Reviewerly/exHarmony huggingface dataset:
Hybrid AES-Huffman Coding for Secure Lossless Transmission

Encoding is one of the effective ways to secure files, both for storing and sending files through the public network. Advanced Encryption Standard (AES) is one of the most powerful encryption algorithms that are widely used in various researches and implemented in hardware and software. But this algorithm has negative side effects, which can increase file size. The lossless Huffman compression algorithm can be used to overcome these side effects. But after reviewing the relevant research, the effect of compression on encrypted files is not discussed in detail. This research proposes a combination of AES-Huffman to produce lossless compressed encryption files. Where encryption is done first, then the compression process is carried out. To find out the quality of file security, measurements were made using the avalanche effect (AE) and entropy after encryption and compression. Based on the test results it is proven true that AES encryption has side effects in increasing file size around 25% of the original file size. But after Huffman compression the encrypted file code shrank by around 30%, meaning that the file size after being compressed was smaller than the original file size. However, from the aspect of security, the Huffman file gives a positive effect, where there is a significant increase in the value of AE and entropy.
Scenario-based creation and digital investigation of ethereum ERC20 tokens

This paper examines the Ethereum network in the context of an investigation. The validation of data sources is achieved through different client software on both the Ropsten network and the live blockchain. New scenarios are also used test common patterns in order to track for start and end points for Ethereum and ERC20 tokens.
Preserving ON-OFF Privacy for Past and Future Requests

We study the ON-OFF privacy problem. At each time, the user is interested in the latest message of one of N sources. Moreover, the user is assumed to be incentivized to turn privacy ON or OFF whether he/she needs it or not. When privacy is ON, the user wants to keep private which source he/she is interested in. The challenge here is that the user's behavior is correlated over time. Therefore, the user cannot simply ignore privacy when privacy is OFF, because this may leak information about his/her behavior when privacy was ON due to correlation. We model the user's requests by a Markov chain. The goal is to design ON-OFF privacy schemes with optimal download rate that ensure privacy for past and future requests. The user is assumed to know future requests within a window of positive size ω and uses it to construct privacy-preserving queries. In this paper, we construct ON-OFF privacy schemes for N=2 sources and prove their optimality.
Using Cluster Analysis to Assess the Impact of Dataset Heterogeneity on Deep Convolutional Network Accuracy: A First Glance

In this paper we performed cluster analysis using Fuzzy K-means over the image-based features of two models, to assess how dataset heterogeneity impacts model accuracy. A highly heterogeneous dataset is linked with sparse data samples, which usually impacts the overall model generalization and accuracy with test samples. We propose to measure the Coefficient of Variation (CV) in the resulting clusters, to estimate data heterogeneity as a metric for predicting model generalization and test accuracy. We show that highly heterogeneous datasets are common when the number of samples are not enough, thus yielding a high CV. In our experiments with two different models and datasets, higher CV values decreased model test accuracy considerably. We tested ResNet 18, to solve binary classification of x-ray teeth scans, and VGG16, to solve age regression from hand x-ray scans. Results obtained suggest that cluster analysis can be used to identify heterogeneity influence on CNN model testing accuracy. According to our experiments, we consider that a CV $$< 5\%$$ is recommended to yield a satisfactory model test accuracy.
A Method for Digital Modulation Recognition Based on Mixed Signal Features

Digital modulation recognition is an important part of signal processing. With inherent anti-noise performance, High-Order Cumulants have natural advantage in recognizing digital modulation pattern, and is widely studied in the past several decades. On the basis of high-order cumulants, combined with peak feature of FFT spectrum and instantaneous signal feature, this paper proposed a new method for digital modulation recognition based on mixed signal features, which took advantage of classical decision-tree classifier. Simulation results showed that the method proposed in this paper can recognize six classical digital modulation pattern efficiently, and achieved satisfactory recognition result even at rather low SNR.
Detecting Default Payment Fraud in Credit Cards

A credit card account is said to be in default if the payment into the credit card account is not done in due time because of credit card fraud. In many cases the default account credit card holders are found to be fraudsters. It is necessary for financial Institutions to determine which accounts may go into default so that they can take necessary preventive steps to curb any fraudulent transactions. The transactions of the clients can be observed and using this information if any account resembles a fraud, the financial institutions can take necessary actions like blocking the account. We have considered some standard machine learning algorithms for the classification of the default accounts. Later, we have proposed a model by combining the classification algorithms and critically examined it with other machine learning and deep learning models.
TCD: Statically Detecting Type Confusion Errors in C++ Programs

