Reviewerly/exHarmony huggingface dataset:
CONTENT ANALYSIS OF SELECT COLLEGE LIBRARY WEBSITES OF JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY, HYDERABAD: A STUDY

The study assesses ten select library websites from colleges affiliated with JNTU, Hydera-bad, for their usability, correctness, and accessibility. Additionally, it discusses the features and services offered by websites. A well-organized checklist was used to collect data from the study's selected colleges. According to the survey, the majority of colleges have included information about their services (80%) and infrastructure (80%) on their websites, and all libraries of selected colleges have incorporated details about their introduction (100%) collection (100%), and statistics (100%). However, the report did point out that none of the library websites or web pages have a section for people with disabilities, a user manual, a single window search, an Ask a librarian link, newspaper clippings, remote access, interlibrary loan (ILL)/document delivery, social networking facilities, or regular updates. They also fall short in terms of accessibility.The study's findings indicate that Sultanul Uloom College of Pharmacy and Shadan Wom-an's College of Pharmacy received the lowest score of six (06) out of forty-three (43) and are currently ranked last, while the library at Aurora's Scientific, Technological and Re-search Academy had the highest score of twenty-two (22) out of forty-three (43), and is currently ranked first. The paper offers suggestions for carrying out these assessment studies, which are critically required to allow organization to continually update their website and deliver better results in terms of user requirement.
Advancing Robot Perception in Non-Spiral Environments through Camera-based Image Processing

Robot perception heavily relies on camera-based visual input for navigating and interacting in its environment. As robots become integral parts of various applications, the need to efficiently compute their visibility regions in complex environments has grown. The key challenge addressed in this paper is to devise an innovative solution that not only accurately computes the visibility region V of a robot operating in a polynomial environment but also optimizes memory utilization to ensure real-time performance and scalability.The main objective of this research is to propose an algorithm that achieves optimal-time complexity and significantly reduces memory requirements for visibility region computation. By focusing on sub-linear memory utilization, we aim to enhance the robot's ability to perceive its surroundings effectively and efficiently.Previous approaches have provided solutions for visibility region computation in non-spiral environments, but most were not tailored to memory limitations. In contrast, the proposed algorithm is designed to achieve optimal time complexity that is O(n) while reducing memory usage to O(c/log n) variables, where c &lt; n represents the number of critical corners in the environment. Leveraging the constant-memory model and memory-constrained algorithm, we aim to strike a balance between computational efficiency and memory usage.The algorithm's performance is rigorously evaluated through extensive simulations and practical experiments. The results demonstrate its linear-time complexity and substantial reduction in memory usage without compromising the accuracy of the visibility region computation. By efficiently handling memory constraints, the robot gains a cost-effective and reliable perception mechanism, making it well-suited for a wide range of real-world applications.The constant-memory model and memory-constrained algorithm presented in this paper offer a significant advancement in robot perception capabilities. By optimizing the visibility region computation in polynomial environments, our approach contributes to the efficient operation of robots, enhancing their performance and applicability in complex real-world scenarios. The results of this research hold promising potential for future developments in robotics, computer vision, and related fields.
Riverside: A design study on visualization for situation awareness in cybersecurity

Real-time situation awareness is a key challenge of cybersecurity defense. Visual analytics has been utilized for this purpose, but existing tools tend to require detailed knowledge about the network, which can be challenging in large-scale, production networks. We conducted an interview study involving 24 security professionals to gather requirements for the design, development, and evaluation of visualization to aid situation awareness in cybersecurity. Using these findings, we designed a visualization tool – called RIVERSIDE – for providing a real-time view of the dynamically changing computer network to support situation awareness. We evaluated Riverside in a user study involving 10 participants. Participants were placed in an incident response scenario that tasked them to identify malicious activity on a network. 20% of the users identified all attack component, while an additional 40% only missed one component.
Study of Machine Learning for Recommendation Systems

