Reviewerly/exHarmony huggingface dataset:
Product review classification using LSA with improved feature weight model

Product review classification plays a vital role in understanding the likes and dislikes of users of the product.This analysis can be utilized to improve the quality of product as expected by users.In this paper, classification of reviews is performed using Naïve Bayes and K-nearest neighbor algorithms.The key factors contributing to improving classification performance are proper feature weights and less number of dimensions.To improve feature weights, a novel feature weight modification technique is proposed which is based on sentiment scores of the Synset words of input set of words.And to reduce the number of dimensions, we used Latent Semantic Analysis (LSA) technique.From the results, it is proved that the proposed method of modifying weights gives significant improvement in classification performance.
Research on Multi-data Center Collaboration Technology for Multi-station Fusion

This paper designs an intelligent collaboration mechanism between the distributed edge data center and the core data center platform. The core data center is responsible for managing and monitoring the distributed data center and the resources of the data center, coordinating the collaborative allocation of resources between the platforms to complete the needs of users. The distributed data center independently manages and monitors the computing resources of the data center and is responsible for processing specific storage and computing tasks. The platforms are connected to each other through the backbone network. This paper innovatively proposes a cloud edge resource graph intelligent scheduling method based on graph database technology that adapts to the collaborative operation of multiple data centers, adapts to multi-center resource collaborative operation scenarios, and is different from traditional centralized data center data resource scheduling methods. The bottom layer is based on graph database technology. Storing resource types and relationships between them, constructing a graph state library describing the correlation between multi-data center cloud-side collaborative resources, and using graph scheduling algorithms to convert heterogeneous multi-data center resource scheduling into directed acyclic graph scheduling problems. Using the minimum cut algorithm of the graph database and the algorithm based on the critical path ensures the optimal scheduling results in a complex multi-data center cloud-side collaborative resource scheduling environment.
DSGSR: Dynamic Semantic Generation and Similarity Reasoning for Image-Text Matching

Cross-modal image-text matching is vital for building visual and language relationship. The biggest challenge is to eliminate the heterogeneity in image and text. Existing fine-grained image-text matching methods make great progress in exploring fine-grained correspondence. However, they only use the Cross-Attention method, which ignores the importance of image region semantics and dynamic image-text matching. In this paper, we propose a novel Dynamic Semantic Generation and Similarity Reasoning (DSGSR) network model for image-text matching. Specifically, we use intra-modal relations to enrich the regional features of the image. Then, in consideration of dynamic cross-modal matching, we dynamically generate the query text or image representation according to the retrieved image or text representation. We also introduce the Graph Convolutional Network (GCN) to deal with the effect of neighbor node information on matching accuracy when measuring the image-text similarity. A large number of experiments and analyses show that the DSGSR model surpass state-of-the-art methods on Flickr30K and MSCOCO datasets.
Event-based Product Carousel Recommendation with Query-Click Graph

Many current recommender systems mainly focus on the product-to-product recommendations and user-to-product recommendations even during the time of events rather than modeling the typical recommendations for the target event (e.g., festivals, seasonal activities, or social activities) without addressing the multiple aspects of the shopping demands for the target event. Product recommendations for the multiple aspects of the target event are usually generated by human curators who manually identify the aspects and select a list of aspect-related products (i.e., product carousel) for each aspect as recommendations. However, building a recommender system with machine learning is non-trivial due to the lack of both the ground truth of event-related aspects and the aspect-related products. To fill this gap, we define the novel problem as the event-based product carousel recommendations in e-commerce and propose an effective recommender system based on the query-click bipartite graph. We apply the iterative clustering algorithm over the query-click bipartite graph and infer the event-related aspects by the clusters of queries. The aspect-related recommendations are powered by the click-through rate of products regarding each aspect. We show through experiments that this approach effectively mines product carousels for the target event.
What Is Learned in Knowledge Graph Embeddings?

A knowledge graph (KG) is a data structure which represents entities and relations as the vertices and edges of a directed graph with edge types. KGs are an important primitive in modern machine learning and artificial intelligence. Embedding-based models, such as the seminal TransE [Bordes et al. 2013] and the recent PairRE [Chao et al. 202] are among the most popular and successful approaches for representing KGs and inferring missing edges (link completion). Their relative success is often credited in the literature to their ability to learn logical rules between the relations. In this work, we investigate whether learning rules between relations is indeed what drives the performance of embedding-based methods. We define motif learning and two alternative mechanisms, network learning (based only on the connectivity of the KG, ignoring the relation types), and unstructured statistical learning (ignoring the connectivity of the graph). Using experiments on synthetic KGs, we show that KG models can learn motifs and how this ability is degraded by non-motif (noise) edges. We propose tests to distinguish the contributions of the three mechanisms to performance, and apply them to popular KG benchmarks. We also discuss an issue with the standard performance testing protocol and suggest an improvement.
SoPC Implementation of a Genetic Algorithm for Circle Detection

