Reviewerly/exHarmony huggingface dataset:
Classification of breast abnormalities in digital mammography with a deep convolutional neural network

A novel algorithm for analysis and classification of breast abnormalities in digital mammography based on a deep convolutional neural network is proposed. Simplified neural network architectures such as MobileNetV2, InceptionResNetV2, Xception, and ResNetV2 are intensively studied for this task. In order to improve the accuracy of detection and classification of breast abnormalities on real data an efficient training algorithm based on augmentation technique is suggested. The performance of the proposed algorithm for analysis and classification of breast abnormalities on real data is discussed and compared to that of the state-of-the-art algorithms.
Priority based Scheduling Algorithm using Divisible Load Theory in Cloud

Cloud computing is a technology that works on a distributed network. It delivers computation, network, and storage as a service instead of a product, where the resources are shared and provided to the end-users’ devices. The user has to pay based on his/her consumptions. In cloud computing, entities are mainly users, service providers, and scheduling systems. The scheduler in the algorithm should arrange the jobs in such a way that the utilization of resources should be maximized and simultaneously improving quality at a reasonable cost. In the existing job scheduling process, the algorithm uses the divisible load theory to divide a large workload among the available processors. The proposed work is to modify the scheduling algorithm based on separating loads dynamically and avoiding failure. This work is done using the Cloud Simulator (CloudSim), which is a framework for simulating Cloud computing scheduling algorithms. It provides the ambiance for the creation of data centers, instances of virtual machines, user applications and computational principles and policies for managing several parts of the system.
Embedding-Driven Multi-Dimensional Topic Mining and Text Analysis

People nowadays are immersed in a wealth of text data, ranging from news articles, to social media, academic publications, advertisements, and economic reports. A grand challenge of data mining is to develop effective, scalable and weakly-supervised methods for extracting actionable structures and knowledge from massive text data. Without requiring extensive and corpus-specific human annotations, these methods will satisfy people's diverse applications and needs for comprehending and making good use of large-scale corpora.
Detection of Artificial Images and Changes in Real Images Using Convolutional Neural Networks

Image recognition has now become one of the most popular methods used in the entertainment industry, media, automotive, etc. The possibilities provided by neural networks and deep learning algorithms, cause the development of various methods for generating, modifying and falsifying information. An example would be the use of deep learning algorithms to replace faces in a video recording. Social networks, video materials are full of fake video and images. Our work proposes a method of detecting forgery on real images and detecting artificially generated images using Convolutional Neural Networks (CNN). Our approach introduces the possibility of classifying images into one of three classes: the class of real images, the class of real and modified images, and the class of artificially generated images. An important element of our work is the practical detection of modified or artificially generated images that could be used when phishing biometrically protected data. The research has been narrowed down to the facial images of people of different skin colour, nationality and age. The conducted tests show the acceptable effectiveness of our method and become a positive element of further experiments.
A Differential Evolutionary Algorithm for Image Segmentation of White Blood Cells in Acute Lymphoblastic Leukaemia Images

White blood cells play an important role by enabling a protection against any foreign substances or pathogens that enter inside the human immune system. The detection of the white blood cells is done by using the image processing techniques. The lymphoblast images are a slight modification of the white blood cells. It has nucleus, which is often large and lobed, and it helps to distinguish it with the other blood cells. In this paper, the differential evolution algorithm is used for the segmentation of white blood cells from the acute leukemic images. Differential evolution algorithm is an iterative process through which the solution gets improvised as the algorithm proceeds with less number of assumptions. The experimental results showed that DE was able to successfully segment the white blood cells.
Build: Web Services based Source Code Editor Integrate with Community Question Answer

Source code editor have different features to ease and speed-up the developer works. This paper proposed the source code editor BUILD integrated with Community Question and Answer (Q&A) and stack overflow web service. While programming developer often came across the set of questions, programming errors, thus in order to find the solutions or resolving the errors developers take reference from the different online resources (Documentations, Books, Q & A Services) through web browser. This leads to switching windows between source code editor and web-browser. To reduce development time and effort, neglect web or internet distractions and to get the specific result to questions we form a community. we combine source code editor and community Q & a services (Stack Overflow, institutional CQA). We also understand different learning resources and development tool which preferred by the developers. Explains the important of the Community Question & Answer (CQA) services.
System Hardening for Infrastructure as a Service (IaaS)

