Reviewerly/exHarmony huggingface dataset:
Applying Deep Learning-Based Personalized Item Recommendation for Mobile Service in Retailor Industry

Various kinds of mobile services allow integrating terminal customers as important coproducers into the whole retailer’s business processes. People have enjoyed increasing popularity in the past years since they allow saving costs and increasing satisfaction. However, in some retail settings, as the technology relies on retailers providing terminals, it does not yet fully utilize the possibilities provided by mobile service, which until recently have mostly served as shopping aids. Recommendation systems can provide accurate recommendation services to users, especially in the field of e-commerce. In this study, a mobile retail terminal, Kkbox, leverages deep learning-based recommendation and self-service technologies to provide an express and personalized self-checkout retail environment without the engagement of storekeepers and cashiers. An attention-based mechanism for product personalization recommendation model is adopted, and it models the intrinsic relationship between users’ historical interactions with products through a multilayer self-attentive network and then feeds the output of the multilayer self-attentive network into a GRU network with attention scores to model the evolution of users’ interests. We analyze the performance of the product recommendation module based on user data from multiple perspectives, such as purchase frequency, purchase time, and product category. In the comparison experiments with some traditional recommendation methods, the recommendation accuracy of the model used in this study achieves better results. Besides, it significantly reduces the labor cost and provides enough flexibility. The time performance of app users is independent of store rush. The time for a transaction is significantly lower for app users than the regular shoppers during peak periods. The Kkbox has been deployed in several communities in Taizhou, China, to provide fast and convenient mobile retail services to residents.
Efficient JPEG Encoding Using Bernoulli Shift Map for Secure Communication

To ensure confidentiality and efficient network bandwidth, digital data must be compressed and encrypted. In most communication systems, these two factors are critical for information processing. Image compression and encryption may result in lower restoration quality and performance. Secure-JPEG is an effort to create a compression and encryption technique for digital data. This approach is based on the JPEG compression standard, which is the most extensively used lossy compression scheme. It enhances the usual JPEG compression algorithm to encrypt data during compression. The Secure-JPEG approach encrypts the data while it is compressed, and it may be easily modified to offer near lossless compression. Lossless compression, on the other hand, has a lower compression ratio and is only useful in certain situations. The paper addresses the issue of insufficient security as a result of the usage of a simple random number generator that is not cryptographically safe. The enhanced security characteristics are provided via the Generalized Bernoulli Shift Map, which has a chaotic system with proven security. Several cryptographic tests are used to validate the algorithm's security, and the chaotic system's behavior is also examined.
Perceptually-motivated adversarial training for deep ensemble denoising of hyperspectral images

In this letter, we present a deep-learning-based methodology for recovering hyperspectral images (HSIs) distorted by Gaussian and impulsive noise. This work makes the following contribution: To begin with, the Wasserstein Generative Adversarial Network (WGAN) is used to mitigate the effects of vanishing gradient and mode collapse that can occur when training a vanilla GAN. Secondly, data are passed via three distinct pathways in a parallel ensemble to promote multiscale feature extraction. Normal and multiscale dilated 3D convolutions are utilized to train the model in each pair of parallel paths. Thirdly, features are recovered following data permutation across three different spatial planes (viz. xy,yz, and xz planes) and after passing through parallel convolutional blocks; to promote spatio-spectral similarity within and across the different layers of the HSI data. Fourthly, by adopting Structural Similarity (SSIM) as the content loss, the issue of loss in resolution encountered during adversarial training is mitigated. Finally, the incorporation of 3D depth-wise separable convolution and batch re-normalization (BRN) solves the major issue of computational burden encountered while processing HSI data. Extensive experimental evaluation on synthetically corrupted data and real HSI data (obtained from real hyperspectral sensors) under various degradation conditions suggests that the aforementioned denoising approach could be used in real time.
Path Planning with Hybrid Maps for processing and memory usage optimisation