This study provides an overview of recommendation systems and machine learning and their types. It briefly outlines the types of machine learning, such as supervised, unsupervised, semi-supervised learning and reinforcement. It explores how to implement recommendation systems using three types of filtering techniques: collaborative filtering, content-based filtering, and hybrid filtering. The machine learning techniques explained are clustering, co-clustering, and matrix factorization methods, such as Single value decomposition (SVD) and Non-negative matrix factorization (NMF). It also discusses K-nearest neighbors (KNN), K-means clustering, Naive Bayes and Random Forest algorithms. The evaluation of these algorithms is performed on the basis of three metric parameters: F1 measurement, Root mean squared error (RMSE) and Mean absolute error (MAE). For the experimentation, this study uses the BookCrossing dataset and compares analysis based on metric parameters. Finally, it also graphically depicts the metric parameters and shows the best and the worst techniques to incorporate into the recommendation system. This study will assist researchers in understanding the summary of machine learning in recommendation systems.
Sequent-type rejection systems for finite-valued non-deterministic logics

AbstractA rejection system, also referred to as a complementary calculus, is a proof system axiomatising the invalid formulas of a logic, in contrast to traditional calculi which axiomatise the valid ones. Rejection systems therefore introduce a purely syntactic way of determining non-validity without having to consider countermodels, which can be useful in procedures for automated deduction and proof search. Rejection calculi have first been formally introduced by Łukasiewicz in the context of Aristotelian syllogistic and subsequently rejection systems for many well-known logics have been proposed. In this paper, we deal with rejection systems for so-called non-deterministic finite-valued logics, a special case of non-deterministic many-valued logics which were introduced by Avron and Lev as a generalisation of traditional many-valued logics. More specifically, we introduce a systematic method for constructing sequent-style rejection systems for any given non-deterministic finite-valued logic. Furthermore, as special instances of our method, we provide concrete calculi for specific paraconsistent logics which can be characterised in terms of non-deterministic two- and three-valued semantics, respectively.Keywords: Sequent-type systemsfinite-valued logicsproof theoryaxiomatic rejectionparaconsistent logics Disclosure statementNo potential conflict of interest was reported by the authors.Notes1 For an excellent recent survey about rejection systems, cf. the paper by Goranko et al. (Citation2020).
Introduction and Comparison of Novel Decentral Learning Schemes with Multiple Data Pools for Privacy-Preserving ECG Classification

Artificial intelligence and machine learning have led to prominent and spectacular innovations in various scenarios. Application in medicine, however, can be challenging due to privacy concerns and strict legal regulations. Methods that centralize knowledge instead of data could address this issue. In this work, 6 different decentralized machine learning algorithms are applied to 12-lead ECG classification and compared to conventional, centralized machine learning. The results show that state-of-the-art federated learning leads to reasonable losses of classification performance compared to a standard, central model (-0.054 AUROC) while providing a significantly higher level of privacy. A proposed weighted variant of federated learning (-0.049 AUROC) and an ensemble (-0.035 AUROC) outperformed the standard federated learning algorithm. Overall, considering multiple metrics, the novel batch-wise sequential learning scheme performed best (-0.036 AUROC to baseline). Although, the technical aspects of implementing them in a real-world application are to be carefully considered, the described algorithms constitute a way forward towards preserving-preserving AI in medicine.
Research on the Detection and Analysis of Students’ Classroom Behavioral Features Based on Deep CNNs

The continuous development of artificial intelligence technology has caused the traditional education model to begin to change as well. The new educational model of applying intelligent monitoring devices to modern classrooms, thus assisting teachers in teaching students, has been successfully favored by major universities. Based on this, this research attempts to improve the target detection algorithm in the field of deep learning, and proposes a method of detecting and analyzing students' classroom behavior based on deep convolutional neural network, and applies the method to detect students' classroom behavior characteristics, aiming to improve the method of detecting and analyzing students' classroom behavior, assist teachers in student management with intelligent monitoring devices, and thus improve the quality of teaching. The study firstly investigated the core steps of the target detection algorithm, and secondly improved the YOLOv5s algorithm by adding feature fusion layer and reimaging module, and finally obtained the Ghost-4D-YOLOv5s network model. The experimental results show that the Ghost-4D-YOLOv5s model performs well in terms of precision, recall, F1 value and average precision mean. When the number of samples to be tested is 350, the performance of the model achieves 58.2% precision, 59.6% recall, 62.5% F1 value and 62.8% average precision mean value. This indicates that the model constructed in this study has good performance in detecting the behavioral characteristics of students in the classroom. It can be concluded that the model constructed in this study can have a good performance in the detection of students' classroom behavioral characteristics. Through this model, teachers can better manage their students and improve the quality of teaching. In addition, this study highlights the potential of deep learning in the field of education and contributes to the development of intelligent educational models.
Blockchain solutions for scientific paper peer review: a systematic mapping of the literature

