Reviewerly/exHarmony huggingface dataset:
Hybrid Recommendation Model Fused with Compressed Cross Network

In recent years, with the successful application of Deep Neural Network (DNN) in various fields, researchers have also proposed several DNN-based recommendation models to learn low-level and high-level feature interactions. Although ordinary DNN has powerful learning capabilities, it lacks vector-level interactive operations and increases the time complexity of calculation. In this paper, a new hybrid recommendation model (DeepFM & CIN, DeepFC) is proposed based on the Compressed Interaction Network (CIN) method, whose purpose is to generate interactive features in a display manner at the vector level. The model can not only perform low-order and high-order point-level interactive operations, but also combines high-order vector-level operations. Through simulation experiments on the Criteo and Avazu datasets and comparison with the baseline model, the experimental results show that the model has improved both the logic loss and the AUC value. Finally, the method of structural pruning is used to further reduce the computational time complexity of the model while maintaining good performance.
Detection and Location of Multi-Period Phenomena in Chaotic Binary Sequences

Due to the influence of finite calculation accuracy and binary quantization method, the performance of chaotic binary sequences has been degraded in varying degrees, and some sequences emerge as multi-period phenomena. Aiming at the problem that it is difficult to accurately detect this phenomenon, this paper proposes a multi-period positioning algorithm (MPPA), which can accurately detect and locate the accurate period and local period phenomena contained in chaotic binary sequences. In order to test the effectiveness and correctness of the algorithm, the multi-period characteristics of logistic binary sequences with different calculation accuracy are analyzed. MPPA evaluates the randomness of binary sequences from a new perspective, which provides a new idea for the analysis of cryptographic security of chaotic sequences.
Impossible differential and zero-correlation linear cryptanalysis of Marx, Marx2, Chaskey and Speck32

ARX symmetric ciphers use three operations: Additional, Rotational, and XOR in their structures. The SPECK, MARX, MARX2, and CHASKEY are examples of ARX block ciphers. In this paper, we consider the zero-correlation linear cryptanalysis of MARX, MARX2 and impossible differential cryptanalysis of MARX, MARX2, and CHASKEY.To our knowledge, this cryptanalysis has not been performed on any of the mentioned ciphers so far. Among the available methods for finding the best impossible differential and zero correlation characteristic in the corresponding cryptanalysis, we use Mixed Integer Linear Programming (MILP). The MILP is an automated search method to find the best differential (linear) characteristics of a cipher with a specified number of rounds. Thanks to the MILP method, for the CHASKEY cipher, we find 4-round impossible differential characteristics. For MARX, we present a 13-round attack by using a 10-round impossible differential characteristic. Also, for MARX2, we find 7-round impossible differential characteristics that cause the 11 rounds attack. Moreover, we apply zero-correlation linear cryptanalysis on 10 and 7-round of MARX and MARX2, respectively.
Profiled side channel attacks against the RSA cryptosystem using neural networks

Profiled attacks employing machine learning techniques for secret information extraction are currently one of the main interests of the research community working on side channel attacks. In this work, we tackle the use of machine learning methods to perform a power consumption based side channel attack against asymmetric cryptosystems. In particular, we analyze different machine-learning based strategies to retrieve the secret exponent from a square-and-multiply-always modular exponentiation in RSA cryptosystem software implementations. We validate our proposals conducting effective attacks against the modular exponentiation contained in the BearSSL software library, a timing side-channel hardened public library. The experimental validation shows that our attack strategy improves the state-of-art solution and is able to retrieve the correct exponent value with a single trace from the attacked device, and, in the worst case, with an exhaustive search in a set of candidates containing a few hundred possible values for the sought secret.
Cerebrospinal Fluid Cell Segmentation and Classification in a Class Imbalanced Dataset

Many medical image segmentation tasks usually face the common challenges of limited training samples and class imbalance, which makes the existing SOTA methods such as Mask R-CNN unable to effectively recognize and segment small sample categories. To solve this problem, in this paper, we referred to coarse-to-fine structure and designed a novel instance segmentation network for cerebrospinal fluid cell segmentation, which is composed of a coarse classifier and a dedicated classifier. The former is used to roughly recognize cells and segment them. The latter uses methods such as focal loss to solve the problem of class imbalance and accurately recognizes cells with limited training samples. Experiments on cerebrospinal fluid cell image recognition and segmentation tasks have demonstrated the effectiveness of the proposed network, especially in the small sample category. In addition, this method can be easily extended to other object instance segmentation methods.
Travel Path Planning Algorithm Based on Deep Learning Technology