Reduce the attack vector.! Minimize risk.! When discussing the topic of system hardening, the goal is to lock down the system to make it more difficult for a hacker to break in. There exist a number of hardening standards for a variety of applications and products, but perhaps the first stop should be the operating system itself. We examine system hardening in a cloud environment to assess if standard operating system hardening benchmarks are applicable in a cloud virtual machine. Five case studies of large-scale security breaches are analyzed to determine which system hardening benchmarks would have mitigated and possibly prevented the various attacks. These examples serve as a reminder of the importance of system hardening in general, and the IaaS consumer must protect the portion of the environment under their control and responsibility. Two industry standard benchmarks (CIS and STIG) are discussed, and STIG is implemented on an AWS EC2 instance running Red Hat Enterprise Linux 7. After evaluating 207 scripted benchmarks, we recommend and pass 195, leaving only 12 failed items which form the basis of discussion for this paper. These failed items include five false positives, one known bug and two exemptions, subsequently raising our compliance score to 98%. In a real-world audit, the remaining four failed items would be scrutinized for compensating controls or strong justification for a process exemption.
Adaptive Importance Pooling Network for Scene Text Recognition

Scene text recognition (STR) has attracted extensive attention in pattern recognition community. With the development of deep learning, the object detection and sequence recognition schemes based on deep neural networks have been widely used in this task. Crucially, the discriminative features play a vital role in complex scene text backgrounds. However, for specific tasks, inappropriate pooling strategies may lose feature details. To tackle this problem, in this paper, an end-to-end based on adaptive importance pooling network (AIPN) is proposed. Concretely, we embed the novel AIP strategy into feature extraction stage. Additionally, we adopt the attention-based LSTM as decoder so that the useful image feature information regions are automatically focused while predicting final recognition results. Furthermore, to reduce the burden of feature representation for the next recognition, text rectification network (TRN) supervised by text recognition parts is utilized to normalize the input text images. Experimental results show that our model achieves inspiring performances on STR benchmark datasets IIIT5K, SVT, ICDAR-2003 and ICDAR-2013.
It Lurks Within: A Look at the Unexpected Security Implications of Compliance Programs

Compliance programs help protect organizations' intellectual property and valuable resources through mandated security controls; however, organizations often conflate compliance with strong security. Understanding the security gaps that place fully compliant organizations at risk is essential.
A Unified Labeling Model for Open-Domain Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis involves aspect term extraction and sentiment prediction towards aspect terms. Recently, more researchers have proposed integrated approaches to accomplish two tasks simultaneously. However, such approaches always limit the domain, quantity, length and category of aspect terms, which greatly restricts its use. This paper aims to model the joint task as an extension of sequence labeling and presents a novel unified labeling model that supports a wide range of aspect terms. Unlike a conventional tagging scheme that predicts the boundary of an aspect term and classifies its sentiment step by step, our proposed model deals with two tasks simultaneously through one set of labels. Sentiment polarities are labeled directly on aspect term tokens, thus combining the boundary information with sentiment polarity in this unified tagging scheme. In this paper, we take Bidirectional Encoder Representations from Transformer (BERT) as the first representation layer to capture contextual features of the entire sentence. Conditional Random Field (CRF) follows BERT for minimizing empirical risk and labeling each token representation within given label sets based on the learned transition matrix. In our experiments, the proposed method demonstrates superior performance against multiple baselines on three benchmark datasets and one Twitter datasets collected by ourselves containing open-domain sentences and aspect terms with various categories, lengths and quantities.
Pattern matching by the terms of cache memory limitations

A few known techniques of exact pattern matching, such as 2-byte read, skip loop, and sliding search windows, are improved and applied to pattern matching algorithms, performing over 256-ary alphabets. Instead of 2-byte read, we offer “1.5-byte read”, i.e. reading more than 8 but less than 16 bits of two sequential bytes of a text at each iteration of a search loop. This allows us to fit the search table into L1 cache memory, which significantly improves the algorithm performance. Also, we introduce the so-called double skip loop instead of single one, resolve problems caused by endianness of a machine, and adopt the sliding windows technique to our algorithms. The experimental results averaged over 500 runs of algorithms on 40 different computers show that our algorithms outperform all other tested methods for all tested pattern lengths.
Natural Language Processing based Text Summarization and Querying Model

Learning skills play a very important role to build a student’s foundation for a language. It becomes difficult for students to analyze the important information available in an extensively lengthy passage. Our proposed system focuses on helping students in developing their skills by understanding passages in their textbooks through text summarization and question-answer module. The main idea is to save a potential amount of time and effort of readers in finding valuable information in a given document. Also, the question and answer extraction help one to understand a passage in much depth. Our model primarily focuses on History Textbooks passages of Secondary School Certificate (SSC) Board Maharashtra State that consists of facts and figures by combining the Text Summarization Module with Question Answer Module. To achieve this task of text summarization we used the Term Frequency Inverse Document Frequency (TF-IDF) algorithm and question answers are generated based on the generated summary with the help of Stanford CoreNLP suite.
Regression-Based Face Pose Estimation with Deep Multi-modal Feature Loss

