Reviewerly/exHarmony huggingface dataset:
A Convolutional Neural Network Approach for Detecting Malignancy of Ovarian Cancer

Ovarian malignant growth has a poor endurance rate because general analysis and improved techniques are required for its initial discovery. Ovarian malignancy is the sixth most natural disease in women, causing 152,000 deceases overall yearly. To reduce this rate, there is a need for an initial discovery of the disease. We design a deep learning method for primary recognition of ovarian cancer using histopathological images. The deep convolutional neural network is used for the process. Feature extraction is done by using DenseNet-201 model of CNN. Malignant and benign cancer types are classified. In the classification process, the PLCO dataset is used with highest accuracy achieved is 94.73, precision is 0.91, recall and F1-score are 1.00 and 0.95, respectively. Tentative results and examination of other prior work clarify very dependable execution and productivity of the proposed work.
Enhance Computing Performance Through Deep Learning and Blockchain Integration for Public Health Problems

Blockchain is one of the most phenomenal innovations and becomes an attraction to be widely implemented in public health. Blockchain manages and accesses various data sets distributed across all users. It requires tools that can solve this data processing problem. Deep learning can be used to analyses and process public data efficiently and rapidly. Blockchain and deep learning integration support efforts to address accuracy, latency, centralization, security, and privacy issues. This research proposes the concept and architecture of blockchain and deep learning integration for public health problems. Results show that deep learning is capable of classifying cases with more than 80% accuracy and dengue fever cases forecasting with average RMSE 10,39
Construction of a Knowledge Base for Empirical Knowledge in Neurosurgery

Neurosurgeons accumulate a variety of empirical knowledge through surgeries. Post-operative reports, incident reports, and accident reports are effective means of recording and sharing empirical knowledge, but they are costly to analyze them, and new methods of sharing knowledge are needed. In addition, the interface using CG technology, which is used in surgical planning, is actively used as a means for doctors to easily obtain information, but it is not widely used for knowledge sharing. In this research, we aim to build a knowledge base to convey and utilize physicians’ know-how, which is difficult to convey, by accurately expressing empirical knowledge using CG technology. One of the challenges in sharing empirical knowledge is the difficulty of handling medical information from the viewpoint of personal information protection. Medical information is data that can easily identify individuals and that has a very high importance of rare data. Therefore, we examined the environment for using medical information and appropriate anonymization. First, we proposed a method for constructing an ontology for neurosurgery based on the medical ontology that has been studied in the medical field by organizing the structure of empirical knowledge of doctors. In addition, we designed and fabricated a prototype interface using 3D models as a system for data input and search display. We selected the glTF format as the 3D model format to be used in this study. In this paper, we report on the construction of a knowledge base for sharing empirical knowledge of neurosurgery, and the evaluation of the ontology constructed by the proposed method.
Sparse Coding-based Intra Prediction in VVC

Intra prediction is crucial to video coding as it is the only option for prediction when motion compensation either fails or no reference frames are available. Hence, current state-of-the-art video coding standards utilize numerous intra prediction modes in order to predict different angled structures as well as smooth areas. This contribution introduces an additional mode for intra prediction which is based on the concepts of Dictionary Learning (DL), Sparse Coding (SC) [1] and adjusted Anchored Neighborhood Regression (ANR) [2] to be able to adapt to more arbitrary structures. The general idea is built on trained dictionaries, which sparsely represent the reference area of a block to be predicted. Alongside learning the dictionaries, linear projection matrices, projecting the reference areas to the corresponding blocks, are trained with ANR. For the actual intra prediction step, each given reference area is then projected onto the to-be-predicted block by multiple linear projections, which are blended according to the sparse codes representing the reference area. Experimentally, offering the proposed mode to the state-of-the-art video coding standard Versatile Video Coding (VVC) outperforms the traditional VVC modes: In particular, -0.26% BD-rate gains in comparison to the VVC reference software VTM-9.3 and a usage percentage of 12.83% can be achieved on average for the All Intra (AI) coding configuration. Furthermore, a peak coding gain of -0.6% and a usage percentage of 26.66% is observed for the same setup.
On One Procedure for Obtaining an Interpreted and High-Precision Classifier for the Method of Logical Analysis of Data

A procedure for obtaining an interpreted and high-precision classifier which consists of two steps is proposed. At the first step, the rules of the original classifier, of which it consists, are modified using the aggregation procedure. It is applied to each rule and is aimed at making it more informative. At the second step, the classifier reduction procedure is performed based on the normalized boosting criterion. An empirical confirmation of the expediency of such a procedure is given. #CSOC1120.
CompactNet

Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications. Meanwhile, due to the complex model structures against strict latency and memory restriction, the implementation of CNN models on the resource-limited platforms is becoming more challenging. This work proposes a solution, called CompactNet, which automatically optimizes a pre-trained CNN model on a specific resource-limited platform given a specific target of inference speedup. Guided by a simulator of the target platform, CompactNet progressively trims a pre-trained network by removing certain redundant filters until the target speedup is reached and generates an optimal platform-specific model while maintaining the accuracy. We evaluate our work on two platforms of a mobile ARM CPU and a machine learning accelerator NPU (Cambricon-1A ISA) on a Huawei Mate10 smartphone. For the state-of-the-art slim CNN model made for the embedded platform, MobileNetV2, CompactNet achieves up to a 1.8x kernel computation speedup with equal or even higher accuracy for image classification tasks on the ImageNet dataset, which outperforms other successful CNN optimizing techniques. Compared with the state-of-the-art Neural Architecture Searching (NAS) work, the optimal model generated through our CompactNet is faster and can be applied to bigger datasets like ImageNet. Furthermore, the optimizing process is much faster than those searching approaches.
Human Motion Prediction Using Adaptable Recurrent Neural Networks and Inverse Kinematics

Human motion prediction, especially arm prediction, is critical to facilitate safe and efficient human-robot collaboration (HRC). This paper proposes a novel human motion prediction framework that combines a recurrent neural network (RNN) and inverse kinematics (IK) to predict human arm motion. A modified Kalman filter (MKF) is applied to adapt the model online. The proposed framework is tested on collected human motion data with up to 2 s prediction horizon. The experiments demonstrate that the proposed method improves the prediction accuracy by approximately 14% comparing to the state-of-art on seen situations. It stably adapts to unseen situations by keeping the maximum prediction error under 4 cm, which is 70% lower than other methods. Moreover, it is robust when the arm is partially occluded. The wrist prediction remains the same, while the elbow prediction has 20% less variation.
A Corpus-Based Study on the Classification and Processing Mechanism of English-Chinese One-To-Many Translation-Equivalent Word Pairs

To study the correspondence relation between one English word and its Chinese equivalent words as well as their processing mechanism in bilinguals’ mental lexicons, we make a corpus-based calculation to acquire frequencies of some English-Chinese one-to-many translation-equivalent word pairs (i.e., the respective usage frequency of multiple Chinese words corresponding to an English word), according to which we introduce a classification criterion to divide these word pairs into two groups with the lower and higher maximum values of corresponding frequency. To verify whether this criterion reflects the activation in the actual processing, a semantic consistency decision task under the priming paradigm is carried out. Its results demonstrate that the actual activation of the English-Chinese translation-equivalent word pairs can be predicted by their corresponding frequencies obtained from statistics drawn based on a corpus. Additionally, the research further indicates that current English-Chinese dictionaries are far from sufficient coverage of all the translation-equivalent word pairs, and excluding a large number of words in the actual use of language.
A bias-adjusted estimator in quantile regression for clustered data

Quantile regression models with random effects are useful for studying associations between covariates and quantiles of the response distribution for clustered data. Parameter estimation is examined for a class of mixed-effects quantile regression models, with focus on settings with many but small clusters. The main contributions are the following: (i) documenting that existing methods may lead to severely biased estimators for fixed effects parameters; (ii) proposing a new two-step estimation methodology where predictions of the random effects are first computed by a pseudo likelihood approach (the LQMM method) and then used as offsets in standard quantile regression; (iii) proposing a novel bootstrap sampling procedure in order to reduce bias of the two-step estimator and compute confidence intervals. The proposed estimation and associated inference is assessed numerically through rigorous simulation studies and applied to an AIDS Clinical Trial Group (ACTG) study.
Methods and Models of Resource Allocation in Load Balancing Clusters

Modern data centers are complex and effective solutions for managing enterprises and corporations, organizing data processing and storage systems, and distributing software applications among available resources. This paper considers load balancing clusters of data centers that contain a definite set of application servers, file servers, data storage systems, and an input-output system, all interconnected by a switching system and communication channels. The goal of research is to increase the efficiency of virtualized data centers using a new methodological approach, as well as rational methods and models, to distribute their load among the available software and hardware means. Interrelated mathematical methods and models, necessary to develop specialized software for the rational planning and allocation of the data center’s physical resources on a given time interval, are proposed. These methods and models are based on the system of correct mappings of the set of request parameters into the known characteristics of the data center’s physical resources. As an optimization criterion, the maximum system performance for a certain time interval is used.
Sentiment Analysis of Tweets Using Emojis and Texts

