Reviewerly/exHarmony huggingface dataset:
TFNet: Transformer Fusion Network for Ultrasound Image Segmentation

Automatic lesion segmentation in ultrasound helps diagnose diseases. Segmenting lesion regions accurately from ultrasound images is a challenging task due to the difference in the scale of the lesion and the uneven intensity distribution in the lesion area. Recently, Convolutional Neural Networks have achieved tremendous success on medical image segmentation tasks. However, due to the inherent locality of convolution operations, it is limited in modeling long-range dependency. In this paper, we study the more challenging problem on capturing long-range dependencies and multi-scale targets without losing detailed information. We propose a Transformer-based feature fusion network (TFNet), which fuses long-range dependency of multi-scale CNN features via Transformer to effectively solve the above challenges. In order to make up for the defect of Transformer in channel modeling, will be improved by joining the channel attention mechanism. In addition, a loss function is designed to modify the prediction map by computing the variance between the prediction results of the auxiliary classifier and the main classifier. We have conducted experiments on three data sets, and the results show that our proposed method achieves superior performances against various competing methods on ultrasound image segmentation.
Face Detection Using Only the Chrominance Component

Face detection is one of the most important steps in face recognition. It is quite a complex task from the computer vision point of view. Several attempts have been made to develop a robust face detection scheme. However, it is still problematic to detect the proper face region from a given face image or video frame at different orientations. Violas Jones’ cascade classifier is considered to be the most popular face detection scheme which can detect face regions for frontal images. Other approach to face detection is the skin color based face detection. Proposed here is the face detection method which detect face region from the chrominance component of YCbCr color space. The original face image in RGB is first converted to YCbCr and from the red chrominance component, the skin region is detected using a specific threshold range. From the detected skin regions, face region is detected from the inner width analysis. This gives the proper width of face region without including extra regions around the face. It also gets the hairline as top of bounding box of the face region but the bottom is bit not always chin of the face. It can detect face regions more appropriately at different orientations as compared to cascade classifier. Moreover, it is simple and fast and hence can also be conveniently used for real-time face detection and recognition applications.
Implementation of Data Deduplication Detection and Removal on Cloud Using 64-Bit Architecture of SHA-256

In today’s era due to the pandemic, the data storage on the cloud increases tremendously. Million bytes of data in the form of text files, PDFs, pictures, videos are uploaded on the servers, for the submission of assignments, exam papers, quizzes, practical files, and other purposes like sharing of notes, images, video’s, etc. This increases the storage space requirement of the server extremely. Sometimes Students are uploading so many copies of the same file intentionally or unintentionally as they are new to the system. Now Schools, Colleges, Educational Institutes, and Companies are buying more server space to fulfill the space crunch on the cloud. But for effective utilization of the server space all the duplicate data existing on it is to be removed and effective methods are required for backup of the server data. In this paper, we discuss one efficient, reliable and secure method for detection and removal of data deduplication using 64- bit architecture of SHA- 256 for effective utilization of storage space.
Dual-Gradients Localization Framework With Skip-Layer Connections for Weakly Supervised Object Localization

This article focuses on generating object locations in a given image while only using image-level annotations. Towards this end, we present a simple and effective training-free framework, named Dual-Gradients Localization (DGL) framework. The key idea of the proposed DGL framework is to leverage two kinds of gradients to achieve precise localization on any convolutional layer of a classification model during the testing stage. Concretely, the DGL framework is developed based on two branches: 1) Pixel-level Class Selection, leveraging gradients of the target class to identify the correlation ratio of pixels to the target class within any convolutional feature maps, and 2) Class-aware Enhanced Maps, utilizing linear relationship in gradients of the classification loss function to mine entire target object regions. To further polish the details of objects, we apply the skip-layer connections to the classification model, which concatenates the high- and low-level layers to achieve classification. In such a case, DGL with Skip-layer Connections (DGL-SC) can capture more edge information on the high-level layer. In addition, we propose a Localization Maps Selection method to evaluate the quality of the localization map and provide a way for automatically selecting localization maps produced on different layers. Extensive experiments on public ILSVRC and CUB-200-2011 datasets show the effectiveness of the proposed DGL framework. Especially, our DGL-SC obtains a new state-of-the-art gt-known localization error of 27.35% on the ILSVRC benchmark.
Online and Offline Hybrid Teaching Quality Information Analysis Under the Background of Big Data