This article presents a system-on-programable-chip implementation of a genetic algorithm for circle detection. The use of this implementation technique allows the development of an efficient, decentralized and embedded system with high scalability and robustness, in addition to providing it with an effective and easy-to-use interface. The hardware components of the system implement the evolutionary process and the software elements perform image pre-processing tasks and provide the user interface. The SoPC was implemented on a Zybo-Z7 development board equipped with a Xilinx Zynq-7000 family device and it has been numerically validated on synthetic and real images. Detection rates obtained for both types of images demonstrate the suitability of this proposal to design embedded systems with size, resources and power consumption limitations for applications in Industry 4.0 and other related paradigms.
Human and Organizational Threat Profiling Using Machine Learning

The usage of online social networking sites is increasing rapidly. But the downside is that the growth of various kinds of ongoing social media threats such as fake profiles, cyberbullying, and fake news. Many important observations can be made to increase the existing knowledge about social media threats by studying various information exchanged through public and organizations. One direction is to conduct studies on human behavior and personality traits using public user profile data and the organizational threat classifying. This research aims to build a system to predict human personality behaviors on social media profiles based on the OCEAN Model and company-based threat profiling. All the data collected relating to everyone in the consumer’s friend list is analyzed to obtain the threatening behaviors and classified according to the OCEAN to generate a threat report. Organizational network gathered log data for filtered log protection against malware. Logs received from these endpoints will be collected by collectors. Those logs will be forwarded to our filter, made of a Machine Learning Algorithm (MLA). This will be a custom MLA specially designed for this purpose. MLA will classify and categorize threats according to their severity, filtered log protection system against malware and other threats.
Software Security and the “Hamster Wheel of Pain”

This chapter assesses how the feedback loop that emerged during the dot-com boom had created an ever-increasing number of new vulnerabilities that organizations needed to respond to by applying security patches. At the heart of the perimeter model of security was the firewall, but an organization's firewall had to permit web traffic to reach the organization's web server. If the web server had a vulnerability, which it almost certainly would experience over time, a hacker could compromise its security and likely then compromise the security of the operating system on which the web server was running. A desire for the focus of security efforts to be placed on operating system security was a curious return to the thinking of the 1970s and 1980s. Microsoft is best known for its computer software, and so its security efforts have historically been focused on software security. Ultimately, Microsoft was successful in reducing the number of security vulnerabilities in its software, in contributing to the creation of software security as a discipline, and in raising the profile of software security generally.
An Efficient Approach Toward Security of Web Application Using SQL Attack Detection and Prevention Technique

SQL injection attacks are widely used by the imposters due to its less complexity and high flexibility. The proposed methodology is intended to perform detection and prevention of such malware scripted SQL queries using SVM. The model first trained with various malware strings and then tested with unknown scripts. It also performs prevention of web application from the SQL malware string using string analyzer and dynamic candidate evaluation. The string analyzer is a grammar-based algorithm that locates the context on the string using regular grammar. Dynamic candidate solution is used to dynamically identifies the malware script using review policy network in which it first generate the parse tree of the input query and then it analyze each node of the tree. It also finds the variation of detection time with respect to accuracy. For the prevention system, simplicity calculates ratio of prevented attack queries out of total number of input queries. The accuracy of the model is good and also the fault rate is minimal.
SINGLE-SHOT FACE DETECTION AND RECOGNITION BASED ON CNN

The paper proposes an architecture and training method of a deep convolutional neural network for simultaneous face detection and recognition. The implemented approach combines the ideas of SSD (Single Shot Detector) and Faster R-CNN (Region proposal Convolutional Neural Networks) algorithms. Face detection is performed similarly to single-stage detection algorithms, and then a biometric template is built by employing RoI (Region of Interest) pooling layers and using the separate branch of the neural network. Training process includes three stages: pretraining of thebasic CNN for face recognition on face images, fine-tuning by using RoI pooling on in painted face images, adding SSD layers and fine-tuning on face detection. Wherein, at the latter stage, training is performed by using shared layers technology for two databases simultaneously. The main feature of the algorithm is high processing speed, which does not depend on the number of faces in the input image. For example, in case of using ResNet-34 as the core architecture for the algorithm, the required time for detecting faces and building biometric templates on an image with 100 faces is less than 13 ms. For training purposes we use CASIA-WebFace for face recognition task and Wider Face for face detection task. Testing is performed on FDDB (Face Detection Dataset and Benchmark), since this database is closer to practical applications than Wider. As long as the main practical task the developed method is intended for is face reidentification, we use Fei Face DataBase for face recognition quality testing. We obtain TPR (True Positive Rate) = 0.928@1000 on FDDB Face DataBase and FAR (Face Acceptance Rate) = 0.03309@FRR (Face Rejection Rate) = 10–4. Therefore, the proposed algorithm allows solving face detection and reidentification tasks in real time with any number of faces on an input image.
Hand part segmentations in hand mask of egocentric images using Distance Transformation Map and SVM Classifier

