Reviewerly/exHarmony huggingface dataset:
A Novel Data Hiding Technique with High Imperceptibility Using a 3-Input Majority Function and an Optimal Pixel Adjustment

Maximizing the payload capacity without losing the visual quality of the original image is a challenging problem in stegnography. This paper puts forth a data hiding technique with high imperceptibility and utilize the scope of a 3-input majority (MAJ3) function. Generally, a majority function takes an odd number of inputs and returns the majority value as its output. If the output of the majority function and the secret bits we want to hide are different, then we have defined an optimal pixel adjustment so that the receiver can extract the obscure data back. Empirical results unfold that this recommended approach increased the embedding capacity without comprising the visual quality. Complexity of the proposed algorithm is less compared to other image-based data hiding algorithms.
Analysis and Visualization of Performance Indicators in University Admission Tests

This paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the question or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the composition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters.
Comparative Analysis of Image Segmentation Techniques

Image segmentation is a prominent task done in computer vision. Image thresholding is one such technique in image segmentation. Thresholding is a method of categorizing image intensities into two classes and on the basis of that yielding an image which is a binary image, and ideally also has all the fine details of region of interest which an image should have for analysis. Image thresholding is widely used as it reduces the computational cost of processing the image and makes processing feasible in real-world applications like medical imaging, object detection, recognition task, character recognition, etc. This paper dwells into the depth of thresholding techniques to know which technique can perform better on all kinds of images so as to extract region of interest. We found out that not every technique is good for all cases, Otsu’s global thresholding is a promising and faster way to segment and generate a binary image, but works well with images having negligible noise and region of interest already being very much clear in the original image, whereas applying methods like Otsu’s thresholding on sliced blocks of images and then merging them or applying moving averages (sliding windows) on images having noise which is distributed in a specific region of image, moving averages gave result better on images which have distributed gradient noise. Whereas the hybrid technique used are a combination of global and local thresholding.
<i>Make us difficult</i>

Abstract This paper introduces a previously undiscussed English construction, termed the make us difficult construction. Examples of the construction are presented, from the International Corpus of English and the Corpus of Global Web-Based English , and a quantitative analysis is conducted of the construction’s text frequency and variant rates. Quantitative data on specific usage patterns is employed to inform an analysis of the construction and its productivity, and recommendations are made for future research.
Indoor Scene Recognition with Convolution Supervision and Fusion Deep Network

In order to increase the recognition accuracy of indoor scenes, a network called Convolution Supervision and Fusion Deep Network (CSFDN) based on AlexNet is introduced. Convolutional layers with large convolution kernels are replaced by cascaded ones with smaller kernels and stride. Features of a middle convolutional layer are integrated into that of the high layer to add local information through a fusion branch. A supervision branch is added to the middle convolutional layer to shorten the distance of back propagation and prevent gradient disappearance when training the network. The experimental results on three different datasets indicate that the CSFDN has a higher recognition accuracy than the AlexNet and good generality in indoor scene recognition, which proves the effectiveness of the improved architecture designs.
Smart Systems’ Scales for Measuring

In this chapter the following basic questions are considered: complex systems’ problems of measuring,generalizations of classical scales. Multidimensional, matrix and lattice scales, examples.coding as a tool to measure students’ level of knowledge,quasi-fractals and synergistic effects. Scales fixing thesynergistic effect and the time (level) series determinedby them,measurement scales for quasi-fractal algebraic systems.quasi-fractal scale measurability level.
A Learning-Based Approach for Evaluating the Capacity of Data Processing Pipelines

Data processing pipelines are made of various software components with complex interactions and a large number of configuration settings. Identifying when a pipeline has reached its maximum performance capacity is generally a non-trivial task. Metrics exported at the software and at the hardware levels can provide insightful information about the current state of the system, but it can be difficult to interpret the value of a metric, or even to know which metrics to focus on. Considering a popular pipeline composed of Kafka, Spark Streaming, and Cassandra, this paper proposes a learning-based approach to automatically infer the state of such a pipeline solely by analyzing metrics. Our results show that we are able to achieve a high prediction accuracy when predicting on new configurations and when the number of data sources changes. Furthermore, our analysis demonstrates that the best prediction results are obtained when metrics of different types are combined.
Effects of differential privacy techniques: Considerations for end users