Purpose This paper presents a review of the state of the art on the application of blockchain and smart contracts to the peer-review process of scientific papers. The paper seeks to analyse how the main characteristics of the existing blockchain solutions in this field to detect opportunities for the improvement of future applications. Design/methodology/approach A systematic review of the literature on the subject was carried out in three databases recognized by the research community (IEEE Xplore, Scopus and Web of Science) and the Frontiers in Blockchain journal. A total of 1,967 articles were initially found, and after the exclusion process, the 26 remaining articles were classified according to the following dimensions: System Type, Open Access, Review Type, Reviewer Incentive, Token Economy, Blockchain Access, Blockchain Identification, Blockchain Used, Paper Storage, Anonymity and Maturity of the solution. Findings Results show that the solutions are normally concerned on offering incentives to the reviewers' work (often monetary). Other common general preferences among the solutions are the adoption of open reviews, the use of Ethereum , the implementation of publishing ecosystems and the use of InterPlanetary File System to the storage of the papers. Originality/value There are currently no studies covering the main aspects of blockchain solutions in the field of scientific peer review. The present study provides an overall review of the topic, summarizing important information on the current research and helping new adopters to develop solutions grounded on the existing literature.
Texture features-based lightweight passive multi-state crowd counting algorithm

Abstract Passive crowd counting using channel state information (CSI) is a promising technology for applications in fields such as smart cities and commerce. However, the most existing algorithms can only recognize the total number of people in the monitoring area and cannot simultaneously recognize the number and states of people and ignore the real-time performance of the algorithms. Therefore, they cannot be applied to the scenarios of multi-state crowd counting requiring high real-time performance. To address this issue, a lightweight passive multi-state crowd counting algorithm called TF-LPMCC is proposed. This algorithm constructs CSI amplitude data into amplitude and time–frequency images, extracts texture features using the gray-level co-occurrence matrix (GLCM) and gray-level difference statistic (GLDS) methods, and uses the linear discriminant analysis (LDA) algorithm to count the crowd in multi-states. Experiments show that the TF-LPMCC algorithm not only has low time complexity but also achieves an average recognition accuracy of 98.27% for crowd counting.
LiDAR-camera-system-based unsupervised and weakly supervised 3D object detection

LiDAR camera systems are now becoming an important part of autonomous driving 3D object detection. Due to limitations in time and resources, only a few critical frames of the synchronized camera data and acquired LiDAR points may be annotated. However, there is still a large amount of unannotated data in practical applications. Therefore, we propose a LiDAR-camera-system-based unsupervised and weakly supervised (LCUW) network as a novel 3D object-detection method. When unannotated data are put into the network, we propose an independent learning mode, which is an unsupervised data preprocessing module. Meanwhile, for detection tasks with high accuracy requirements, we propose an Accompany Construction mode, which is a weakly supervised data preprocessing module that requires only a small amount of annotated data. Then, we generate high-quality training data from the remaining unlabeled data. We also propose a full aggregation bridge block in the feature-extraction part, which uses a stepwise fusion and deepening representation strategy to improve the accuracy. Our comparative, ablation, and runtime test experiments show that the proposed method performs well while advancing the application of LiDAR camera systems.
An Approach for Monitoring Shallow Surface Outcrop Mining Activities Based on Multisource Satellite Remote Sensing Data

Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing.
Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