Tourism resources are limited. In the process of development and utilization, they often cause a great waste. Deep learning technology can effectively integrate tourists with local natural landscape and cultural environment. This method realizes the processing of objective function value output results and calculation parameters by constructing a multi-dimensional data set with real-time and predictive characteristics, which contains rich semantic information, can be combined with other contents, and meet the use requirements of different demand objects at the same time. This paper mainly studies the visual dynamic interactive multi-attribute intelligent extraction algorithm based on deep learning technology. Firstly, the application status, existing problems and development status of deep neural network in the history at home and abroad are introduced. Secondly, the planning principle and characteristics of the algorithm, experimental results and theoretical model are analyzed. The experimental results show that when the parameter is 4, the error rate of the algorithm is the lowest, and the difference between the theoretical optimal path distance and the actual average optimal path distance is very small. Therefore, the algorithm can meet the needs of tourism path planning.
A Survey on Algorithms in Game Theory in Big Data

Conventional data processing application software for handling huge data or intricate data is eased nowadays due to the evolving technology specifically termed as big data. Three Vs, namely, volume, velocity and variety, play a significant role in big data which is a necessity for a particular technology as well as analytical approaches involved in its transformation into value. The major issue involved is that the steady growth of it and opportunities are not caught by the organization frequently as well as extraction of actionable data.
Broken Neutral Classification through Anomaly Detection using Features based on Voltage and Current Observations

Neutral integrity failure is a potentially dangerous condition where the current in a residential premise is unable to return to the source via neutral wire. This may cause electric shock when the current flows through a resident to return via earth. Using voltage and current data obtained from a smart meter, a baseline model of a residential premise can be built for broken neutral classification through anomaly detection. Firstly, voltage and current observations are aggregated by day and used to calculate four features. These features are slope, voltage range, voltage difference and voltage standard deviation. The resulting outputs are standardized before being used to train the anomaly detector using Local Outlier Factor algorithm. The resulting approach captures 95&#x0025; of the broken neutral incidents that it is presented with and can be used by distribution service network providers to remediate sites with neutral integrity issues and improve residential safety.
Glitch-less hardware implementation of block ciphers based on an efficient glitch filter

In this paper, we present a glitch-less hardware structure based on a glitch filter for the implementation of the block ciphers. The glitch filter circuit is used to build a glitch-free arbitrary binary signal at full clock speed based on both positive and negative clock edges. In this technique, the desired signal is constructed by combining two signals whose individual frequencies are one-half that of the original signal. From the hardware point of view, the circuit does not employ delay blocks that depend on routing delays. Therefore, this property is suitable for use in both VLSI- and FPGA-based implementations. The glitch filter stops pulses of narrow width while allowing the passage of pulses of larger width. The circuit removes chatter and extraneous noise in hardware structures of the cryptosystem without affecting the output signal. The glitch filter can play both roles of the register for save of data and also glitch remover. In this case, the area overhead for the glitch-less structure is negligible. Therefore, we achieving a good trade-off between glitch-less and area overhead which is essential for an efficient design. The delay and area consumption of the different block ciphers such as PRESENT, HIGHT, and SPECK based on glitch filter circuit are achieved in 180 nm CMOS technology. The results show that the proposed structures have an acceptable area and time complexities compared to the original structures. • The glitch filter is used to build a glitch-free binary signal at full clock speed. • The circuit does not employ delay blocks that depend on routing delays. • The glitch filter can play both roles of the register and glitch remover.
The Research and Implementation of Intelligent Reading Assistant System Framework Based on Knowledge Graph

As an significant form of language skills, reading comprehension teaching frequently requires educators to invest a great deal of energy in analysising and designing exercises for articles. Educators aren't capable of analyzing the reading comprehension ability or making personalized plan for every student just relying on manpower. In this paper, we propose an intelligent reading assistant system which provides learners with cognitive assistance in the reading process through the automatic generation model of article knowledge graphs and able to generate exercises through the question chain model based on text knowledge graphs. The system also can construct a learner's reading ability report based on the learner's profile and make a personalized learning path. The system promotes the efficiency of reading comprehension teaching, realizes personalized education and brings new research trends to reading comprehension teaching.
Bengali Handwritten Grapheme Recognition Using CutMix-Based Data Augmentation