Robotics will play an essential role in agriculture. Deploying agricultural robots on the farm is still a challenging task due to the terrain&#x2019;s irregularity and size. Optimal path planning solutions may fail in larger terrains due to memory requirements as the search space increases. This work presents a novel open-source solution called AgRob Topologic Path Planner, which is capable of performing path planning operations using a hybrid map with topological and metric representations. A local A<sup>&#x002A;</sup> algorithm pre-plans and saves local paths in local metric maps, saving them into the topological structure. Then, a graph-based A<sup>&#x002A;</sup> performs a global search in the topological map, using the saved local paths to provide the full trajectory. Our results demonstrate that this solution could handle large maps (5 hectares) using just 0.002 &#x0025; of the search space required by a previous solution.
V-net Performances for 2D Ultrasound Image Segmentation

Artificial intelligence (AI) has conquered all areas of human being life through its performance when it is adapted to a particular domain. Nowadays, different research papers are interested in the application of AI in medical area for ultrasound imaging. Hence, the most important task in medical field is imaging and image segmentation since it helps doctors to perform accurate diagnosis and therefore to prescribe the right treatment. In this paper, we study the image segmentation to improve the visualization and quantification of different image regions. To this end we propose the implementation of a 2D version of V-net architecture. The results are compared to the popular medical’s imaging algorithm U-net and its variation U-net++. The performance of our results is validated by the widely used metrics in segmentation field which are Dice coefficient, Sensitivity, Specificity and Accuracy. In addition, losses function has a high influence on training models. Therefore, our model will be experimented under different losses such as function Cross-Entropy, Dice-Similarity-loss, Focal loss and Focal Tversky loss to end up with the good cases for a training model. Extensive simulation of the proposed V-net model shows an improvement of 85.01% in Dice Coefficient, 85% in terms of sensitivity, 99% in specificity and 99% in accuracy.
A Choreography-Driven Approach for Blockchain-Based IoT Applications

The engineering of Internet of Things applications always requires more and more devices to be connected and collaborate in a secure and trusted environment. However, this is not always possible unless considering the adoption of alternative technologies such as blockchain. For this reason, the development of blockchain-based IoT applications is gaining even more interest both in industry and academia. Nevertheless, the combination between IoT and blockchain is perceived as a time and resource-consuming activity, making the development more complex. In such a context, a model-driven approach can overcome such an issue by automating the implementation phases. This paper presents an approach for the development of choreography-driven blockchain-based IoT applications. In particular, it shows the feasibility of the approach in practice by developing a Smart Thermostat scenario.
Hand Gesture Speed Recognition and Classification using IR-UWB Radar Sensor

Human-computer interaction (HCI) is a term that refers to a set of methods and techniques that enable humans to connect with machines and computers. Based on Hand Gesture Recognition (HGR), HCI enables a naturally occurring contactless interface, bringing humans closer to a more natural way of communication. Numerous studies have identified the potential of Impulse Radio-Ultra Wide Band (IR-UWB) radar sensor-based HGR as a future enabler for a diverse variety of applications. This work presents the development of a method to utilise an IR-UWB radar sensor to recognise and classify hand gesture speeds. The findings of the performance tests reveal a very high degree of confidence and accuracy in recognising and classifying hand gestures speeds.
A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different nature. Although efforts have been made to establish a general labeling scheme in their classification, there is still limited data labeled in such a format. The usual approaches are based on feature engineering to correctly identify phishing campaigns, exporting lexical, syntactic, and semantic features, e.g., previous phrases. In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). However, these models lose semantic information related to the specific problem, resulting in a variation in their performance, depending on the different data sets and the corresponding standards used for labeling. In this paper, we propose to extend word embeddings with word vectors that indicate the semantic similarity of each word with each phishing campaigns template tag. These embedded keywords are calculated based on semantic subfields corresponding to each phishing campaign tag, constructed based on the automatic extraction of keywords representing these tags. Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art.
An Ethereum based Fake Product Identification System using Smart Contract

In recent years, counterfeit products have played an important role in the product making industry. It has also affected the company's brand, sales, and profits. Blockchain technology is now being used to identify authentic products and detect counterfeits. Blockchain is a distributed, expanded, digital book that stores business data as blocks in multiple archives linked by chains. No block can be changed or modified because blockchain technology is secure. Customers or users who use the blockchain technology does not have to be depend on mediator to ensure product security of the system. Counterfeit products can be effectively combated using serial numbers which is assigned to the product uniquely. With emerging Blockchain technology trends in this project. As a result, this system can be used to store product information as well as generate distinct product codes for items such as blocks. It generates a unique code for the user and matches it to the Blockchain network. If the input matches, the customer will be notified; if the input does not match, the customer will be notified that the product is counterfeit.
An Efficient VLSI design of Median Filters using 8-bit Data Comparators in Image Applications