We study the effects of differentially private (DP) noise injection techniques in a survey data setting, using the release of cost of early care and education estimates from the National Survey of Early Care and Education as a motivating example. As an example of how DP noise injection affects statistical estimates, our analysis compares the relative performance of DP techniques in the context of releasing estimates of means, medians, and regression coefficients. The results show that for many statistics, basic DP techniques show good performance provided that the privacy budget does not need to be split over too many estimates. Throughout, we show that small decisions, such as the number of bins in a histogram or the scaling of a variable in a regression equation, can have sometimes dramatic effects on the end results. Because of this, it is important to develop DP techniques with an eye towards the most important aspects of the data for end users.
The Study of Interface Design for Museum Mobile Learning from the Relationship Between Visitor’s Behavior Needs and Information Transmission

The study is focus on the analysis of interface design for museum mobile learning between the visitor’s behavior needs and information transmission. When we design learning environment system within the museum including exhibitions, objects, tour guide, education activity etc., the whole museum design should be considered about the user-friendly learning environment as a whole system to reduce visitor stress within the museum and to help visitor avoid museum fatigue. So, it’s important to museum for how to avoid the visitor’s cognition differences between the visitor’s behavior needs and information transmission. This study would try to propose the principle of interface design for the museum mobile learning system.
A recognition algorithm of VGPO jamming based on discrete chirp-Fourier transform

Abstract This paper addresses the recognition problem of velocity gate pull-off (VGPO) jamming from the target echo signal for the velocity-based tracking system. The discrete chirp-Fourier transform (DCFT) is studied in this paper to jointly estimate the chirp rates and frequencies of the target and jamming signals. Firstly, the scaling characteristic of the DCFT algorithm is explored. Then, we focus on the quantitative effect of the VGPO jamming signal by analyzing the jointly estimated results in each pulse. The quantitative effect indicates that, as long as the estimated frequency is unchanged, the relationship between the estimated chirp rate and the pulse numbers is similar to the relationship between the frequency offset of VGPO jamming and the time. Finally, by utilizing the joint estimated results in each pulse repetition interval and calculating the mean square to variance ratio (MSVR) of the normalized estimated chirp rate, the VGPO jamming can be recognized. Simulation results show that the jamming signal and the target echo become distinguishable with the proposed feature. Comparing to the existing works, the proposed method can correctly recognize the jamming signal with lower jamming-to-noise ratio (JNR) 5 dB with less data needed, which means it can work effectively in the early stage of interference implementation and shows great potential in practical applications.
HTBN: A Heterogeneous Network for Breast Ultrasound Image Classification

Ultrasound (US) is one of a primary imageological examination and preoperative assessment for brleast nodules. However, in the field of ultrasound diagnosis, it relies heavily on the experience of physicians due to the overlapping image expression of benign and malignant breast nodules. The diagnostic accuracy of physicians with different qualifications differs by up to 30%. Therefore, it is easy to lead to misdiagnosis and increase the needless rate of puncture biopsy. On the other hand, the current computer-assisted breast ultrasound diagnosis requires lots of human interactions while the accuracy is not reliable enough. In this paper, an end-to-end model is proposed for automatically nodule classification. We presents a heterogeneous three-branch network (HTBN) for benign and malignant classification of the breast ultrasound images. In HTBN, the image information including ultrasound images, contrast-enhanced ultrasound (CEUS) images and non-image information including patient's age and other six pathological features are used simultaneously. In order to validate our method, a breast ultrasound data set with 1303 cases is collected. On this data set, the average diagnosis accuracy of physicians with five-year qualifications is 85.3%. However, the classification accuracy of our method is 92.41%. Through experiments, we confirmed our point of view that by incorporating medical knowledge into the optimization process, adding contrast-enhanced ultrasound images and non-image information to the network, the accuracy and robustness of breast diagnosis are greatly improved.
Online Credit Card Fraud Analytics Using Machine Learning Techniques