For performance reasons, C++, albeit unsafe, is often the programming language of choice for developing software infrastructures. A serious type of security vulnerability in C++ programs is type confusion, which may lead to program crashes and control flow hijack attacks. While existing mitigation solutions almost exclusively rely on dynamic analysis techniques, which suffer from low code coverage and high overhead, static analysis has rarely been investigated. This paper presents TCD, a static type confusion detector built on top of a precise demand-driven field-, context-and flow-sensitive pointer analysis. Unlike existing pointer analyses, TCD is type-aware as it not only preserves the type information in the pointed-to objects but also handles complex language features of C++ such as multiple inheritance and placement new, making it therefore possible to reason about type casting in C++ programs. We have implemented TCD in LLVM and evaluated it using seven C++ applications (totaling 526,385 lines of C++ code) from Qt, a widely-adopted C++ toolkit for creating GUIs and cross-platform software. TCD has found five type confusion bugs, including one reported previously in prior work and four new ones, in under 7.3 hours, with a low false positive rate of 28.2%.
Hybrid classification algorithms based on instance filtering

Basic classification algorithms induce a single model from training data. The interpretation of a model is relatively easy, while basic algorithms have limitations in achieving high accuracy. An instance misclassified by a model may be correctly predicted by another. Hybrid classification is a concept that employs basic classification algorithms for model induction and for data preprocessing. Misclassification instances are usually considered to be noise, yet those still may carry useful information for identifying the class values of some other instances. This study proposes hybrid classification algorithms in which training instances are filtered to build three models for prediction. Each testing instance is classified by exactly one of them. The algorithms involved in the proposed hybrid classification algorithms are decision tree induction and naïve Bayesian classifier. The testing results on twenty data sets demonstrate that our hybrid classification algorithms can significantly outperform the basic ones as well as the hybrid algorithm proposed in a previous study. The hybrid classification algorithms based on instance filtering achieve relatively high accuracy and maintain the easy interpretation of learning results.
A Physical Embedding Model for Knowledge Graphs

Knowledge graph embedding methods learn continuous vector representations for entities in knowledge graphs and have been used successfully in a large number of applications. We present a novel and scalable paradigm for the computation of knowledge graph embeddings, which we dub Pyke. Our approach combines a physical model based on Hooke’s law and its inverse with ideas from simulated annealing to compute embeddings for knowledge graphs efficiently. We prove that Pyke achieves a linear space complexity. While the time complexity for the initialization of our approach is quadratic, the time complexity of each of its iterations is linear in the size of the input knowledge graph. Hence, Pyke’s overall runtime is close to linear. Consequently, our approach easily scales up to knowledge graphs containing millions of triples. We evaluate our approach against six state-of-the-art embedding approaches on the DrugBank and DBpedia datasets in two series of experiments. The first series shows that the cluster purity achieved by Pyke is up to 26% (absolute) better than that of the state of art. In addition, Pyke is more than 22 times faster than existing embedding solutions in the best case. The results of our second series of experiments show that Pyke is up to 23% (absolute) better than the state of art on the task of type prediction while maintaining its superior scalability. Our implementation and results are open-source and are available at http://github.com/dice-group/PYKE .
A Novel Data Security Framework in Distributed Cloud Computing

Now a day's everyone is using network to transmit the data. Even though the security has been increased the data is not still protected. So before sending the data through the network it has to encrypt so that data will be secured. In this paper to secure the data the combination of encryption algorithms is used that is known as hybrid encryption algorithm. It is the combination of symmetric (AES) and asymmetric (RSA) encryption algorithms. This hybrid encryption algorithm is to expand the security level to the data. For the encryption of sensitive data, both symmetric and asymmetric encryption techniques are important.
Convolutional neural network for human behavior recognition based on smart bracelet

In this paper, we designed a smart bracelet to realize human behavior recognition, remote monitoring, fall detection, alarm and other functions. We use a six-axis inertial measurement module to collect a large amount of human body posture data. The Kalman filter is used to preprocess the data of ac celeration and gyroscope, and then the processed series data is segmented by sliding window method. At last these data are put into a convolutional neural network(CNN) to identify human activities. Furthermore, we designed a two-level fall behavior judgment, the first-level threshold determination is performed through smart bracelet, when a suspected fall behavior occurred, the sequence data are uploaded to the cloud platform, the second CNN discriminant model is triggered to accurately determine whether a fall behavior occurs.
Ecommerce Fraud Detection Through Fraud Islands and Multi-layer Machine Learning Model