Recent work has demonstrated that deep learning has been widely used in various challenging images classification tasks. In our paper, we describe Bengali handwritten grapheme classification based on deep learning. We take advantage of a deep learning architecture called convolutional neural networks (CNNs) due to its splendid performance, effectiveness of convolutional operations on many different visual recognition tasks. In addition, we propose Cutmix-based data augmentation to virtually enlarge the training dataset size and avoid over-fitting in performing the further recognition tasks. Comparisons have been made between Cutmix-based data augmentation and other data augmentation techniques (such as flipping, distorting, rotation, mixup) on four widely-used CNN architectures: Inception, ResNet50, DenseNet169 and EfficientNet B1. During experiments, CutMix consistently outperforms the other augmentation strategies on Bengali handwritten grapheme classification tasks, by making efficient use of training pixels and retaining the regularization effect of regional dropout. Unlike previous augmentation methods, our CutMix-trained EfficientNet classifier results in consistent performance gains in Bengali handwritten grapheme detection and handwritten image captioning benchmarks. Moreover, CutMix improves the model robustness against input corruptions and its out-of-distribution detection performances. Therefore, CutMix proved to be a naturally enhanced augmentation strategy with superior concision and effectiveness in classifying Bengali handwritten grapheme.
Scheduling and Routing of Mobile Charging Vehicles for Unmanned Aerial Vehicles Charging

Battery capacity of Unmanned Aerial Vehicle (UAV) limits its ability to perform long-term reconnaissance missions. This paper proposes a model of Mobile Charging Vehicles (MCVs) to charge UAVs on missions. In this scenario, the UAV can land on top of the MCV and move together along its original route. Scheduling and routing of MCVs are optimized to minimize the investment cost and maximize the quality of mission completion while satisfying time- and power-related constraints. For this multi-objective optimization problem, an improved decomposition-based multi-objective evolutionary algorithm (MOEA/D) is adopted to solve it. Finally, the effectiveness of this model is verified by numerical experiments.
Augmented museum experience through Tangible Narrative

This paper is about an interactive and tangible storytelling installation based on the principle of ubiquitous museum. The installation is designed to focus children’s attention on the sailing practice in the Medieval age by engaging children in an experience where they interact with several ancient navigation tools, equipped with sensors and communication capabilities. The networked infrastructure is combined with the approach of tangible narratives to augment the museum experience. We conducted a pilot study with three different groups of participants in order to investigate the interaction with the augmented ancient navigation tools. We used quantitative and qualitative data to get feedback on participants’ engagement while interacting with diegetic and non-diegetic objects and on factors that can impact engagement. Even though preliminary, the results can be useful to designers of ubiquitous museum installations to know possible risks and factors to take into account at design time.
Integrating Line Weber Local Descriptor and Deep Feature for Tire Indentation Mark Image Classification

Tire indentation mark matching is an essential tool used for the investigation of criminal cases and traffic incidents. As such images are unique and uncommon, there is a lack of dedicated databases and relevant research on this topic. This paper presents a feature extraction algorithm effective for tire indentation mark image description. The main contributions include: (1) Line feature Weber local descriptor (LWLD) is proposed, which uses the Gabor orientations instead of the original gradient orientation. This feature can describe texture information of tire indentation mark image more efficiently. (2) An attention model is constructed to produce attention feature map of tire indentation mark image. This attention feature map is then fused with LWLD resulting in a feature with more powerful representation capability. Experimental results prove that the combined use of LWLD and attention model greatly enhances the performance of tire indentation mark image matching tasks.
Challenges in Designing Inclusive Immersive Technologies

Immersive experiences – enabled by technologies such as VR, AR, 360° video and other highly immersive multimedia applications – have the potential to make interacting with various activities more inclusive for many people. This can be achieved by applying the principles of inclusive design. This panel will discuss the current challenges in designing inclusive immersive technologies, and how they should be addressed.
Electronic Card Localization Algorithm Based on Visible Light Screen Communication

In recent years, with the continuous advancement of image processing technology and the popularization of camera-equipped devices, methods of using camera positioning have been gradually developed. The camera positioning can avoid the interference of various wireless signals in the indoor environment, and the positioning stability is strong. At the same time, since most indoor environments and mobile terminals have their own cameras, the use of camera positioning also has the advantage of low hardware costs. In this paper, we propose a QR-Code based localization system which employs QR-code to transmit necessary information for localization. In addition, this paper uses a QR-Code to assist positioning, which improves the accuracy of positioning, and uses a rewritten music algorithm to estimate angle. Finally, a positioning scheme with both precision and low-cost is developed.
Universal Sparse Adversarial Attack on Video Recognition Models

Recent studies have discovered that deep neural networks (DNNs) are vulnerable to adversarial examples. So far, most of the adversarial researches have focused on image models. Whilst several attacks have been proposed for video models, their crafted perturbation are mainly per-instance and totally polluted ways. Thus, universal sparse video attacks are still unexplored. In this article, the authors propose a new method to explore universal sparse adversarial perturbation for video recognition system and study the robustness of a 3D-ResNet-based video action recognition model. A large number of experiments on UCF101 and HMDB51 show that this attack method can reduce the success rate of recognition model to 5% or less while only changing 1% of pixels in the video. On this basis, by changing the selection method of sparse pixels and the pollution mode in the algorithm, the patch attack algorithm with temporal sparsity and the one-pixel attack algorithm are proposed.
A Novel Approach to Encrypt the Data Using DWT and Histogram Feature