Nowadays, social media generate massive volumes of data containing valuable information to several applications such as marketing, business, and politics which are interested to analyze opinions and sentiments of individuals. A lot of efforts have been directed towards sentiment analysis (SA) based on texts; however, limited efforts have been directed towards the study and analysis of other DTs such as emojis and other multimedia. The processing of other data types (DTs) not only can generate complementary information, but we argue that the combined processing of heterogeneous DTs (HDTs), called heterogeneous data mining (HDM), should lead to more accurate results. In this work, we propose the HDM of tweets using both texts and emojis for bi-sense SA; the SA results using HDM were more accurate than the exclusive use of either texts or emojis, and interestingly the use of emojis achieved a higher accuracy than texts.
Towards Automated Detection of Higher-Order Memory Corruption Vulnerabilities in Embedded Devices

The rapid growth and limited security protection of the networked embedded devices put the threat of remote code execution related memory corruption attacks front and center among security concerns. Current detection approaches can detect single-step and single-process memory corruption vulnerabilities well by fuzzing tests, and often assume that data stored in the current embedded device or even the embedded device connected to it is safe. However, an adversary might corrupt memory via multi-step exploits if she manages first to abuse the embedded application to store the attack payload and later use this payload in a security-critical operation on memory. These exploits usually lead to persistent code execution attacks and complete control of the device in practice but are rarely covered in state-of-the-art dynamic testing techniques. To address these stealthy yet harmful threats, we identify a large class of such multi-step memory corruption attacks and define them as higher-order memory corruption vulnerabilities (HOMCVs). We can abstract the detailed multi-step exploit models for these vulnerabilities and expose various attacker-controllable data stores (ACDS) that contribute to memory corruption. Aided by the abstract models, a dynamic data flow tracking (DDFA) based solution is developed to detect data stores that would be transferred to memory and then identify HOMCVs. Our proposed method is validated on an experimental embedded system injected with different variants of higher-order memory corruption vulnerabilities and two real-world embedded devices. We demonstrate that successful detection can be accomplished with an automatic system named Higher-Order Fuzzing Framework (HOFF) which realizes the DDFA-based solution.
Hardware Design and Implementation of a Wireless Chaotic Text Encryption Scheme

This work proposes a chaotic text encryption scheme implemented in a microcontroller device. Initially, a chaotic 1D map is proposed and studied through its bifurcation diagrams and Lyapunov exponent diagram. The map showcases a constant chaotic behavior, which makes it suitable for use in encrytption. Based on this map, a pseudo random bit generator is designed. This generator is then used as a source of deterministic randomness, in order to encrypt a text message, using XOR. The complete scheme is then implemented in two microcontroller devices that can each implement the encryption and decryption procedures. This allows the safe transmission of an information text between the two devices, and can be the basis for further experimentation.
Single Image Super Resolution Using Multi-path Convolutional Neural Network

Nowadays, deep learning-based methods are used in single image super resolution tasks. Even though these methods yield acceptable results, they have the problems of requiring more data and memory space. In this paper, a method based on convolutional neural network is proposed, which not only requires less data and memory space for learning, but also leads to accurate results. In the proposed approach, a three-path network is presented which has the same subnetworks fed by different Low Resolution (LR) images. The original LR data are fed into the first path and its high frequency-discarded versions are fed into the other paths. In this way, the network is forced to detect and learn high level features from low information inputs via parallel feature extractors. Furthermore, multi-scale manner is utilized to simultaneously detect coarse and fine features. In addition, feature maps have been fully exploited by concatenating and merging them throughout the network. Moreover, a strategy of parallelizing upsampling methods is proposed to ensure the reconstruction of the High Resolution (HR) image is accurately verified. Experimental results demonstrate that the proposed network outperforms most of the state-of-the-art methods in terms of the depth, the number of parameters and input data, and overall complexity of the network. The proposed method also improves visual performance and reaches higher evaluation metrics compared to the most of the existing methods.
Clustering Text: A Comparison Between Available Text Vectorization Techniques

The concept of clustering is of primitive importance in the field of unsupervised learning. We have always required the need to categorize data with respect to some parameters. More or less, this can become quite challenging with the increasing amount of jargon, which requires expert domain knowledge and with the increasing amount of data. Sometimes, we even do not possess enough knowledge about the data to divide it into categories. We simply do not possess past experiences to train a classification model for categorizing data. This paper presents a comparative study on the techniques available for clustering text data using only text vectorization methods.
Developing an Effective Graph Based Block Chain Framework