Main challenge for e-commerce transaction fraud prevention is that fraud patterns are rather dynamic and diverse. This paper introduces two innovative methods, fraud islands (link analysis) and multi-layer machine learning model, which can effectively tackle the challenge of detecting diverse fraud patterns. Fraud Islands are formed using link analysis to investigate the relationships between different fraudulent entities and to uncover the hidden complex fraud patterns through the formed network. Multi-layer model is used to deal with the largely diverse nature of fraud patterns. Currently, the fraud labels are determined through different channels which are banks' declination decision, manual review agents' rejection decisions, banks' fraud alert and customers' chargeback requests. It can be reasonably assumed that different fraud patterns could be caught though different fraud risk prevention forces (i.e. bank, manual review team and fraud machine learning model). The experiments showed that by integrating few different machine learning models which were trained using different types of fraud labels, the accuracy of fraud decisions can be significantly improved.
Classification of Book Genres using Book Cover and Title

The main purpose of this research was to determine if the cover and title of a book can be used to predict its genre. The main motivation behind this was that since the cover or title individually may not always be suggestive of the type of book, using both of them together may be more indicative of the genre of the book. The idea was to extract features from the cover and title and combine them to predict the genre of a book. A logistic regression model was used for training and prediction. An accuracy of 87.2% was obtained when we used the combined features. We got an accuracy of 67.04% using only cover features and an accuracy of 86.2% using only the title features. This shows that although using both the title and cover features gives better results, the title seems to more expressive of the genre of the book.
Deep Learning from 3DLBP Descriptors for Depth Image Based Face Recognition

In this paper, we propose a new framework for face recognition from depth images, which is both effective and efficient. It consists of two main stages: First, a handcrafted low-level feature extractor is applied to the raw depth data of the face, thus extracting the corresponding descriptor images (DIs); Then, a not-so-deep (shallow) convolutional neural network (SCNN) has been designed that learns from the DIs. This architecture showed two main advantages over the direct application of deep-CNN (DCNN) to the depth images of the face: On the one hand, the DIs are capable of enriching the raw depth data, emphasizing relevant traits of the face, while reducing their acquisition noise. This resulted decisive in improving the learning capability of the network; On the other, the DIs capture low-level features of the face, thus playing the role for the SCNN as the first layers do in a DCNN architecture. In this way, the SCNN we have designed has much less layers and can be trained more easily and faster. Extensive experiments on low- and high-resolution depth face datasets confirmed us the above advantages, showing results that are comparable or superior to the state-of-the-art, using by far less training data, time, and memory occupancy of the network.
Fast Scale Estimation Method in Object Tracking

Accurate and fast scale estimation of targets is a challenging research problem in visual object tracking.Most trackers employ an exhaustive scale search to estimate the target size.The exhaustive search strategy is computationally expensive and it struggles when encountered with large scale variations.In this paper, a scale adaptive method is proposed, which not only improves the tracking performance, but also greatly reduces the computational costs and improves the tracking speed.Based on the scale estimation method of SAMF, the original 7 fixed scale sizes were reduced to 3, and an adaptive scale size was added.Three fixed scales were used to determine the direction of scale change, and the APCE change rate of the current frame and the previous frame was used to control another adaptive scale size.Finally, the optimal scale estimation was determined.Additionally, we investigate the update strategy to further improve the tracking accuracy.Extensive experiments on OTB50, OTB100 and VOT-ST2019 datasets demonstrate that the proposed method can tackle challenging videos well compared with baseline tracker.On OTB, we obtain a gain of 7.0% in Distance Precision, and 18.8% in Centre Location Error on the selected 43 videos with scale variation attribute, and a mean gain of 6.2% in Precision and 4.6% in Success plots on OTB50, compared with the baseline tracker SAMF.Furthermore, the proposed approach improves the tracking speed by 34% in FPS compared with SAMF.
A Hardware Efficient Elliptic Curve Accelerator for FPGA Based Cryptographic Applications

The work presents an elliptic curve crypto accelerator to implement scalar multiplication on standardized NIST curve over Galois binary fields by using polynomial basis. The proposed design provides efficient hardware utilization and low power consumption. For this purpose, hybrid finite field multiplier is proposed using standard Karatsuba multiplier and shift-and-add multiplication algorithms. The proposed hybrid finite field multiplier can perform one finite field multiplication in m/2 clock cycles, with m being the key-length. Montgomery algorithm with projective coordinates (Lopez-Dahab) is used for the computation of scalar multiplication. An FSM based control unit is developed, governing the overall operations of proposed accelerator. The proposed architecture has been modeled in Verilog-HDL and implemented up to place and route level using the Xilinx Vivado and ISE design software for various FPGA devices. The achieved results demonstrate the convenience of proposed ECC accelerator for resource constrained embedded systems applications requiring low area and low power hardware.
Levy inspired Enhanced Grey Wolf Optimizer