Chromite is a strategic mineral resource for many countries, and chromite deposit occurrences are widespread in the ultramafic rocks of the Yarlung Zangbo ophiolite belt, particularly in the harzburgite unit of the mantle section. Conducting field surveys in complex and poorly accessible terrain is challenging, expensive, and time-consuming. Remote sensing is an advanced method of achieving modern geological work and is a powerful technical means of geological research and mineral exploration. In order to delineate outcrops of chromite-bearing mantle peridotite, the present research study integrates seven image-enhancement techniques, including optimal band combination, decorrelation stretching, band ratio, independent component analysis, principal component analysis, minimum noise fraction, and false color composite, for the interpretation of Landsat8 OLI and WorldView-2 satellite data. This integrated approach allows the effective discrimination of chromite-containing peridotite outcrops in the Luobusa area, Tibet. The interpretation results derived from these integrated image-processing techniques were systematically verified in the field and formed the basis of the feature selection process of different lithologies supported by the support vector machine algorithm. Furthermore, the distribution range of the ferric contamination anomaly is detected through the de-interference abnormal principal component thresholding technique, which shows a high spatial matching relationship with mantle peridotite. This is the first study to utilize Landsat8 OLI and WorldView-2 remote sensing satellite data to explore the largest chromite deposit in China, which enriches the research methods for the chromite deposits in the Luobusa area. Accordingly, the results of this investigation indicate that the integration of information extracted from image-processing algorithms using remote sensing data could be a broadly applicable tool for prospecting chromite ore deposits associated with ophiolitic complexes in mountainous and inaccessible regions such as Tibet’s ophiolitic zones.
Named Entity Recognition in Power Marketing Domain Based on Whole Word Masking and Dual Feature Extraction

With the aim of solving the current problems of low utilization of entity features, multiple meanings of a word, and poor recognition of specialized terms in the Chinese power marketing domain named entity recognition (PMDNER), this study proposes a Chinese power marketing named entity recognition method based on whole word masking and joint extraction of dual features. Firstly, word vectorization of the electricity text data is performed using the RoBERTa pre-training model; then, it is fed into the constructed dual feature extraction neural network (DFENN) to acquire the local and global features of text in a parallel manner and fuse them. The output of the RoBERTa layer is used as the auxiliary classification layer, the output of the DFENN layer is used as the master classification layer, and the output of the two layers is dynamically combined through the attention mechanism to weight the outputs of the two layers so as to fuse new features, which are input into the conditional random field (CRF) layer to obtain the most reasonable label sequence. A focal loss function is used in the training process to alleviate the problem of uneven sample distribution. The experimental results show that the method achieved an F1 value of 88.58% on the constructed named entity recognition dataset in the power marketing domain, which is a significant improvement in performance compared with the existing methods.
Machine Unlearning by Reversing the Continual Learning

Recent legislations, such as the European General Data Protection Regulation (GDPR), require user data holders to guarantee the individual’s right to be forgotten. This means that user data holders must completely delete user data upon request. However, in the field of machine learning, it is not possible to simply remove these data from the back-end database wherein the training dataset is stored, because the machine learning model still retains this data information. Retraining the model using a dataset with these data removed can overcome this problem; however, this can lead to expensive computational overheads. In order to remedy this shortcoming, we propose two effective methods to help model owners or data holders remove private data from a trained model. The first method uses an elastic weight consolidation (EWC) constraint term and a modified loss function to neutralize the data to be removed. The second method approximates the posterior distribution of the model as a Gaussian distribution, and the model after unlearning is computed by decreasingly matching the moment (DMM) of the posterior distribution of the neural network trained on all data and the data to be removed. Finally, we conducted experiments on three standard datasets using backdoor attacks as the evaluation metric. The results show that both methods are effective in removing backdoor triggers in deep learning models. Specifically, EWC can reduce the success rate of backdoor attacks to 0. IMM can ensure that the model prediction accuracy is higher than 80% and keep the success rate of backdoor attacks below 10%.
VR Traffic Dataset on Broad Range of End-User Activities

With the emergence of new internet traffic types in modern transport networks, it has become critical for service providers to understand the structure of that traffic and predict peaks of that load for planning infrastructure expansion. Several studies have investigated traffic parameters for Virtual Reality (VR) applications. Still, most of them test only a partial range of user activities during a limited time interval. This work creates a dataset of captures from a broader spectrum of VR activities performed with a Meta Quest 2 headset, with the duration of each real residential user session recorded for at least half an hour. Newly collected data helped show that some gaming VR traffic activities have a high share of uplink traffic and require symmetric user links. Also, we have figured out that the gaming phase of the overall gameplay is more sensitive to the channel resources reduction than the higher bitrate game launch phase. Hence, we recommend it as a source of traffic distribution for channel sizing model creation. From the gaming phase, capture intervals of more than 100 s contain the most representative information for modeling activity.