With the development of the Internet age, blended teaching is increasingly being applied to various colleges and universities. How to evaluate students’ learning effects has important research value. The purpose of this article is to study hybrid teaching quality information based on the background of big data. On the basis of the preliminarily selected evaluation indicators, through investigating expert opinions, supplementing and improving the mixed teaching evaluation indicators as the proposed evaluation indicators; then surveying students to further determine the mixed teaching evaluation index system; Among them, “professional ability enhancement” and “inter-student communication” have reached a significant correlation, with a correlation coefficient of 0.55, indicating that in blended teaching, the better the student-student communication, the better the enhancement of students’ professional ability.
DICDP: Deep Incomplete Clustering with Distribution Preserving

Clustering is a fundamental task in the computer vision and machine learning community. Although various methods have been proposed, the performance of existing approaches drops dramatically when handling incomplete high-dimensional data (which is common in real world applications). To solve the problem, we propose a novel deep incomplete clustering method, named Deep Incomplete Clustering with Distribution Preserving (DICDP). To avoid insufficient sample utilization in existing methods limited by few fully-observed samples, we propose to measure distribution distance with the optimal transport for reconstruction evaluation instead of traditional pixel-wise loss function. Moreover, the clustering loss of the latent feature is introduced to regularize the embedding with more discrimination capability. As a consequence, the network becomes more robust against missing features and the unified framework which combines clustering and sample imputation enables the two procedures to negotiate to better serve for each other. Extensive experiments demonstrate that the proposed network achieves superior and stable clustering performance improvement against existing state-of-the-art incomplete clustering methods over different missing ratios.
Comparative Framework for Information Security Risk Assessment Model

Due to the internet, which connects thousands of networks to each other, today's world is considered a global village. Because the majority of information on electronic devices is now collected, processed, saved, and distributed through networks, IT is also involved with security, privacy, usage, and retention. Information security is a growing issue for all organizations with IT facilities. Security in these organizations is currently becoming a serious concern, and these organizations are not protected because there are many threats to internal and external security that can affect the vulnerable assets of these organizations. The primary concern is the exploitation of vulnerability by threats.Information Security Risk Assessment techniques are required to protect data from various threats and vulnerabilities. There are various methodologies for risk management of information security and several more on a consistent basis. They all have the same underlying goal, but strive to accomplish it from very different viewpoints and to deal with problems differently.The strategy of this research paper is to provide a comparative framework for Information Security Risk Assessment Model. This research paper also provides the comparative study of various Information Security Risk Assessment Model based on proposed Comparative Framework.
Blockchain for IoT-based medical delivery drones

The internet of things (IoT) has expanded the reachability of pervasive applications in different domains. Smart healthcare is one such area where IoT-based approaches have been successfully demonstrated in the recent past. However, issues related to the scaling of large ad hoc elements are still a major challenge. Medical delivery drones are a new variant of unmanned aerial vehicle (UAV) to pave a holistic orientation into the smart health service paradigm. Lack of augmentation aware notions has resisted the realization of an actual intervention of IoT-based medical delivery via UAVs. Proper architectural frameworks and models are needed to efficiently integrate IoT with medical delivery drones. However, major security drawbacks should be investigated a priori to imply the health delivery services in coming days. We can use blockchain-centric approaches to fill this gap in the IoT-based medical delivery drone ecosystem. In this chapter, we first present details about drone suppliers and perform a comparative analysis of such drones for medical delivery. Secondly, we present a state of the art on blockchain-assisted, IoT-based, drone-centric, medical delivery. Finally, we depict some key challenges associated with existing drone delivery mechanisms and prescribe future directions. We conclude that drones have huge potential to revolutionize the status of medical delivery with support from the IoT and blockchain platforms. Overall, the medical delivery domain is envisaged to augment existing medical care with enriched security.
D*+: A Risk Aware Platform Agnostic Heterogeneous Path Planner

