aims to achieve Level 3 conditional automation whereby the researched Unmanned Ground Vehicle (UGV) is bound to classify and detect its own obstacle with human assistance as it cruises through a plantation field. Recognizing the different classes of obstacles enable the UGV to plan out the most efficient path to meet its desired goal. The purpose of this research project was to develop a classification and detection of obstacle and an optimal path planning algorithm suitable to be implemented for relieving the working process in an extreme condition plantation field. This paper presents an algorithm whereby it can conduct image-based obstacle detection through image masking and model prediction, along with a trigonometrical-based path planning approach. The proposed algorithm should hypothetically allow the UGV to conduct real-time path planning as it classifies some common obstacles such as leaves, rocks, and branches existing in a plantation field. As the waypoints were marked from the Ground Control Station (GCS), the UGV will travel towards the given waypoints to complete the given mission. When the UGV meets an obstacle, it will first differentiate whether it’s traversable, followed by running the proposed algorithm to avoid the risk of destructing the UGV by choosing a collision free path. The basic idea is to apply path planning by considering the available spacing between the detected obstacle by comparing with a predefined threshold. Through the provided threshold value, the UGV can identify the type of obstacle yet to be detected. For instance, obstacles within the given range of value can be labelled to be a leafy obstacle, otherwise it is not considered to be a leafy obstacle. To ensure the behaviour and safety measure of the UGV to run smoothly, the author had undergone model training for an elevated model prediction by training and deploying a custom training loop through TensorFlow. Nevertheless, MATLAB was utilized to test out the concept of the path planning algorithm to examine its behaviour as untraversable obstacles were met. All these implementations can further grow in the agricultural industries as it can aid humans with performing tedious and impossible tasks on site.
Rapid training of quantum recurrent neural networks

Abstract Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness recurrent neural networks (RNNs). However, while their predictions are quite accurate, their learning process is complex and, thus, time and energy consuming. Here, we propose to extend the concept of RRNs by including continuous-variable quantum resources in it and to use a quantum-enhanced RNN to overcome these obstacles. The design of the continuous-variable quantum RNN (CV-QRNN) is rooted in the continuous-variable quantum computing paradigm. By performing extensive numerical simulations, we demonstrate that the quantum network is capable of learning-time dependence of several types of temporal data and that it converges to the optimal weights in fewer epochs than a classical network. Furthermore, for a small number of trainable parameters, it can achieve lower losses than its classical counterpart. CV-QRNN can be implemented using commercially available quantum-photonic hardware.
A Wheeled Mobile Robot Obstacles Avoidance for Navigation Control in a Static and Dynamic Environments

Abstract This research focuses on the performance of the obstacle avoidance feature implemented on a wheeled mobile robot by using Sugeno Fuzzy Inference System (FIS). The test was done using a simulation software Webots and the layout was first draft out using walls, floors, and objects in order to set a boundary and obstacles for the simulation. To test the effectiveness and efficiency in terms of time and distance, the simulation time and trajectory of the Khepera implementing the Sugeno FIS was recorded and compared with the Khepera without implementing the Sugeno FIS. The inputs of the ultrasonic sensors were also recorded in order to analyze whether if the Khepera detects incoming objects or if Khepera follows the rules set in the algorithm.
An empirical comparison of vignette and virtual reality experiments in tourism research

ABSTRACTABSTRACTThere has been a call for more experimental studies in tourism research. The validity of different experiment tools has not been scientifically investigated. This study empirically compared the results from three experiments with identical research designs but different presentation modes (written vignette vs. VR via a Head Mounted Display vs. VR via a screen) for a research stimulus of experiencing a virtual national park. A total of 272, 131 and 106 valid responses were included in the final analysis, respectively. Participants in each experiment were randomly assigned to one of the three pricing conditions and asked to report post-visit perceptual evaluations and behavioural intention. This research found that the three experiments generated consistent results in the effects of pricing conditions on perceptual evaluations but different results on behavioural intention. Practical implications and limitations were discussed at the end.KEYWORDS: Experimentlaboratory experimentwritten vignettevirtual reality360-degree videopresentation mode Disclosure statementNo potential conflict of interest was reported by the author(s).
Breast ultrasound image classification and physiological assessment based on GoogLeNet