In recent years, digital image data techniques are used in very large scale. Data watermarking is the best technique that allows us to hide the secret information into an image. In this paper, discrete wavelet transfer (DWT) low frequency band is used for embedding watermark techniques. Binary code, i.e., 0, 1, is used to hide the data with watermark technique, which hides the information into an image. In this proposed work, information is hidden by carrier image where DWT feature hides the data by histogram feature shifting. By using this technique, information can be hidden point by point, and image can be recovered easily. Further verification is also done by taking authentic dataset image. These operations demonstrate the proposed work with SNR and PSNR with maximum secured information.
Threshold Segmentation Based on Fuzzy Kaniadakis Entropy for Criminal Investigation Images

Criminal investigation image segmentation is considered one of the most important tasks in the field of criminal investigation image processing. However, the segmentation for criminal investigation images is a challenging task due to natural or human factors. In this paper, an image threshold segmentation algorithm based on fuzzy Kaniadakis entropy was proposed for criminal investigation image segmentation. Firstly, the weighted least squares filter was used for image pre-processing. Then, by using membership functions, image fuzzy sets were constructed based on restricted dissimilarity function. Finally, the maximum fuzzy Kaniadakis entropy was corresponded to the optimal segmentation threshold. The experimental results demonstrate that compared with several existing entropy-based thresholding algorithms, the proposed algorithm is effective in terms of visual effects and segmentation quality measures.
The Frame Display of the Digital Clothing Display Design System Based on Intelligent Man-Machine Cooperation

In recent years, as people’s requirements for economy and culture have become higher and higher, the demand for digital store displays has become increasingly urgent, and the research on clothing display design systems has also entered an unprecedented high period. The purpose of this article is to study the architecture display of the digital clothing display design system based on intelligent human-computer collaboration. The research status of clothing terminal display under clothing display and intelligent man-machine collaboration is described, and the functional requirements for the digital clothing display design system software are put forward. Taking the principle of system theory as another theoretical basis, the concept of clothing store system is proposed. Closely combining with the reality of clothing marketing, try to propose a quality evaluation system for clothing stores. An overall evaluation of the display of the clothing store system is made. The experimental results show that when the total score F < 60, the clothing store is a substandard structure display. In order to study and solve the complex display quality of the structure of the clothing display design system the problem provides an effective method.
Breast Cancer Survival Prediction Using Machine Learning

Breast cancer is one of the most prevalent cancers in women, and recent breakthroughs in data mining have provided more insight into the disease and its prognosis. We offer a set of machine learning models for predicting breast cancer survival in this chapter. There are 272 occurrences, 1564 characteristics, and a target variable in the original data, which were obtained from the data world Website (195 not survived, 77 survived). Out of 1564 features, the top ten features were selected using the extreme gradient boosting (XGB) method. 5-fold and 10-fold stratified cross-validation were used to extract the average results and accuracy for both methods is compared. The outcomes of six machine learning classifiers were compared and rated using a variety of statistical rates (accuracy, precision, true positive rate, true negative rate, F1-score, ROC–AUC score). We offer random forest (RFC) and XGB as the top classifiers after evaluating the models, with overall testing accuracy of 78% and 77.2%, respectively. However, all the classifiers performed well in predicting label 0 (high true negative rate) as compared to label 1 (low true positive rate).
MFDNN: Mixed Features Deep Neural Network Model for Prompt-independent Automated Essay Scoring

Most of the existing Automatic Essay Scoring (AES) models are prompt-dependent models that need the rated essays of specific prompt for training. However, there are few studies on prompt-independent AES. This paper studies how to fully use the effective prompt-dependent features to solve the prompt-independent AES problem. Different from the common method of only extracts multiple features, we consider reducing the interference between different features. We propose a new feature, called the deep dependent feature, which is extracted from the essay by a deep neural network. It is the representative feature that can distinguish the prompt label of the essay and has less overlap and conflict with other features. Firstly, we pre-scored the unrated target prompt data to generate pseudo data based on the manually extracted features. Then we build a new model, which is training on pseudo data to learn prompt-dependent information. Our model considers relevance feature, syntactic feature, and deep dependent feature. The performance of our model is evaluated on ASAP datasets, and the results show that our model outperforms the existing methods for prompt-independent AES.