Reviewerly/exHarmony huggingface dataset:
Riemannian submanifold framework for log-Euclidean metric learning on symmetric positive definite manifolds

This study presents a novel Riemannian submanifold (RS) framework for log-Euclidean metric learning on symmetric positive definite manifolds. Our method identifies the optimal RS without changing the original tangent space. The RS is spanned by multiple bases, and each data point is parameterized using these bases, such that the data can be represented more informatively compared to conventional approaches. In the RS, a new distance function is defined, and its derivative cannot be obtained trivially. We overcome this difficulty and provide a simple analytic form of the derivative. The proposed transformation matrix can take any form, which gives flexibility to our method and enables the creation of several variants. In experiments, our method and its variants surpass state-of-the-art metric learning methods in synthetic, material categorization, and action recognition problems.
Regulating Group Exposure for Item Providers in Recommendation

Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendation services, the interests of those providers should be valued. In this paper, we consider providers as grouped based on a common characteristic in settings in which certain provider groups have low representation of items in the catalog and, thus, in the user interactions. Then, we envision a scenario wherein platform owners seek to control the degree of exposure to such groups in the recommendation process. To support this scenario, we rely on disparate exposure measures that characterize the gap between the share of recommendations given to groups and the target level of exposure pursued by the platform owners. We then propose a re-ranking procedure that ensures desired levels of exposure are met. Experiments show that, while supporting certain groups of providers by rendering them with the target exposure, beyond-accuracy objectives experience significant gains with negligible impact in recommendation utility.
Enhanced Security of Digital Picture and Text Information with Hybride Model of Hiding and Encryption Techniques

In current development, for the exchanging of data, web is most notable and critical way. With movement of web and information development, electronic media has become maybe most well known and eminent information move apparatuses. To secure these data various techniques and methods are used. In the case of digital image and text data, data hiding and scrambling techniques are utilized for secure this digital information. In this paper present the enhanced digital picture and text information security with hybrid model of data hiding and encryption. For hiding purpose utilized XOR method and for encryption purpose utilized the AES algorithm.
FedClean: A Defense Mechanism against Parameter Poisoning Attacks in Federated Learning

In Federated learning (FL) systems, a centralized entity (server), instead of access to the training data, has access to model parameter updates computed by each participant independently and based solely on their samples. Unfortunately, FL is susceptible to model poisoning attacks, in which malicious or malfunctioning entities share polluted updates that can compromise the model’s accuracy. In this study, we propose FedClean, an FL mechanism that is robust to model poisoning attacks. The accuracy of the models trained with the assistance of FedClean is close to the one where malicious entities do not participate.
Enterprise Email Server Data Protection System using Geo-Fence Technology and Machine Learning

Data sharing and protection is becoming an increasingly important element of end users' daily life as they access various systems, services, and apps. Data disclosure often occurs in real-world email services. Verification and copyright protection of multimedia content has always been a huge problem for secure data transfer media. The problem is exacerbated by the increasing use of the Internet and digital technology. But, implementing copyright protection is very complex and difficult. Digital watermarking emerged as a best solution to the problem of copyright protection. In the proposed method using Geofence technology both the Watermarking and Encryption method is used to share active content. A geofence is a virtual boundary around a physical location. A Geofence could be built dynamically in the form of a radius around a spot. Watermarking is used to hide information such as hiding private information on digital media such as photos. Encryption techniques are used to provide data safety and security. By encryption, encrypted information prevents unauthorized access and unauthorized persons from reading it. Finally, the authorized user can extract the encryption key with the help of the entered data entry process. Unauthorized access can be detected, where user information does not match the embedded information. This proposed application helps to track unauthorized access and prevent the distribution of content in the email environment Also provide group data sharing based on legal process using machine learning algorithm and provide approval for the post delivery system. suppose any unauthorized access to the information is identified, a notification to the admin regarding the forgery will be sent.
Development and validation of an internet unethical behavior scale

Internet unethical behavior (IUEB) is known to negatively affect the wellbeing of individuals and society and thus should be actively prevented. To develop relevant and effective prevention measures, a valid and reliable scale to identify IUEB is urgently needed. This study developed an adequately comprehensive and valid scale to assess IUEB in the general population. Prior studies and feedback from focus groups and experts were referenced to create the initial draft of this scale, which was used to collect data from a heterogeneous sample of 308 typical individuals. The findings revealed IUEB measured using 27 indices to be a reflective-reflective, second-order construct comprising the four underlying dimensions of online piracy, online hostility, online privacy and security breach, and online sexual harassment. The results of this study can provide librarians, educators, researchers, and policymakers with several important theoretical and practical implications for how to reduce IUEB.
Sparse coding and normalization for deep Fisher score representation