Medical ultrasound image classification based on convolutional neural network is the mainstream breast cancer classification model, but its limited perceptual ability limits its ability to obtain global information. A total of 880 breast ultrasound images were collected from 700 patients including 103 normal images, 467 malignant tumor images and 210 benign tumor images. In this paper, the diagnosis of breast ultrasound images was realized by constructing the CNN model of GoogLeNet. Firstly, the breast images were preprocessed based on the TV model. After that, the CNN model was trained and a more accurate model with a wider range of application was obtained based on improved Inception. Then we extract image features of different sizes; Then, feature classification was completed in different model classifiers to realize benign and malignant detection of breast cancer. Meanwhile, comparative analysis was performed to verify the excellence of the GoogLeNet model. The training time for classification was effectively reduced, and the classification accuracy rate was improved, reaching 96.37% combined with transformer learning. The loss value is down to 0.3492. Then, the classification accuracy of different structural models is discussed in the two models. The results show that GoogLeNet has great advantages in the detection of breast cancer ultrasound images. The influence of migration on experimental results is further discussed. Finally, combined with transfer learning, the three models were tested separately. The results show that transfer learning can improve system performance. Based on the TV model and the GoogLeNet model, this paper designs a method of breast cancer detection and classification based on the combination of TV and GoogLeNet model and transfer learning. Experiments show that this method can repair part of the texture damaged by markers in ultrasonic images, effectively extract the features of thyroid nodule, and accurately judge whether the breast is diseased, which greatly improves the diagnostic efficiency of doctors.
Physical realization of realignment criteria using the structural physical approximation

Entanglement detection is an important problem in quantum information theory because quantum entanglement is a key resource in quantum information processing. Realignment criteria is a powerful tool for detection of entangled states in bipartite and multipartite quantum system. It is an important criteria for entanglement detection because it works well; not only for negative partial transpose entangled states (NPTES) but also for positive partial transpose entangled states (PPTES). Since the matrix corresponding to realignment map is indefinite so the experimental implementation of the map is an obscure task. In this work, firstly, we have approximated the realignment map to a positive map using the method of structural physical approximation (SPA) and then we have shown that the structural physical approximation of realignment map (SPA-R) is completely positive. Positivity of the constructed map is characterized using moments which can be physically measured. Next, we develop a separability criterion based on our SPA-R map in the form of an inequality and have shown that the developed criterion not only detect NPTES but also PPTES. We have provided some examples to support the results obtained. Moreover, we have analysed the error that may occur because of approximating the realignment map.
Transformer-Based Hate Speech Detection in Assamese

Hate speech or hate content detection in a text is one of the emerging research in Natural Language Processing. Over several years, the popularity of social media has sky-rocketed, where anyone can share their feelings, desire, anger, etc. Sometimes, these posts include offensive information that hurts others, intentionally or unconsciously. Depending on the forum's visibility, such posts instantly spread over millions of people, which may lead to violence. In these circumstances, it is an essential task to identify whether the comment is hateful. In the Indian language context, this task presents greater complexity due to its multilingual nature and limited availability of resources. This situation motivated us to analyze hate speech in the Assamese language. The paper's contribution is in two aspects: firstly, it involves the development of a tagged dataset in Assamese comprising 4,000 sentences; secondly, by fine-tuning the mBERT-cased (bert-base-multilingual-cased) and the Bangla-BERT (sagorsarker/bangla-bert-base) model using the Assamese data to detect instances of hate speech effectively. Based on what we know, it is one of the pioneering attempts at detecting hate speech in the Assamese language.
Privacy Leakage via Unrestricted Motion-Position Sensors in the Age of Virtual Reality: A Study of Snooping Typed Input on Virtual Keyboards

Virtual Reality (VR) has gained popularity in numerous fields, including gaming, social interactions, shopping, and education. In this paper, we conduct a comprehensive study to assess the trustworthiness of the embedded sensors on VR, which embed various forms of sensitive data that may put users’ privacy at risk. We find that accessing most on-board sensors (e.g., motion, position, and button sensors) on VR SDKs/APIs, such as OpenVR, Oculus Platform, and WebXR, requires no security permission, exposing a huge attack surface for an adversary to steal the user’s privacy. We validate this vulnerability through developing malware programs and malicious websites and specifically explore to what extent it exposes the user’s information in the context of keystroke snooping. To examine its actual threat in practice, the adversary in the considered attack model doesn’t possess any labeled data from the user nor knowledge about the user’s VR settings. Extensive experiments, involving two mainstream VR systems and four keyboards with different typing mechanisms, demonstrate that our proof-of-concept attack can recognize the user’s virtual typing with over 89.7% accuracy. The attack can recover the user’s passwords with up to 84.9% recognition accuracy if three attempts are allowed and achieve an average of 87.1% word recognition rate for paragraph inference. We hope this study will help the community gain awareness of the vulnerability in the sensor management of current VR systems and provide insights to facilitate the future design of more comprehensive and restricted sensor access control mechanisms.
Artificial Intelligence and Blockchain-Based Trading Framework for Satellite Images