Every year, companies and financial institutions worldwide lose billions due to credit card fraud. With the advancement in electronic commerce and communication technology and the development of modern technology, there has been an increase in the usage of credit cards and also the risks associated with it. Financial fraud detection systems are being implemented everywhere to minimize these losses. In the real world, fraudulent and real transactions are scattered all around and it is extremely difficult to distinguish between them. There is a class imbalance problem due to the minority of fraudulent transactions. The enormity of imbalanced data poses a challenge on how well Machine Learning techniques can efficiently detect fraud with a high prediction accuracy, and at the same time reduce misclassification costs. In this paper, experiments are performed to compare the most commonly used ML algorithms and investigate their performance when applied to a massive and highly imbalanced dataset. We compare six Supervised Machine learning techniques, namely, Logistic Regression, K-Nearest Neighbours, Naive Bayes, Decision Trees, Random Forest and Linear Support Vector Machine (SVM).
Rule Inference Network for Classification

To alleviate the low interpretability of algorithms in neural networks, we propose a rule inference network based on rule-based system using the evidential reasoning approach (RIMER), which is interpretable by the rules in belief rule base (BRB). Considering the influence of data distribution on attribute weights, we optimize attribute weights based on the Sigmoid activation function to ensure that the normalization process adapts the overall data distribution. A rule inference network for classification is constructed including the framework and the learning algorithm. A comparison with the other algorithms demonstrates that the proposed rule inference network for classification has advantages in interpretability and learning capability.
Topological Graph Representation Learning on Property Graph

Property graph representation learning is using the property features from the graph to build the embeddings over the nodes and edges. There are many graph application tasks are using the property graph representation learning as part of the process. However, existing methods on Property graph representation learning ignore either the property features or the global topological structure information. We propose the TPGL, which utilizes the topological data analysis with a bias property graph representation learning strategy. The topological data analysis could augment the global topological information to the embedding and significantly improve the embedding performance on node classification experiments. Moreover, the designed bias strategy aggregated the property features into node embedding by using GNN. Particularly, the proposed TPGL outperformed the start of the art methods including PGE in node classification tasks on public datasets.
The influence of imprecise quantum measurement on quantum dense coding

Abstract In this paper, the influence of imprecise quantum measurement on quantum dense coding would be analysed. The realization processes of dense coding under the condition of imprecise measurement are given in detail. The performance of dense coding is analysed from the successful probability and average classical information. The successful probability and the classical information are immune to the phase parameter error of quantum measurement. However, with the increase of amplitude factor error, both probability and classical information will decrease. The results of this paper would be useful in the field of quantum communication.
Address Usage Estimation Based on Bitcoin Traffic Behavior

This paper studies bitcoin address usage, which is assumed to be hidden via address pseudonyms. Transaction anonymity is ensured by means of bitcoin addresses, leading to abuse for illegitimate purposes, e.g., payments of illegal drugs, ransom, fraud, and money laundering. Although all the transactions are available in the bitcoin system, it is not trivial to determine the usage of addresses. This work aims to estimate typical usages of bitcoin transactions based on transaction features. With the decision tree learning algorithm, the proposed algorithm classifies a set of unknown addresses into seven classes; provider addresses of three services for mining pool, Bitcoin ATM, and dark websites; and user addresses of four services for mining Bitcoin ATM, dark websites, exchange, and a bulletin board system. The experimental results reveal some useful characteristics of bitcoin traffic, including statistics of frequency, amount of value, and significant transaction features.
Nonparametric Statistical Anomaly Detection Approach for ATMS DDoS Attack

Distributed Denial of Service (DDoS) attack is a standout amongst the most prominent attacks types going for the accessibility of framework. We consider the convenient identification and alleviation of DDoS attacks in Automated Traffic Management Systems (ATMS). Utilizing diverse attack traffic designs, it is conceivable to watch the conduct of the algorithm under investigation. The principle objective of this paper is to break down the recursive nonparametric CUSUM, since it is new to the information organize network and it guarantees to have a great deal of future applications in the region. A novel system for recognizing and relieving low-rate DDoS attacks in ITS dependent on nonparametric statistical anomaly/hybrid detection is proposed. The outcome will demonstrate that our proposed technique significantly beats two parametric strategies for opportune identification dependent on the Cumulative Sum (CUSUM) test, just as the conventional information filtering approach as far as normal recognition delay and false alert rate.
Pairwise-Based Hierarchical Gating Networks for Sequential Recommendation