Forearm and palm segmentation is a crucial element in the hand-pose estimation and mobility of the arms and hands estimation. However, hand parts separations (i.e, forearm and palm) from egocentric images have less been explored. In this study, we propose a novel hand forearm and palm segmentation method using the distance transformation map and an SVM classifier. First, we use a distance transformation map of the hand mask to find circles inscribing on the hand mask. These circles are vectored and construct an SVM classifier to predict the correct circle for approximating the palm region. Based on the predicted palm area, we propose a method for forearm and palm segmentations. The proposed method is evaluated on the rehabilitation dataset which includes egocentric hand mask images extracted from the hand rehabilitation exercise egocentric images. The results show that the proposed method successfully segments hand and forearm with high accuracy and requires low computational time.
Combinatorial Boosting of Ensembles of Diversified Classifiers for Defense Against Evasion Attacks

Adversarial evasion attacks challenge the integrity of machine learning models by creating out-of-distribution samples that are then consistently misclassified. With a variety of detection and mitigation approaches proposed already, more sophisticated attacks typically defeat them. One of the most promising group of such approaches is based on creating multiple diversified models and leverage their ensemble properties for detection and mitigation of attacks. However, such approaches entail heavy computational cost for designing and training a significant number of models. The paper proposes (i) a combinatorial boosting of the number of diversified models that provides an exponentially expanded scope of reliable decisions, and (ii) robust methods for fusion of the resulting models and their combinations towards enhanced decisions in both benign and adversarial scenarios. Several versions of the approach were implemented and tested for network intrusion detection and color image classification tasks; the results show significant increase of resiliency against evasion attacks with low impact on benign performance.
Class 3 Wiener Filtering for Underwater Image Enhancement and Restoration

Visibility in underwater images is usually poor because of the presence of impurities and light being absorbed and scattered when traveling through the impure water. In this paper, TURBID image datasets are used to undergo image enhancement and restoration. TURBID datasets consist of three different types of underwater image conditions where the water solution is added with blue solution, milk solution, and chlorophyll solution. Then, the images undergo Histogram equalization (HE) and Wiener filter respectively for image enhancement and image restoration. HE as the chosen enhancement method proved that it could enhance the image quality as the water surface can be seen clearer after enhancement. Wiener Filter Class 3 is chosen as a restoration method to reduce the mean square error (MSE) value and to get a high Peak signal-to-noise ratio (PSNR) with desired SNR value. Finally, these two image processing technique, i.e., enhancement, and restoration are combined and then all the quantitative values are compared to show the image quality and clarity can be improved with the two processing techniques.
A Latent Feature Autoencoder via Adversarial Training for Unsupervised Anomaly Detection

Anomaly detection is an active area of computer vision and widely applied in diverse fields. As known, it is a considerable challenge to collect abnormalities in practice. To tackle it, researchers propose many unsupervised or semi-supervised algorithms based on autoencoders or their variants. They focus on reconstruction loss between input and reconstructed samples but ignore the latent features extracted by an autoencoder. Namely, the existing algorithms tend to learn the local features of samples and have the approximate capabilities to reconstruct normal and abnormal samples. To capture the latent spatial features of anomaly detection, we present an unsupervised latent feature autoencoder via adversarial training. Particularly, we propose a weighted feature consistency loss to exploit the correlation between the corresponding layers of an encoder and decoder in the autoencoder. A feature discrimination loss is also designed to improve its ability to identify real and reconstructed samples by utilizing the latent spatial features of a discriminator. Next, we develop a discriminator that consists of two networks, i.e., a feature extraction network and a classification one. The pre-trained model can extract the input features accurately and stably, while the classification network can avoid excessive information losses and strengthen the ability to acquire deep semantics. Extensive experiments conducted on six public datasets show that the proposed method is competitive with the existing mainstream methods.
Two-Step Blind Deconvolution of UPC-A Barcode Images