The convergence of artificial intelligence (AI) and the space industry is a crucial step towards advancing humanity. It has the potential to create innovative technologies and services, revolutionize existing practices, and provide valuable insights to space researchers. Many private investors and government organizations possess high demand for satellite images for various reasons, including weather forecasting, global disaster management (e.g., floods, earthquakes, and tsunamis), space object location, land records, geolocations, and international shipment management. However, the traditional centralized economic model for space business is costly to implement in terms of infrastructure, deployment, security risks, ownership, and marketing of the data. Additionally, the initial acquisition of satellite images is usually retrieved in a raw format without any authentication or interpretation. Consequently, the integration of AI and the space industry has the potential to revolutionize the existing model by unlocking new avenues to monetize satellite data by introducing automated processes that are more secure and cost-effective. In this paper, we aim to use deep learning models that are capable of automatically analyzing and classifying raw satellite images into valuable and actionable formats. Furthermore, we are inspired to design a blockchain-based solution to make satellite image datasets available to private and government agencies through a user-friendly and secure marketplace. The proposed solution will focus on a decentralized socio-economic model using blockchain to increase the security of transactions. Blockchain networks facilitate the fair exchange of transactions using smart contracts that will execute and enforce agreement rules among the blockchain participants. The proposed research will mainly focus on the efficient design of smart contracts for satellite data blockchain. In addition, our integrated image quality assessment method is built and tested to accurately group high-quality satellite images into valid categories to make the trading and marketing process more efficient.
Redeem Myself: Purifying Backdoors in Deep Learning Models using Self Attention Distillation

Recent works have revealed the vulnerability of deep neural networks to backdoor attacks, where a backdoored model orchestrates targeted or untargeted misclassification when activated by a trigger. A line of purification methods (e.g., fine-pruning, neural attention transfer, MCR [69]) have been proposed to remove the backdoor in a model. However, they either fail to reduce the attack success rate of more advanced backdoor attacks or largely degrade the prediction capacity of the model for clean samples. In this paper, we put forward a new purification defense framework, dubbed SAGE, which utilizes self-attention distillation to purge models of backdoors. Unlike traditional attention transfer mechanisms that require a teacher model to supervise the distillation process, SAGE can realize self-purification with a small number of clean samples. To enhance the defense performance, we further propose a dynamic learning rate adjustment strategy that carefully tracks the prediction accuracy of clean samples to guide the learning rate adjustment. We compare the defense performance of SAGE with 6 state-of-the-art defense approaches against 8 backdoor attacks on 4 datasets. It is shown that SAGE can reduce the attack success rate by as much as 90% with less than 3% decrease in prediction accuracy for clean samples. We will open-source our codes upon publication.
Sentiment Analysis Perspective using Supervised Machine Learning Method

Customer feedback play an important role in assisting the organization. Sentiment analysis of the customer feedback takes the business to next level of profit making. Based on the sentiment or the opinion of the customer organization can identify the needs of the customer. Sentiment analysis is applicable not only for the customer feedback analysis, it can also be used employee review system. To create the sentiment analysis model supervised machine learning algorithm can be used extensively. Supervised learning algorithm is used for training the model for classification and regression problem. In this article various learning algorithm like linear regression, KNN, SVM, Random Forest, Bagging and Gradient Boosting are considered for developing and testing the model. The dataset was divided into training and test dataset. The model was developed using the training dataset and tested on the test dataset. The data was collected from 884 employees working in different organizations. The reliability control has shown that 1.1 % of respondents were unreliable, as some questions were left unattended. The final data of 802 employees was taken for the study. The different lexical resources like Afinn, VADER and sentiment from textblob were considered for determining the sentiment related to all the three dimensions of positive leadership those are strength, perspective and recognition. The result shows that random forest supervised machine learning algorithm gives the highest accuracy of 0.711 whereas the KNN algorithm gives the accuracy of 0.515.
STOA based Feature Selection with Improved LSTM Model for Breast Cancer Diagnosis in IoT