In image applications, noise removal is one of the important requirement. Impulse noise is removed by median filters using image processing. Median filter is the good non-linear filter used for edge preservation in images and in real-time hardware implementation, sorting network is used. Sorting network must have an efficient implementation in terms of area, delay and power. Row three cell sorter, column three cell sorter, and right diagonal three cell sorter comprise the median filter. The median value of the inputs is the output of the median filter. Three data comparators give the maximum, minimum and middle value in the three cell sorter. The comparator returns the highest and lowest value of given inputs. The middle value is calculated using a modified shear sorting method. This paper compares two existing data comparators and suggests a modified version of those two existing comparators in factors of delay and area. Full adders and full subtractors are used to build the proposed comparators using MUX. The two&#x2019;s complement based data comparator consumes less time. As a result, an implementation using two&#x2019;s complement is suited for high-speed implementations. The carry select data comparator based implementation consumes smaller area, hence it can be employed in reduced area applications. The median filter is intended to be used with a two&#x2019;s complement based data comparator. In the parallel architecture of modified shear sorting for constructing median filter, different architectures for data comparators were used.
Using active learning selection approach for cross-project software defect prediction

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Data Augmentation in Time and Doppler Frequency Domain for Radar-based Gesture Recognition

This paper presents novel data augmentation techniques for gesture recognition based on multistatic Doppler spectrograms. Contrary to intricate approaches such as generative models or simulations, the presented methods generate additional samples by manipulating data in time and Doppler frequency domain. Thus, they resemble well-known techniques from computer vision tasks, but produce physically plausible data. Moreover, based on a comprehensive dataset a thorough investigation of the impact of training data extent on cross-subject validation accuracy is conducted. It is demonstrated that accuracy tends to saturate with growing training sets, and that the techniques presented in this work can help to reach the accuracy at saturation with only half the number of measurements.
Decoupling Induction and Multi-Order Attention Drop-Out Gating Based Joint Motion Deblurring and Image Super-Resolution

Resolution decrease and motion blur are two typical image degradation processes that are usually addressed by deep networks, specifically convolutional neural networks (CNNs). However, since real images are usually obtained through multiple degradations, the vast majority of current CNN methods that employ a single degradation process inevitably need to be improved to account for multiple degradation effects. In this work, motivated by degradation decoupling and multiple-order attention drop-out gating, we propose a joint deep recovery model to efficiently address motion blur and resolution reduction simultaneously. Our degradation decoupling style improves the continence and the efficiency of model construction and training. Moreover, the proposed multi-order attention mechanism comprehensively and hierarchically extracts multiple attention features and fuses them properly by drop-out gating. The proposed approach is evaluated using diverse benchmark datasets including natural and synthetic images. The experimental results show that our proposed method can efficiently complete joint motion blur and image super-resolution (SR).
Evaluation of Physical Education Teaching Effect Based on Action Skill Recognition

In order to improve the effect of physical education teaching in modern colleges and universities, this paper combines the movement skill recognition algorithm to construct a physical education effect evaluation system and studies the multispectral image reproduction system in practical application. Moreover, this paper gives a detailed description of more advanced functional modules in the system, such as spectral acquisition, spectral reflectance reconstruction, and spectral color correction. In addition, this paper focuses on the algorithm design of the color appearance matching module. By adding the "color appearance transformation" on the source side and the "color appearance inversion" on the reproduction side, the source image and the reproduced image can achieve color appearance matching in the observation condition-independent space. The simulation experiment verifies that the physical education effect evaluation system based on action skill recognition meets the actual needs of physical education and has a good role in promoting the recognition of physical skills and the improvement of physical education effect.
Correlation Analysis Between Japanese Literature and Psychotherapy Based on Diagnostic Equation Algorithm