Image-based face pose estimation tries to estimate the facial direction with 2D images. It provides important information for many face recognition applications. However, it is a difficult task due to complex conditions and appearances. Deep learning method used in this field has the disadvantage of ignoring the natural structures of human faces. To solve this problem, a framework is proposed in this paper to estimate face poses with regression, which is based on deep learning and multi-modal feature loss ( $$M^2FL$$ ). Different from current loss functions using only a single type of features, the descriptive power was improved by combining multiple image features. To achieve it, hypergraph-based manifold regularization was applied. In this way, the loss of face pose estimation was reduced. Experimental results on commonly-used benchmark datasets demonstrate the performance of $$M^2FL$$ .
Imbalanced Classification Problem Using Data-driven and Random Forest Method

Classification problem is a major concern in numerous domains, for instance, customer retaining problem on electronic devices, rare disease diagnosis, bank fraud identification etc. Accurate and efficient classification approaches can reduce operation cost of companies and thus save a mass of artificial work significantly. Identifying the potential targets and then put promotion strategy to customers in order to achieve considerable improvement has become a critical objective in a range of institutions. Nevertheless, missing values and some statistical errors are always involved in the majority of datasets. Besides, the available datasets usually tend to be imbalanced, which leads to weak performance of the classification model. Though random forest method is widely adopted in prediction problem, few of them introduce data-driven technique in this problem. In th is work, firstly, a data-driven method is applied to generate new features based on the imbalanced datasets. Then, the prediction is executed by Random Forest(RF) algorithm on three open-sourced datasets. Results reveal that random forest combined with data-driven method improved the prediction accuracy. The AUC values also perform well stable and even increased. Our main contributions are: We consider two-class classification problem and employ a data-driven method to generate new features, which can raise the correlation of the features and increase the accuracy without losing original information meanwhile. 2. We use random forest coupled with a data-driven method, and consider imbalanced condition in our test which are more reasonable in real world applications, which can provide a general idea and direction in the industry.
CyberWolf

The volume of transactions in financial markets is impressive: e.g., every four days an amount similar to the USA GDP flows through the Forex (foreign exchange, which is only one of the several financial markets). Currently, financial markets are ICT-driven, i.e., about 60% of transactions are automatically launched by computers and the annual impact of such transaction is greater than 30 times the world GDP. The growing complexity of financial instruments has led to an increase in the underlying technology, and consequently it opened the door to vulnerabilities in the security and control systems.
Optimal Dimensional Synthesis of Rhombus Path Generating Adjustable Four-Bar Mechanism

Precise and continuous generation of rhombus path by adjustable four-bar mechanism is investigated and confirmed using Reconstructed Adjustable Parameter Curve (RAPC) technique. RAPC method is able to extract adjustable parameter value of intermediary path points along the path generation profile corresponding to uniformly incremented crank angle values. Harmonic Spacing (HS) algorithm is used to improve the precision points spacing along the perimeter of intended rhombus path. Optimal dimensional synthesis of adjustable four-bar mechanism makes use of these precision points. Simulation of motion of adjustable four-bar mechanism to generate profile path of rhombus is carried out to verify the dimensional synthesis of the linkage. Optimization combining Genetic Algorithm (GA) and Pattern Search (PS) is implemented to synthesize rhombus path generating mechanism. Results show the achievement of precise and continuous path generation with high accuracy for the selected rhombus path profile.
Evolution of SSL/TLS Indicators and Warnings in Web Browsers

The creation of the World Wide Web (WWW) in the early 1990’s finally made the Internet accessible to a wider part of the population. With this increase in users, security became more important. To address confidentiality and integrity requirements on the web, Netscape—by then a major web browser vendor—presented the Secure Socket Layer (SSL), later versions of which were renamed to Transport Layer Security (TLS). In turn, this necessitated the introduction of both security indicators in browsers to inform users about the TLS connection state and also of warnings to inform users about potential errors in the TLS connection to a website. Looking at the evolution of indicators and warnings, we find that the qualitative data on security indicators and warnings, i.e., screen shots of different browsers over time is inconsistent. Hence, in this paper we outline our methodology for collecting a comprehensive data set of web browser security indicators and warnings, which will enable researchers to better understand how security indicators and TLS warnings in web browsers evolved over time.
SRIM and SCRIM factors of xn + 1 over finite fields and their applications