Medical and health care have benefited greatly from IoT advancements. This technology helps both patients and doctors get a clear picture of a wide range of illnesses and make accurate diagnosis. The problem of low diagnostic accuracy in breast cancer diagnosis is, however, already included in the standard research approaches. Maintaining a strong foundation for breast cancer management and therapeutic advancement, early detection is essential. However, due to the nonappearance of indications in the early stages, early identification of cancer is challenging. As a result, cancer is still one area of medicine that scientists are working to advance in terms of detection, prevention, and therapy. The use of deep learning methods in mammogram processing has helped radiologists save money in recent years. In the current breast mass classification methods, deep learning knowledges like a (CNN). Although CNN-based systems have improved upon the pictures, several problems remain. Ignorance of semantic characteristics, analysis bound to the present patch of pictures, missing patches in low-contrast mammograms, and ambiguity in segmentation are all problems that need to be addressed. Because of these problems, this study's primary impartial is to create a deep learning-based system for classifying breast tumours in mammographic images as malignant or benign utilising two approaches: feature selection and classification. In this study, a recurrent neural network is employed for classification after the unnecessary data has been removed using the Sooty Tern Optimization Algorithm (STOA). Elite opposition-based learning optimally selects the weight and bias of Long-Short Term Memory (LSTM) (EOBL). Furthermore, two publicly accessible datasets of mammographic pictures are used to equivalence the projected approach to preexisting categorization systems. Comparative studies showed that the suggested strategy outperformed previously developed mammography categorization algorithms.
Skip-ST: Anomaly Detection for Medical Images Using Student-Teacher Network with Skip Connections

Anomaly detection (AD) aims to recognize abnormal inputs in testing data when only normal data are available during training. Most AD models perform well on specific datasets but are difficult to generalize to other tasks, especially on medical datasets with high heterogeneity. In this paper, we propose a student-teacher network with skip connections (Skip-ST) which is trained by a novel knowledge distillation paradigm called direct reverse knowledge distillation (DRKD) to realize AD. Skip-ST consists of a pretrained teacher encoder and a randomly initialized student decoder. The output of the teacher encoder's last layer is the input of the student decoder, which aims to recover the multi-scale representation extracted by the teacher encoder. We introduce skip connections between the teacher encoder and student decoder to prevent the student decoder from missing normal information of images at multi-scale. Experimental results show that Skip-ST achieves a 7.95% Area Under the Receiver Operating Characteristic (AUROC) improvement averagely on five challenging medical datasets, outperforming the state-of-the-art AD models. Our code is available at https://github.com/Arktis2022/Skip-TS.
Scatter and Split Securely: Defeating Cache Contention and Occupancy Attacks

In this paper, we propose SassCache, a secure skewed associative cache with keyed index mapping. For this purpose, we design a new two-layered, low-latency cryptographic construction with configurable output coverage based on state-of-the-art cryptographic primitives. Based on this construction, SassCache is the first secure randomized cache with secure spacing. Victim cache lines automatically hide in locations the attacker cannot reach after less than 1 access on average. Consequently, attackers cannot evict the cache line, no matter which and how many memory accesses they perform. Our security analysis shows that all existing techniques for eviction set construction fail, and state-of-the-art attacks only apply to 1 in 3 million addresses, where SassCache is still as secure as ScatterCache. Compared to standard caches, Sass Cache has a single-threaded performance penalty of 1.75 % on the last-level cache hit rate in the SPEC2017 benchmark, and an average decrease of 11.7 p.p. in hit rate for MiBench, GAP and Scimark for our high-security settings.
Approach research of user experience of various target audiences’ interaction with the portal interface

Recently, much attention has been paid to the convenience and accessibility of software products, websites, mobile applications, etc. It increases interest in user-oriented product design and UX design. When developing digital products, designers use a spe cial pool of tools that allow to conduct empirical studies of user experience and model scenarios for the use of software products and services. Such methods and tools as interviews, observation, customer journey mapping (CJM), Persona Frameworks, User Stories, and heatmap have become widespread. Each of them allows to identify problematic areas of a software product or ser vice application, determine the directions of its update so that it fully satisfies users’ needs. It also solves key business tasks. The purpose of the study is to analyze and identify ways to improve the web portal based on usability testing. The official website for posting information on the preparation of draft regulatory legal acts by federal executive authorities and the results of its public discussion (https://regulation.gov.ru) has been taken as the object of the research. The portal plays a significant role in the tasks of ensuring transparency of normative activities in the field of public administration. In this regard, monitoring the effectiveness of the portal and availability of information for a wide audience on it is an urgent task. The study uses usability testing methods, decision-making interview, user feedback matrix, CJM, HotJar, SpyWord, and SemRush as tools of the research. The article describes the features of usability testing. It draws conclusions about possible directions of the portal modernization in order to achieve its convenience for a wide audience. As a result, a number of specific proposals have been formulated.