Barcodes are widely used in supermarkets and tracking systems. One noticeable difficulty in barcode recognition is caused by blurring artifacts. For example, a focal blur may occur when the data is captured at a far distance, and motion blur is often inevitable under a low light condition. To decipher the barcode, many barcode reading systems may require more information than the image alone, e.g., the digits written below the barcode and/or a blurring kernel that describes how the data is blurred from a clean image. In this paper, we propose a method to recover a clean barcode from the blurred data without any additional information (e.g. information on the blurring kernel and so on). In particular, we propose a two-step procedure for blind deconvolution of barcode images. In the first step, we estimate the blurring kernel by exploiting a barcode structure with an exclusive search of the first two digits. Using the estimated kernel, we rely on a non-blind deconvolution technique to restore the entire barcode. We conduct numerical experiments based on both synthetic and real data, showing the efficiency and accuracy of the proposed method.
Noise and Scalability Investigation of SOA-based All-optical Photonic Deep Neural Network

We emulate and experimentally validate the scaling of SOA-based all-optical deep neural networks by accurately modeling the OSNR degradation in a chain of SOAs: The photonic neural network can scale up to 16 layers when using only 4 neurons/layer without notable accuracy degradation.
Three Branches of Accountability

Security protocols usually describe how honest agents behave, and one proves some security goals to hold even in the presence of an intruder who just does whatever he is capable of where cryptography alone does not provide sufficient protection, accountability can help as a deterrent for the intruder, because his actions may be detected and he could be punished. The novelty of this work is to model actually all three branches of government that are relevant here. First, instead of protocols we have a legal system that defines which actions are legal. Second, we have the police that may detect some crimes and collect evidence. Third, we have a justice system that evaluates evidence, can subpoena participants, and finally may convict players. The broad definition of a legal system allows us to avoid defining all protocols that honest participants may engage in. Rather we describe players (no matter if honest or dishonest) who may do anything that is legal and who can do anything except breaking the cryptography.
Potential of tidal power plants on Tibo Beach with spektrum method

Abstract As time evolved, the demand for electrical energy also increased. As a result, renewable energy is needed to replace fossil fuels as fuel for electricity generation. Tidal water is one of the renewable alternative energy sources used as fuel for electricity generation. The tides will never run out and can also be predicted. One method that can predict data about waves is the Spectrum Method. Tide prediction data can be indicated by carrying out tidal height observations for 15 days at Tibo Beach, Donggala Regency, Central Sulawesi. Based on observations, the data obtained are the highest high water level (HHWL) is 2.4 meters, and the lowest low water level (LLWL) is 0.1 meters. Also, the type of tides on the Tibo coast is a type of mixed waves that tend to double daily, there are two tides with high water and two tides with low water with different water levels. Because the value of formzahl is 0.312. With a 2.4 meter high HHWL and a sea area of 35.4 km2, the electricity generated is 13.08 MW.
Integration of The Hidden Genes Genetic Algorithm With The Evolutionary Mission Trajectory Generator

The Hidden Genes Genetic Algorithm (HGGA) is an automated global trajectory optimization tool that has the capability of searching for the optimal flyby sequence. The Evolutionary Mission Trajectory Generator (EMTG) is also a global trajectory optimization tool that optimizes low-thrust and impulsive trajectories. However, the current version of EMTG does not have the capability of autonomously searching for the optimal flyby sequence in interplanetary trajectories. This work aims to integrate the HGGA with the EMTG. This paper presents the details of this integration along with test cases for known benchmark problems. In all of the test cases, the new integration is able to find autonomously the optimal flyby sequence.
Missing Glow Phenomenon: Learning Disentangled Representation of Missing Data

Learning from incomplete data has been recognized as one of the fundamental challenges in deep learning. There are many more or less complicated methods for processing missing data by neural networks in the literature. In this paper, we show that flow-based generative models can work directly on images with missing data to produce full images without missing parts. We name this behavior Missing Glow Phenomenon. We present experiments that document such behaviors and propose theoretical justification of such phenomena.
Human Activity Recognition Using Stacked LSTM

Recently, the rapid growth of artificial intelligence has led to the deployment of Deep learning techniques for Human Activity Recognition (HAR) issues. Lately, this application has received a lot of attention because of its effectiveness in gaining knowledge from signals generated by sensors. It can use nonlinear transformations to extract features from raw data in a hierarchical way, unlike traditional methods that depend on feature engineering. This paper proposed a human activity recognition model that can deal directly with data sensors without any pre-processing. It uses stacked LSTM layers to recognize several daily life activities. Experience on two datasets shows that we get accuracy comparable or better than the existing state-of-the-art methods.