Reviewerly/exHarmony huggingface dataset:
Attentive Hierarchical Label Sharing for Enhanced Garment and Attribute Classification of Fashion Imagery

Fine-grained information extraction from fashion imagery is a challenging task due to the inherent diversity and complexity of fashion categories and attributes. Additionally, fashion imagery often depict multiple items while fashion items tend to follow hierarchical relations among various object types, categories, and attributes. In this study, we address both issues with a 2-step hierarchical deep learning pipeline consisting of (1) a low granularity object type detection module (upper body, lower body, full-body, footwear) and (2) two classification modules for garment categories and attributes based on the outcome of the first step. For the category and attribute-level classification stages, we examine a hierarchical label sharing (HLS) technique in two settings: (1) single-task learning (STL w/ HLS) and (2) multi-task learning with RNN and visual attention (MTL w/ RNN+VA). Our approach enables progressively focusing on appropriately detailed features for automatically learning the hierarchical relations of fashion and enabling predictions on images with complete outfits. Empirically, STL w/ HLS reached 93.99% top-3 accuracy while MTL w/ RNN+VA reached 97.57% top-5 accuracy for category classification on the DeepFashion benchmark, surpassing the current state of the art without requiring landmark or mask annotations nor specialized domain expertise.
Ceiling Improvement on Breast Cancer Prediction Accuracy Using Unary KNN and Binary LightGBM Stacked Ensemble Learning

The employment of data mining, granular knowledge exploration and machine learning approaches in the medical field (several of diseases in bioinformatics) are proven to be prolific as such approaches are thought of computationally empirical within the higher cognitive process of medical practitioners. As an intelligent healthcare strategy, this paper presents a fast and accurate framework for breast cancer disease classification and predictive analysis. In an experimental simulation, we obtained ceiling improvement on prediction accuracy using Unary KNN and Binary LightGBM Stacked Ensemble Learning. We have given the visualization of results and judged the obtained various statistical performance measures. Our empirical evaluation and experimental analysis reflect that this strategy with provable performance guarantees performs well in comparison with other commonly used significant state-of-the-art methods. We observed that the proposed framework can be a powerful tool for simplifying or speeding up computations, and when employed appropriately it can lead to little loss in classification quality.
Smart Mirror for Personalized Content Delivery Through Facial Emotion Recognition

As technologies emerge to integrate many devices and appliances in a smart home environment, smart mirrors have a potential of being used for personalized content delivery to smart home users. In this study, we have developed a smart mirror using internet of things and deep learning technologies to provide users daily news updates and current events based on the user emotion. In addition, the smart mirror will also display current weather information based on the location, calendar information, to-do-list, traffic updates, currency rates, reminders, etc. The proposed smart mirror is powered by a Raspberry Pi and provides facial emotion classification using a FER CNN deep learning model. The deep learning emotion classification model was implemented on an embedded device with minimum computational resources and was able to classify emotions to three emotion labels: happy, sad and neutral with training and validation accuracies of 84.17% and 81.69% respectively. Furthermore, the proposed system proved that it could deliver personalized content based on user emotion, as it recorded an experimental accuracy of 80% when it was tested with 60 test-cases from 10 different pre-registered users of the system.
Vision-Based Hand Tracking System Development for Non-Face-to-Face Interaction

Human-computer interaction (HCI) focuses on the interaction between humans and computers and it exists ubiquitously in our daily lives, especially in post COVID era where non-face-to-face interaction is common. Since HCI usually uses a physical controller such as a mouse or a keyboard, it hinders National User Interface, giving a middle ground between the user and the computer. This paper presents a vision-based hand tracking system development for non-face-to-face interaction, which aims to improve HCI by being able to track the hand which will act as the pen and functioning as a reusable writing surface for creating texts, drawings, and such as well as removing or erasing using the user’s hand as the pen, and utilizing Open Computer Vision Library (OpenCV) and Mediapipe. Using the computer’s camera the hand will be tracked as the pen for creating basic drawings and handwriting. The vision-based board where the user can draw on and the pen or marker will be the user’s hand. The results indicate that this system is accurate enough to be a feasible application for handwriting ad basic drawings.
Reversible Video Steganography Using Quick Response Codes and Modified ElGamal Cryptosystem