Fisher scores have been shown to be accurate global image features for classification. However, their performance is very dependent on the quality of the input features as well as the normalization steps applied to them. In this paper, we propose to embed the Fisher scores in an end-to-end trainable deep network by concentrating on two elements: adapting the encoding to the deep features and normalizing the extracted second-order statistics. Therefore, we make use of a deep sparse coding module that allows to sample the center of each Gaussian function from a learned subspace and thus to better fit the high dimensional data distribution. Second, we introduce a new normalization module that computes an approximate square root matrix normalization well adapted to the Fisher scores. These processing steps are embedded in a deep network so that all the modules work together for the sole purpose of improving classification performance. Experimental results show that this solution outperforms many alternatives in the context of material, indoor scene and fine-grained image classification.
Characterizing the research data management practices of <scp>NIH</scp> biomedical researchers indicates the need for better support at laboratory level

The study investigated the research data management (RDM) practices of biomedical researchers at the National Institutes of Health (NIH) representing various biomedical disciplines.This study aimed to analyse the state of biomedical researchers' RDM practices based on RDM practice levels (individual, laboratory, institution and external). The findings of the study are expected to provide directions to information professionals for effective RDM services.Semi-structured interviews with 11 researchers were conducted. The interviews were analysed by levels of RDM practices.The findings revealed that biomedical researchers focus on storing and sharing data and that RDM is performed mainly at the individual level. There seems to be a lack of laboratory level RDM system that allows consistent RDM practices among researchers. External RDM practice is often challenged by not having one responsible for RDM.Findings suggested a need for an agreed RDM system and customized support, particularly at the laboratory level. Also, institutional support can help researchers prepare for long term data preservation.Our suggestions emphasize the importance of RDM training and support for long term data preservation, especially at the laboratory level.
Few-Shot Object Detection with Local Correspondence RPN and Attentive Head

Existing object detection methods rely heavily on a large number of annotated bounding boxes, which is expensive to collect. In this paper, we propose a novel few-shot object detection method named GCN-FSOD. Intending to find informal local correspondence to fully explore cues of novel classes, we propose the local correspondence region proposal network (lcRPN) and the attentive detection head for few-shot detection. Taking features from the support-query image pair as inputs, lcRPN generates region proposals by mining fine-grained local correspondence with the help of GCNs. Then the proposed attentive head performs precise detection. We conduct extensive experiments on the wildly adopted MS-COCO benchmark. The proposed GCN-FSOD brings significant performance gains and outperforms the state-of-the-art by a large margin (1.7% mAP for 10-shot).
Fuzzy Structural Broad Learning for Breast Cancer Classification

We present a novel fuzzy structural broad learning model for diffusion MRI-based breast cancer classification. We first reconstruct a group of fuzzy samples from the original uncertain breast cancer data by type-2 fuzzy subsystems. To better obtain intra-class compactness and inter-class separability, we design a generalized correntropy autoencoder with strict graph constraints to fine-tune the spatial distribution of the fuzzy samples. Third, the proposed fuzzy graph structure attention network is trained by the new representation of the fuzzy samples to jointly utilize/update the features in edges and nodes and learn structural uncertainty. The final classification is solved in a closed form by calculating the pseudoinverse matrix constructed by the refined fuzzy samples and their structural features. Extensive experiments demonstrate our method can better deal with the intrinsic uncertainties of breast lesions and extract structural information of breast cancer. The proposed method outperforms other competitive methods in terms of quantitative measures, qualitative displays, and evaluation metrics for breast cancer diagnosis.
Distances Release with Differential Privacy in Tree and Grid Graph