The sequential pattern behind users' behaviors indicates the importance of exploring the transition relationships among adjacent items in next-item recommendation task. Most existing methods based on Markov Chains or deep learning architecture have demonstrated their superiority in sequential recommendation scenario, but they have not been well-studied at a range of problems: First, the influence strength of items that the user just access might be different since not all items are equally important for modeling user's preferences. Second, the user might assign various interests to certain parts of items, as what often attracts users is a specific feature or aspect of an item. Third, many methods ignore the complex item relations in user's previous actions. In this paper, we present a novel recommendation approach with gating mechanism and encoding module to address above problems. Specifically, the pair-wise encoding layer is first introduced to build 3-way tensor for modeling the relationships among items in user interact histories. We also apply two gating layers to filter useful information and capture user's short-term preference from aspect-level and item-level. We also follow the similar sprits to model user's long-term preference by integrating user latent embeddings. Empirical results on three public datasets show that our method achieves effective improvements over the state-of-the-art sequence-based models.
Reliability Modeling and Analysis of Command and Control Network Based on Dynamic Bayes-GO Method

A The combat command and control system is the nerve center to carry out joint operations. It is an important part of military operations in the information age and plays the role of "amplification multiplier" in the enemy operations. In this paper, a combat command network system was taken as the research object, and the reliability modeling and analysis of the combat command network were conducted based on dynamic Bayes-GO method. Firstly, the mapping rules for transforming common operators, type 18A and type 20 operators in GO method into dynamic Bayesian networks are given. Then, the task of the brigade command vehicle 2 commanding the combat vehicle 5 to strike the target in a combat command network was modeled and analyzed. Finally, the dynamic availability curve of the system within t = 300h and the availability corresponding to the system at each moment are calculated, which can enable the combat commander to clearly judge the reliability of the system in successfully hitting the target at each moment, thus the other combat equipment can be better dispatched for cooperative operations.
Research on Image-based Tracking Method for Six Rotor Aircraft

In view of the low accuracy of Hough transform in road edge detection, this paper presents a research method of MATLAB splicing technology and fuzzy control algorithm based on Hough transform. On the premise of guaranteeing the original photographic image not to be distorted, the method splices several groups of experimental photos through MATLAB, and then extracts the road edge lines by Hough transform. In addition, the fuzzy control algorithm is used to control the flight direction of the rotorcraft and the final tracking trajectory is drawn when the obstacle is extracted. Experiments show that compared with the traditional Hough transform, the proposed method can increase the proportion of the target road in the image in complex environment by mosaic technology. This method has the advantages of strong anti-jamming ability and high running efficiency, and can control the flight direction of the rotorcraft by fuzzy control algorithm when the target road cannot be accurately identified, thus effectively improving the accuracy of target road detection and planning the tracking route of the rotorcraft.
Temporal Action Detection Based on Temporal Convolutional Network with Coarse-Grained Clips Construction

Temporal action detection has gradually become a hot field with the development of video. This task focuses on untrimmed video applications, which is close to practical applications. To improve the accuracy of temporal action detection, this paper proposes a novel framework based on temporal convolutional network with coarse-grained clips construction and our framework mainly includes three parts as follows: feature extraction, coarse-grained clips construction and improved boundary regression model. In particular, to make our model more stable without reducing the sensitivity to boundary, this paper proposes a novel coarse-grained clips construction to gain coarse-grained clips. Furthermore, we refine action boundary with improved boundary regression model based on temporal convolutional network and design three sub-module structures to preserve temporal information. The results on THUMOS14 illustrate that our framework could provide high quality of action propose to improve the detection accuracy.
Drift Adaptation via Joint Distribution Alignment

Machine learning in evolving environment faces challenges due to concept drift. Most concept drift adaptation methods focus on modifying the model. In this paper, a method, Drift Adaptation via Joint Distribution Alignment (DAJDA), is proposed. DAJDA performs a linear transformation to the drift instances instead of modifying model. Instances are transformed into a common feature space, reducing the discrepancy of distributions before and after drift. Experimental studies show that DAJDA has abilities to improve the performance of learning model under concept drift.