This article establishes the novel D ∗ + , a risk-aware and platform agnostic heterogeneous global path planner for robotic navigation in complex environments. The proposed planner addresses a fundamental bottleneck of occupancy-based path planners related to their dependency on accurate and dense maps. More specifically, their performance is highly affected by poorly reconstructed or sparse areas (e.g. holes in the walls or ceilings) leading to faulty generated paths outside the physical boundaries of the 3-dimensional space. As it will be presented, D ∗ + addresses this challenge with three novel contributions, integrated into one solution, namely: a) the proximity risk, b) the modeling of the unknown space, and c) the map updates. By adding a risk layer to spaces that are closer to the occupied ones, some holes are filed and thus the problematic short-cutting through them to the final goal is prevented. The novel established D ∗ + also provides safety marginals to the walls and other obstacles, a property that results in paths that do not cut the corners that could potentially disrupt the platform operation. D ∗ + has also the capability to model the unknown space as risk free areas that should keep the paths inside, e.g in a tunnel environment and thus heavily reducing the risk of larger shortcuts through openings in the walls. D ∗ + is also introducing a dynamic map handling capability that continuously updates with the latest information acquired during the map building process, allowing the planner to use a constant map growth and resolve cases of planning over outdated sparser map reconstructions. The proposed path planner is also capable to plan 2D and 3D paths by only changing the input map to a 2D or 3D map and it is independent of the dynamics of the robotic platform. The efficiency of the proposed scheme is experimentally evaluated in multiple real life experiments where D ∗ + is producing successfully proper planned paths, either in 2D in the use case of the Boston Dynamics Spot robot or 3D paths in the case of an unmanned areal vehicle in varying and challenging scenarios.
A Digital Body Farm for Collecting Deleted File Decay Data

Recovering deleted data can be a time-consuming task during digital forensic investigations. As tedious as the task is, it may not produce useful results. New files written to locations containing previously-deleted file data may render some or even all of the deleted file unrecoverable. Insights into the factors that influence deleted file decay are required to enable digital forensic professionals to determine if attempting file recovery is a wise use of time. Significant research efforts have focused on deleted file decay, but gaps in knowledge still exist. This chapter discusses an attempt at collecting data to help discover how deleted file content decays over time in computing systems running the Microsoft New Technology Filesystem (NTFS). In particular, it describes the implementation of a digital body farm that uses differential analysis to monitor and record patterns of decay as deleted data are erased or overwritten on secondary storage media attached to a live system. The collection of realistic file decay data and relevant system parameters can be used to create a model that provides useful insights into the deleted file decay process.
The Effect of Parenting, Learning Motivation, and Learning Discipline on Learning Outcomes of College Student (Study Case of State University Faculty of Economics Students Jakarta)

The purpose of this study was to determine the effect of parenting, learning motivation, and learning discipline on learning outcomes in students of the Faculty of Economics, State University of Jakarta. The type of research used is quantitative method with descriptive analysis. The data collection technique was carried out using a questionnaire method with a Likert scale of one to five. The data analysis techniques used in this study are: Outer Model by calculation Convergent Validity, Discriminant Validity, Composite Reliability, Cronbach's Alpha andInner Modelby calculation T-Statisticc, R- Square, f-Square, and VIF whose calculations are performed using the SmartPLS 3.0 application. The results obtained are that: a) there is a positive influence between parenting patterns on learning discipline; b) there is a positive influence between parenting patterns on learning outcomes; c) there is a positive influence between learning motivation and learning discipline; d) there is a positive influence between learning motivation on learning outcomes; e) there is a positive influence between learning disciplines on learning outcomes.
A Comprehensive Survey on Machine Reading Comprehension: Models, Benchmarked Datasets, Evaluation Metrics, and Trends

Machine reading comprehension (MRC) is a core process in question answering systems. Question answering systems are capable to extract the answer from relevant resources automatically for the questions posed by humans, and machine reading comprehension brings attention to a textual understanding with answering questions. Recent advancements in deep learning and natural language processing pave the way to improve the accuracy in question answering systems with the major developments in neural machine reading comprehension, transfer learning, deep learning-based information retrieval, and knowledge-based information extraction. Herein, this research analysis included the comprehensive analysis of MRC tasks, benchmarked datasets, classic models, performance evaluation metrics, and modern trends and techniques on MRC.
Efficient Designated-Server Proxy Re-encryption with Keyword Search for Big Data

Against the background of the era of big data and cloud computing, cloud platforms have become the first choice for data storage. While cloud platforms also face serious threats to data security and user privacy. At the same time, users will be faced with the problem of how to efficiently retrieve ciphertext on the cloud. Searchable encryption allows users to directly retrieve ciphertext data on the cloud through keywords, which provides an effective method for solving efficient retrieval of ciphertext on the cloud. As an important branch of searchable encryption technology, proxy re-encryption can realize the sharing of ciphertext among multiple users. However, most of the existing schemes have security shortcomings or functional defects, such as inability to resist keyword guessing attack, and do not support search authority authorization. A new designated-server proxy re-encryption with keyword search scheme was proposed, which not only can resist keyword guessing attack, but also realizes the function of sharing search authority. Finally, we give the performance and security analysis. The experimental results show that our scheme is superior compared with other related schemes.
Artificial Neural Network-Based Model for the Prediction of Evaporation in Agriculture