Data about individuals may contain private and sensitive information. The differential privacy (DP) was proposed to address the problem of protecting the privacy of each individual while keeping useful information about a population. Sealfon [1] introduced a private graph model in which the graph topology is assumed to be public while the weight information is assumed to be private. That model can express hidden congestion patterns in a known transportation system. In this paper, we revisit the problem of privately releasing approximate distances between all pairs of vertices in [1]. Our goal is to minimize the additive error, namely the difference between the released distance and actual distance under private setting. We propose improved solutions to that problem for several cases.For the problem of privately releasing all-pairs distances, we show that for tree with depth h, we can release all-pairs distances with additive error O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.5</sup> h • log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.5</sup> V) for fixed privacy parameter where V the number of vertices in the tree, which improves the previous error bound O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sup> V), since the size of h can be as small as O(log V). Our result implies that a log V factor is saved, and the additive error in tree can be smaller than the error on array/path. Additionally, for the grid graph with arbitrary edge weights, we also propose a method to release all-pairs distances with additive error $\tilde O\left( {{V^{3/4}}} \right)$ for fixed privacy parameters. On the application side, many cities like Manhattan are composed of horizontal streets and vertical avenues, which can be modeled as a grid graph.
Application of Cuckoo Search Algorithm in Cost Estimation of Building Energy Engineering

In order to solve the problem that the construction project cost estimation is at a relatively advanced stage, many information about the project cannot be determined, and many unforeseen things affecting the project cost will occur in the process of project construction, which makes the preparation of project investment estimation very difficult. An optimized firefly algorithm is proposed. By introducing cuckoo algorithm, the initial population of fireflies is optimized, which greatly improves the quality of the initial population and speeds up the convergence of fireflies to the optimal solution; Secondly, the performance of CSFA algorithm is tested by six standard test functions; Finally, the algorithm is applied to solve the pressure vessel design problem. The effectiveness of the experiment is verified.
Local Resultant Gradient Vector Difference and Inpainting for 3D Text Detection in the Wild

Three-dimensional (3D) text appearing in natural scene images is common due to 3D cameras and the capture of text from different angles, which presents new problems for text detection. This is because of the presence of depth information, shadows, and decorative characters in the images. In this work, we consider those images where 3D text appears with depth, as well as shadow information for text detection. We propose a novel method based on local resultant gradient vector difference (LRGVD), inpainting and a deep learning model for detecting 3D as well as two-dimensional (2D) texts in natural scene images. The boundary of components that are invariant to the above challenges is detected by exploring LRGVD. The LRGVD uses gradient magnitude and direction in a novel way for detecting the boundary of the components. Further, we propose an inpainting method in a new way for restoring the character background information using boundaries. For a given region and the input image, the inpainting method divides the whole image into planes and then propagates the values in the planes into the missing region based on posterior probabilities and neighboring information. This results in text regions with false positives. Then, the differential binarization network (DB-Net) is proposed for detecting text irrespective of orientation, background, 3D or 2D, etc. Experiments conducted on our 3D text images and standard datasets of natural scene text images, namely ICDAR 2019 MLT, ICDAR 2019 ArT, DAST1500, Total-Text and SCUT-CTW1500, show that the proposed method is effective in detecting 3D and 2D texts in the images.
Improving the Detection of The Prostrate in Ultrasound Images Using Machine Learning Based Image Processing

This work aims to develop a method for accurate prostate segmentation in transrectal ultrasound (TRUS) images. However, accurate prostate segmentation remains a challenging task for many reasons, such as the missing/ambiguous boundary between the prostate and surrounding organs, the presence of shadow artifacts, and intra-prostate intensity heterogeneity. This work proposes a three-cascaded prostate segmentation framework, using only a few manually delineated points as a prior, including (1) an improved principal curve-based model is used to obtain the data sequences consisting of data points and projection indexes; (2) an improved differential evolution-based artificial neural network is used for training to decrease the model error; and (3) the artificial neural network’s parameters are used to explain the smooth mathematical description of the prostate contour. Experimental results show that our proposed method achieves superior segmentation performance in prostate TRUS images than state-of-the-art methods.
Development of a Normal/Abnormal RBC Detection Algorithm using YOLOv5

Blood with cells such as WBC, RBC, and platelets, is often used in health diagnoses using morphological analyses. Among them, RBC is used as an indicator for diagnosing diseases such as diabetes, anemia, and heart failure; however, their accuracy is poor due to subjective analyst interpretations since they change reversibly based on various environmental factors such as viscosity, osmotic pressure, and calcium in the blood. In this study, an algorithm that objectively analyzes the shape of RBC and measures the ratio using YOLOv5, an AI technology, was developed and used to distinguish them as normal or abnormal. Data was augmented using image processing technology due to low data, and the YOLOv5 model was applied after labeling, where it was classified into four classes. The developed algorithm was verified through precision and recall, and subsequently applied to the test data to distinguish normal/abnormal red blood cells to obtain a ratio.
Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment

The multi-sonar distributed fusion system has been pervasively deployed to jointly detect and track marine targets. In the realistic scenario, the origin of locally transmitted tracks is uncertain due to clutter disturbance and the presence of multi-target. Moreover, attributed to the different sonar internal processing times and diverse communication delays between sonar and the fusion center, tracks unavoidably arrive in the fusion center with temporal out-of-sequence (OOS), both problems pose significant challenges to the fusion system. Under the distributed fusion framework with memory, this paper proposes a novel multiple forward prediction-integrated equivalent measurement fusion (MFP-IEMF) method, it fuses the multi-lag OOST with track origin uncertainty in an optimal manner and is capable to be implemented in both the synchronous and asynchronous multi-sonar tracks fusion system. Furthermore, a random central track initialization technique is also proposed to detect the randomly born marine target in time via quickly initiating and confirming true tracks. The numerical results show that the proposed algorithm achieves the same optimality as the existing OOS reprocessing method, and delivers substantially improved detection and tracking performance in terms of both ANCTT and estimation accuracy compared to the existing OOST discarding fusion method and the ANF-IFPFD method.
Gaussian similarity preserving for cross-modal hashing

Cross-modal hashing (CMH) has gained considerable attention due to its low storage and fast query speed. Most existing CMH research pursues the binary codes of each modality by preserving a similarity matrix which is computationally expensive. In this paper, we propose a Gaussian similarity preserving method for cross-modal hashing. By using the semantic transformation, our model can avoid computing the similarity matrix explicitly. We further employ the sequential learning approach to reduce the quantization error. Experimental results on three benchmark datasets clearly show that our proposed method can outperform the state-of-the-art methods.
Multimedia Systems and its Role in Adaptive Network Management

Multimedia systems are the systems that are capable of processing multimedia applications and data. This is characterised by processing, generation, storage, rendition and manipulation of the multimedia information. Additionally, adaptive network is the new approach, which expands upon autonomous networking concepts for transforming static networks into programmable and dynamic environments driven by intelligence and analytics. Autonomous network operates without much human intervention, and it configures, maintains and monitors itselfindependently. This research aimed to discuss the multimedia systems as well as its role in adaptive network management. It has provided a research rationale to discuss the issue and then use multimedia for effective adaptive network management. Moreover, this paper has provided a literature review, where it has provided the details of the multimedia system and benefits of it. In addition, it has identified some gaps from this literature so that in future other studies can mitigate these gaps by conducting better research. Moreover, the research has collected secondary data to get relevant information on the selected topic. It has been considered a secondary data collection method, because it is cost-effective and with help of it, this study could access the data of both primary and secondary research. Furthermore, the findings have suggested that multiple media systems are used for different purposes and enhance the adaptive network sector. However, the multimedia product is costly and it requires electronics that again increase the cost and that is why it is a huge advantage of multimedia systems.
Kuiper Belt: Utilizing the “Out-of-natural Angle” Region in the Eye-gaze Interaction for Virtual Reality

The maximum physical range of horizontal human eye movement is approximately 45°. However, in a natural gaze shift, the difference in the direction of the gaze relative to the frontal direction of the head rarely exceeds 25°. We name this region of 25° − 45° the “Kuiper Belt” in the eye-gaze interaction. We try to utilize this region to solve the Midas touch problem to enable a search task while reducing false input in the Virtual Reality environment. In this work, we conduct two studies to figure out the design principle of how we place menu items in the Kuiper Belt as an “out-of-natural angle” region of the eye-gaze movement, and determine the effectiveness and workload of the Kuiper Belt-based method. The results indicate that the Kuiper Belt-based method facilitated the visual search task while reducing false input. Finally, we present example applications utilizing the findings of these studies.
Density matrix reconstruction using non-negative matrix product states

Quantum state tomography is a key technique for quantum information processing but is challenging due to the exponential growth of its complexity with the system size. In this work we propose an algorithm which iteratively finds the best non-negative matrix product state approximation based on a set of measurement outcomes whose size does not necessarily grow exponentially. Compared to the tomography method based on neural network states, our scheme utilizes a so-called tensor train representation that allows straightforward recovery of the unknown density matrix in the matrix product operator form. As applications, the effectiveness of our algorithm is numerically demonstrated to reconstruct the ground state of the XXZ spin chain under depolarizing noise.