Grey Wolf Algorithm is a recently established efficient optimization algorithm stimulated by the pursuing technique of the grey wolves. In GWO, the next location of the search individuals is modified with respect to the leading wolves. Since in GWO, all the individuals are moving towards the leading wolves and leading wolves often stick to some local solution due to accelerated exploitation. Thus, in this paper, a new modified GWO, named GWO-LF has been introduced to overcome the problem of early convergence. The efficiency of the GWO-LF is validated on 11 standard benchmark functions and outcomes are equated with 4 other recent techniques in respect of average fitness and standard deviation. The experimental outcomes vindicate that the GWO-LF outruns the reviewed techniques on the majority of test functions.
Dirichlet Process Mixture Model Based Nonparametric Bayesian Modeling and Variational Inference

Nonparametric Bayesian modeling receive a great deal of attention in last decades. Dirichlet Process Mixture Model (DPMM) based soft sensing model is developed where the number of parameters can be determined automatically based on the given data. The core task in nonparametric Bayesian modeling is the posterior inference. A novel variational inference algorithm is proposed to determine the posterior distribution. In this variational inference approach, the gradient computation of optimization is derived by Monte Carlo sampling which is not restricted to the specific model expression. To address the challenge of gradient computing, black box Monte Carlo sampling method is also used. To illustrate the effectiveness, the proposed methodology is demonstrated in a practical polypropylene producing process.
3D Object Detection Based on LiDAR Data

Object detection has been a very hot research topic since the advent of artificial intelligence and machine learning. Its importance is very high specifically in advancing autonomous vehicles technology. Many object detection methods have been developed based on different types of data including image, radar, and lidar. Some recent works use point clouds for 3D object detection. One of the recently presented efficient methods is PointPillars, an encoder which learns from data in a point cloud and organizes a representation in vertical columns (pillars) for 3D object detection. in this work, we use PointPillars with lidar data of some urban scenes provided in nuScenes dataset to predict 3D boxes for three different classes of objects (car, pedestrian, bus). We also use nuScenes detection score (NDS) which is a consolidated metric for detection task, to measure and compare different scenarios. Results show that by increasing the number of lidar sweeps, the performance of the 3D object detector improves significantly. We try to increase the performance of the encoder by developing a method to combine different types of input data (lidar, radar, image) based on a weighting system and use it as the input of the encoder.
Turkish Movie Genre Classification from Poster Images using Convolutional Neural Networks

Accessing to the best matching multimedia data is a trending topic due to the enormous amount of demand from people for movies, online TV series, videos etc. Advertising/Introducing form/image of such multimedia applications is important to give the key information to the audience. Sometimes a movie poster may play an important role to present the movie genre correctly. In recent years, Convolutional Neural Networks (CNN) as a deep learning architecture achieved state of-the- art performance in many image processing and recognition applications. In this paper, we implement transfer learning and fine-tuning methods on top of Google Inception-v3 algorithm, which is one of the most popular CNN architectures in this domain, and present comparative results of these methods in classifying the movie genre on a dataset consisting of Turkish movie posters. The obtained results show that fine tuning method performs better than pure CNN and transfer learning models on movie genre classification task.
Smart Contracts-Enabled Simulation for Hyperconnected Logistics

The combination of the Internet of Things and blockchain-based technologies represents a real opportunity for supply chain and logistics protagonists, who need more dynamic, trustworthy and transparent tracking systems in order to improve their efficiency and strengthen customer confidence. In parallel, hyperconnected logistics promise more efficient and sustainable goods handling and delivery. This chapter shows how the Ethereum blockchain and smart contracts can be used to implement a shareable and secured tracking system for hyperconnected logistics. A simulation using the well-known AnyLogic software tool provides insights on the monitoring of properties depicting shipment lifecycle constraints through a stream of blockchain log events processed by BeepBeep 3, an open source stream processing engine.
A Decision-Based Dynamic Ensemble Selection Method for Concept Drift

We propose an online method for concept drift detection based on dynamic classifier ensemble selection. The proposed method generates a pool of ensembles by promoting diversity among classifier members and chooses expert ensembles according to global prequential accuracy values. Unlike current dynamic ensemble selection approaches that use only local knowledge to select the most competent ensemble for each instance, our method focuses on selection taking into account the decision space. Consequently, it is well adapted to the context of drift detection in data stream problems. The results of the experiments show that the proposed method attained the highest detection precision and the lowest number of false alarms, besides competitive classification accuracy rates, in artificial datasets representing different types of drifts. Moreover, it outperformed baselines in different real-problem datasets in terms of classification accuracy.