This research aims to establish a model to predict the evaporation in the fields of rice crop. To construct the predictive model, an ANN, i.e., artificial neural network, which uses the architecture known as multilayer perceptron. The model was built taking various climatic parameters into consideration such as temperature both maximum and minimum (MaxTemp and MinTemp), wind speed (WS), relative humidity (RH1 as morning value and RH2 as evening value), rainfall (RF), and sunshine hours (SSH). The data was collected for the period 1993–2008, for rice crop fields located in Rajendranagar, Hyderabad, a part of India. Mean absolute error and mean square error (MAE & MSE) metrics were used to validate the estimation of the forecast output produced based on the neural model by identifying forecast errors. The ability to forecast the evaporation value of the Rajendranagar area is related to current weather data, which are used as the main feature of the model used for prediction. Based on the predicted values using the MLP network-based ANN model, the lowest MSE and MAE error values were obtained.
Emotion and Gesture Guided Action Recognition in Videos Using Supervised Deep Networks

Emotions and gestures are essential elements in improving social intelligence and predicting real human action. In recent years, recognition of human visual actions using deep neural networks (DNNs) has gained wide popularity in multimedia and computer vision. However, ambiguous action classes, such as “praying” and “pleading,” are still challenging to classify due to similar visual cues of action. We need to focus on attentive associated features of facial expressions and gestures, including the long-term context of a video for the correct classification of ambiguous actions. This article proposes an attention-aware DNN named human action attention network (HAANet) that can capture long-term temporal context to recognize actions in videos. The visual attention network extracts discriminative features of facial expressions and gestures in the spatial and temporal dimensions. We have further consolidated a class-specific attention pooling mechanism to capture transition in semantic traits over time. The efficacy of HAANet is demonstrated on five benchmark datasets. As per our knowledge, no publicly available dataset exists in the literature, which distinguishes ambiguous human actions by focusing on the visual cues of a human in action. This motivated us to create a new dataset, known as Visual Attention with Long-term Context (VALC), which contains 32 actions with about 101 videos per class and an average length of 30 s. HAANet outperforms UCF101, ActivityNet, and BreakFast-Actions datasets in terms of accuracy.
Efficient Stack Distance Approximation Based on Workload Characteristics

A stack distance of a reference is the depth from which the reference must be extracted from a stack. It has been widely applied to a variety of applications utilizing temporal locality information. However, calculating an exact stack distance requires significant computational complexity that is impractical for online production systems. This paper proposes an efficient algorithm that approximates a stack distance based on workload characteristics. The proposed algorithm utilizes two simple reference metrics: inter-reference distance and a quasi-unique reference count. More importantly, its approximation algorithm requires surprisingly simple calculation. Consequently, it significantly reduces computational overhead while exhibiting exceptional accuracy and runs extremely fast. Moreover, it allows configurable parameters for performance optimization by making optimal use of workload characteristics. Our extensive experiments with diverse realistic workloads demonstrate our stack distance approximation algorithm outperforms the current state-of-the-art algorithm with higher accuracy (3.7&#x00D7;), lower memory footprint (3.05&#x00D7;) and fast execution (less than 0.05 seconds) on average. In addition, with configuration parameters, our refined algorithm can achieve a 28% average additional accuracy improvement.
Enhanced Data-Recalibration: Utilizing Validation Data to Mitigate Instance-Dependent Noise in Classification

This paper proposes a practical approach to deal with instance-dependent noise in classification. Supervised learning with noisy labels is one of the major research topics in the deep learning community. While old works typically assume class conditional and instance-independent noise, recent works provide theoretical and empirical proof to show that the noise in real-world cases is instance-dependent. Current state-of-the-art methods for dealing with instance-dependent noise focus on data-recalibrating strategies to iteratively correct labels while training the network. While some methods provide theoretical analysis to prove that each iteration results in a cleaner dataset and a better-performing network, the limiting assumptions and dependency on knowledge about noise for hyperparameter tuning often contrast their claims. The proposed method in this paper is a two-stage data-recalibration algorithm that utilizes validation data to correct noisy labels and refine the model iteratively. The algorithm works by training the network on the latest cleansed training Set to obtain better performance on a small, clean validation set while using the best performing model to cleanse the training set for the next iteration. The intuition behind the method is that a network with decent performance on the clean validation set can be utilized as an oracle network to generate less noisy labels for the training set. While there is no theoretical guarantee attached, the method's effectiveness is demonstrated with extensive experiments on synthetic and real-world benchmark datasets. The empirical evaluation suggests that the proposed method has a better performance compared to the current state-of-the-art works. The implementation is available at https://github.com/Sbakhshigermi/EDR .