Abstract Blockchain’s decentralized characteristic is recognized as a potential technology to deliver secure and safe resources. However, the existing blockchain networks cannot fulfil the transaction given the limited bandwidth for practical application demand. In this article, the cubic design of the conventional public blockchain is strengthened with the Directed Graph Probabilistic model. In the current structure, blocks of the lightweight structure are ordered in grades and width. We develop protocols to position newly created blocks in order to make them more effective and secure. It increases confidentiality and time for authentication of transactions in contrast with standard blockchain protocols and enjoys the accuracy and liveliness of blockchain. This paper introduces two potential methods for targeting opposition parties and we also show that the procedure against them is safe and stable. Experiments will generate three hundred inputs every second that is 64 times the output of Bitcoin’s transaction. This time Bitcoin’s production will hit 27 times its Ethereum’s.
Software fault prediction using machine learning techniques with metric thresholds

BACKGROUND: Fault data is vital to predicting the fault-proneness in large systems. Predicting faulty classes helps in allocating the appropriate testing resources for future releases. However, current fault data face challenges such as unlabeled instances and data imbalance. These challenges degrade the performance of the prediction models. Data imbalance happens because the majority of classes are labeled as not faulty whereas the minority of classes are labeled as faulty. AIM: The research proposes to improve fault prediction using software metrics in combination with threshold values. Statistical techniques are proposed to improve the quality of the datasets and therefore the quality of the fault prediction. METHOD: Threshold values of object-oriented metrics are used to label classes as faulty to improve the fault prediction models The resulting datasets are used to build prediction models using five machine learning techniques. The use of threshold values is validated on ten large object-oriented systems. RESULTS: The models are built for the datasets with and without the use of thresholds. The combination of thresholds with machine learning has improved the fault prediction models significantly for the five classifiers. CONCLUSION: Threshold values can be used to label software classes as fault-prone and can be used to improve machine learners in predicting the fault-prone classes.
Design of an FPGA Hardware Optimizing the Performance and Power Consumption of a Plenoptic Camera Depth Estimation Algorithm

Plenoptic camera based system captures the light-field that can be exploited to estimate the 3D depth of the scene. This process generally consists of a significant number of recurrent operations, and thus requires high computation power. General purpose processor based system, due to its sequential architecture, consequently results in the problem of large execution time. A desktop graphics processing unit (GPU) can be employed to resolve this problem. However, it is an expensive solution with respect to power consumption and therefore cannot be used in mobile applications with low energy requirements. In this paper, we propose a modified plenoptic depth estimation algorithm that works on a single frame recorded by the camera and respective FPGA based hardware design. For this purpose, the algorithm is modified for parallelization and pipelining. In combination with efficient memory access, the results show good performance and lower power consumption compared to other systems.
Application of Virtual Reality in English Teaching

VR technology has the advantages of creating vivid situation, constructing immersive learning environment, visualizing the abstract knowledge, innovating educational approaches and assessment methods. This paper tries to conduct the meta-analysis about current research of the application of VR technology in English teaching both at home and abroad. Four typical technologies in VR-assisted English teaching are summarized, including virtual characters, speech and semantic recognition, intelligent dialogue, and panorama technology. At the same time, this paper explores the development tendency of VR and English teaching from four perspectives such as classroom learning, second classroom, English education games, English for Special Professional Fields. In summary, VR technology can better serve language education and promote the innovation and development of English teaching.
Tor Detection using a Machine Learning Approach Using Correlation based Feature Selection with Best First and Random Forest

Since the evolution of the internet and accompanying services and technologies, people all over the world had started enjoying these developments, but unfortunately this wasn't without cost. On one side, companies and service providers had found ways to maximize their benefit from their customers' usage of their services by tracking them and selling their information exposing their privacy. On the other side, criminals had also found their ways to avoid security measures and bypass technologies to conduct their crimes. One of the technologies that was developed to address the previous issues was TOR, which can provide privacy to users and unfortunately, can provide protection for criminals by hiding and disguising their traffic. Technology developers and researchers successfully tried to develop approaches to detect TOR traffic, but these solutions suffered from shortcomings such as low accuracy and efficiency. In this research, we present an ML approach using Correlation-based Feature Selection with Best First and Random Forest. We used a dataset from (UNB CIC) University implemented in WEKA. Our results show that time based characteristics can be used to effectively detect Tor traffic with very high precision and efficiency.