Self-Reciprocal Irreducible Monic (SRIM) and Self-Conjugate-Reciprocal Irreducible Monic (SCRIM) factors of [Formula: see text] over finite fields have become of interest due to their rich algebraic structures and wide applications. In this paper, these notions are extended to factors of [Formula: see text] over finite fields. Characterization and enumeration of SRIM and SCRIM factors of [Formula: see text] over finite fields are established. Simplification and recessive formulas for the number of such factors are given. Finally, applications in the study of complementary negacyclic codes are discussed.
An Approach to Extract Fine Detail and Unclear Information by Enhancing Computed Tomography Image

Medical imaging is dealt with the variety of images of cross-section, tissues, blood vessels, vein, etc. of the human body that is derived by using the X-rays, MRI, CT scan, Ultrasound, Endoscopy etc. The images generated by Computed Tomography (CT Scan) of cross-sections of body are used for the diagnosis purpose by medical and healthcare experts. In order to follow an appropriate diagnosis process, the images produced by CT scan must be clearly interpretable by the medical experts. However, the images produced by CT scan are mostly suffered with the issues of lower contrast, noise, blurred, distortion, and unsharpness. So, there is a strong demand to solve all these issues for applying the diagnosis process more accurately. The main objective of this paper is to enhance the perceptual quality of images generated by CT scan. Various techniques have been used to achieve the objective, but the main technique used in proposed approach is to apply Laplacian filter for positive slope edges as well as negative slope edges along with CLAHE and Gaussian filtering. Laplacian approach has been used by various experts, but in this proposed work a Laplacian approach has been also applied on the negative slope edges. Thus both positive-slope edges and negative-slope are equally highlighted. As a result this approach assist medical expert to derive more detailed information from the image of Computed Tomography.
Memory-aware Online Compression of CAN Bus Data for Future Vehicular Systems

Vehicles generate a large amount of data from their internal sensors.This data is not only useful for a vehicle's proper operation, but it provides car manufacturers with the ability to optimize the performance of individual vehicles and companies with fleets of vehicles (e.g., trucks, taxis, tractors) to optimize their operations to reduce fuel costs and plan repairs.This paper proposes algorithms to compress CAN bus data, specifically, packaged as MDF4 files.In particular, we propose lightweight, online and configurable compression algorithms that allow limited devices to choose the amount of RAM and flash memory allocated to them.We show that our proposals can outperform LZW for the same RAM footprint, and can even deliver comparable or better performance to DEFLATE under the same RAM limitations.
Deep Neural Semantic Network for Keywords Extraction on Short Text

Keyword extraction is a branch of natural language processing, which plays an important role in many tasks, such as long text classification, automatic summary, machine translation, dialogue system, etc. All of them need to use high-quality keywords as a starting point. In this paper, we propose a deep learning network called deep neural semantic network (DNSN) to solve the problem of short text keyword extraction. It can map short text and words to the same semantic space, get the semantic vector of them at the same time, and then compute the similarity between short text and words to extract top-ranked words as keywords. The Bidirectional Encoder Representations from Transformers was first used to obtain the initial semantic feature vectors of short text and words, and then feed the initial semantic feature vectors to the residual network so as to obtain the final semantic vectors of short text and words at the same vector space. Finally, the keywords were extracted by calculating the similarity between short text and words. Compared with existed baseline models including Frequency, Term Frequency Inverse Document Frequency (TF-IDF) and Text-Rank, the model proposed is superior to the baseline models in Precision, Recall, and F-score on the same batch of test dataset. In addition, the precision, recall, and F-score are 6.79%, 5.67%, and 11.08% higher than the baseline model in the best case, respectively.
Evidence Identification and Acquisition Based on Network Link in an Internet of Things Environment

In an Internet of Things (IoT) environment, IoT devices are typically connected through different network media types such as mobile, WiFi and wired networks. Due to the pervasive nature of such devices, they are a potential evidence source in both civil litigation and criminal investigations. It is, however, challenging to identify and acquire forensic artifacts from the broad range of devices, which have varying storage and communication capabilities. We posit the importance of focusing on the hidden links between different IoT devices (e.g. whether one device is controlled or can be accessed from another device in the system), and design an approach to do so. Specifically, our approach adopts a graph to model the message flows of IoT communications, with the aim of facilitating the identification of correlated network traffic, based on the direction of the network and the associated attributes. To demonstrate how such an approach can be deployed in practice, we evaluate our approach using IoT devices in a smart home environment and achieve an accuracy rate of 98.3% for detecting hidden links between devices.