Literary therapy theory provides a new field for contemporary literature research and is of great significance for maintaining the physical and mental health of modern people. The quantitative evaluation of psychotherapy effects in Japanese healing literature is a hot research topic at present. In this study, a text convolutional neural network (Text-CNN) was selected to extract psychological therapy features with different levels of granularity by using multiple convolutional kernels of different sizes. Bidirectional threshold regression neural network (BiGRU) can characterize the relationship between literature research and the psychotherapy effect. On the basis of the CNN-BilSTM model, a parallel hybrid network integrated with the attention mechanism was constructed to analyze the correlation between literature and psychotherapy. Through experimental verification, the model in this study further improves the accuracy of correlation classification and has strong adaptability.
Theoretical Evaluation of Securing Modules for Educational Chatbot

A "chatbot" is a computer programme that interacts with and processes human conversations while also enabling humans to interact with and communicate with digital devices. It is also described as one of the most advanced and promising expressions of interactions between humans and machines. Its main task is to help students by providing answers to their questions. Existing chatbots are not entirely secure and can create open passages for cyber criminals or hackers to access the data flowing through the chatbot interface. So the chatbot will be made secure using the techniques of authentication (session) timeout and encryption. "Encryption is a method of secure communication that prevents others from accessing data while it is being transferred from one end system to another. The data is encrypted on the sender’s system, so only the intended recipient can decrypt it. " Authentication (session) timeout restricts a time limit on how long an authenticated user can stay "logged in," so this prevents cyber criminals from having enough time to predict their way into somebody’s secured account. This review addresses the various methods and techniques that assist in protecting the student’s information from hackers and making the conversations secure for both the educational institute and the students.
The Benefits and Drawbacks of Eye Tracking for Improving Educational Systems

Educational systems are based on the teaching inputs from teachers and the learning outputs from the pupils/students. Moreover, also the surrounding environment as well as the social activities and networks of the involved people have an impact on the success of such a system. However, in most of them, standard teaching equipment is used while it is difficult to gain some knowledge and insights about the fine-grained spatio-temporal activities in the classroom. On the other hand, understanding why an educational system is not that successful as it was expected, can hardly be explored by just generating statistics about the learning outputs based on several student-related metrics. In this paper we discuss the benefits of using eye tracking as a powerful technology to track people’s visual attention to explore the value of an educational system, however, we also take a look at the drawbacks that come in the form of data recording, storing, processing, and finally, data analysis and visualization to rapidly gain insights in such large datasets apart from many more. We argue that if eye tracking is applied in a clever way, an educational system might draw valuable conclusions to improve it from several perspectives, be it under the light of online/remote or classroom teaching or also from the perspectives of teachers and pupils/students.
Recommending scientific datasets using author networks in ensemble methods

Open access to datasets is increasingly driving modern science. Consequently, discovering such datasets is becoming an important functionality for scientists in many different fields. We investigate methods for dataset recommendation: the task of recommending relevant datasets given a dataset that is already known to be relevant. Previous work has used meta-data descriptions of datasets and interest profiles of authors to support dataset recommendation. In this work, we are the first to investigate the use of co-author networks to drive the recommendation of relevant datasets. We also investigate the combination of such co-author networks with existing methods, resulting in three different algorithms for dataset recommendation. We obtain experimental results on a realistic corpus which show that only the ensemble combination of all three algorithms achieves sufficiently high precision for the dataset recommendation task.
Adversarial Email Generation against Spam Detection Models through Feature Perturbation

Machine learning-based spam detection models learn from a set of labeled training data and detect spam emails after the training phase. We study a class of vulnerabilities of such detection models, where the attack can manipulate a trained model to misclassify maliciously crafted spam emails at the detection phase. However, very often feature extraction methods make it very difficult to translate the change in the feature space to that in the textual email space. This paper proposes a new attack method of making guided changes to text data by taking advantage of findings of generated adversarial examples that purposely modify the features representing an email. We study different feature extraction methods using various Natural Language Processing (NLP) techniques. We develop effective methods to translate adversarial perturbations in the feature space back to a set of “magic words”, or malicious words, in the text space, which can cause desirable misclassifications from the attacker’s perspective. We show that our attacks are effective across different datasets and various machine learning methods in white-box, gray-box, and black-box attack settings. Finally, we discuss preliminary exploration to counter such attacks. We hope our findings and analysis will allow future work to perform additional studies of defensive solutions against this new class of attacks.