The rapid transmission of multimedia information has been achieved mainly by recent advancements in the Internet's speed and information technology.In spite of this, advancements in technology have resulted in breaches of privacy and data security.When it comes to protecting private information in today's Internet era, digital steganography is vital.Many academics are interested in digital video because it has a great capability for concealing important data.There have been a vast number of video steganography solutions developed lately to guard against the theft of confidential data.The visual imperceptibility, robustness, and embedding capacity of these approaches are all challenges that must be addressed.In this paper, a novel solution to reversible video steganography based on Discrete Wavelet Transform (DWT) and Quick Response (QR) codes is proposed to address these concerns.In order to increase the security level of the suggested method, an enhanced ElGamal cryptosystem has also been proposed.Prior to the embedding stage, the suggested method uses the modified ElGamal algorithm to encrypt secret QR codes.Concurrently, it applies two-dimensional DWT on the Y-component of each video frame resulting in Approximation (LL), Horizontal (LH), Vertical (HL), and Diagonal (HH) sub-bands.Then, the encrypted Low (L), Medium (M), Quantile (Q), and High (H) QR codes are embedded into the HL sub-band, HH sub-band, U-component, and V-component of video frames, respectively, using the Least Significant Bit (LSB) technique.As a consequence of extensive testing of the approach, it was shown to be very secure and highly invisible, as well as highly resistant to attacks from Salt & Pepper, Gaussian, Poisson, and Speckle noises, which has an average Structural Similarity Index (SSIM) of more than 0.91.Aside from visual imperceptibility, the suggested method exceeds current methods in terms of Peak Signal-to-Noise Ratio (PSNR) average of 52.143 dB, and embedding capacity 1 bpp.
Text Sentiment Analysis based on BERT and Convolutional Neural Networks

The rapid development of the network has accelerated the speed of information circulation. Analyzing the emotional tendency contained in the network text is very helpful to tap the needs of users. However, most of the existing sentiment classification models rely on manually labeled text features, resulting in insufficient mining of deep semantic features hidden in the text, and it is difficult to improve the classification performance significantly. This paper presents a text sentiment classification model combining BERT and convolutional neural networks (CNN). The model uses BERT to complete the word embedding of the text, and then uses CNN to learn the deep semantic information about the text, so as mine the emotional tendency towards the text. Through verification on the large movie review dataset, BERT-CNN model can achieve an accuracy of 86.67%, which is significantly better than traditional classification method of textCNN. The results show that the method has good performance in this field.
Estimation of the Ambit of Breast Cancer with a Modified Resnet Analysis Using Machine Learning Approach

Breast Cancer has been one of the most common reasons for mortality and morbidity among the females around the world especially in developing countries. In this regard, Mammography is a popular screening technique for breast cancer diagnosis so as to label the existence of cancerous cells. The present work encompasses the design and development of a M-ResNet (Modified ResNet) approach so as to classify the breast cancer into benign and malignant conditions with the inclusions for supervised classification models with the training of both upper as well as the lower layers of the designed networks. The efficacy of the developed approach was evaluated using various performance evaluators such as those of sensitivity, specificity, accuracy and F1-Score. Bi-Rads score was used as a basis for the classification process wherein a score of 0-3 correlated to benign and it is non-cancerous nature of tissues whereas malignancy was denoted by a score of 4 and above. InBreast dataset, a publicly available online dataset with 112 breast images were used for the evaluation of the developed paradigm. The present paradigm portrayed an accuracy of 96.43% with Area Under the Curve (AUC) of 95.63%.
Analytical Review of Methods for Detection, Localization and Error Correction in the Residue Number System

In the modern world, one of the main goods is information. In this regard, two main tasks arise: increasing the data transfer rate and checking the correctness of the transmitted data, and if errors are found, correcting them. One of the ways to speed up the processing and transmission of data is to use a Residue Number System (RNS) to represent them. In this paper, we have considered some methods for detecting and correcting errors in the data presented in the RNS. Two effective methods for finding and correcting errors are the projection method and the syndrome method. The article analyzes these two methods for excess RNS. The effectiveness of each of them for detecting, localizing and correcting errors is shown using specific examples. As a result of the considered example, conclusions were drawn about the effectiveness of the methods of the syndrome and projections. It was shown that with minimal redundancy, a high speed of the methods was obtained. It is noted in the work that for the projection method only 9 projections can be used to correct errors.
Detecting Arguments and Their Positions in Experimental Communication Data

This paper applies argument mining techniques to political discussions obtained from an online chat experiment prior to a ballot. We investigate the task of automatically detecting chat messages that give justification for an underlying claim. We use bag-of-words features as well as state of the art embedding models to train different classifiers on the given task. Moreover, we automatically detect each argument's position and compare our method to a method based on word scores proposed in the political science literature. Our results show that argument mining techniques can be successfully applied to experimental communication data and that it can replace labor-intensive manual annotations by human coders.
Generalizability in mixed models: Lessons from corpus linguistics

Part of the generalizability issues that haunt controlled lab experiment designs in psychology, and more particularly in psycholinguistics, can be alleviated by adopting corpus linguistic methods. These work with natural data. This advantage comes at a cost: in corpus studies, lexemes and language users can show different kinds of skew. We discuss a number of solutions to bolster the control.
Guided Visual Analysis of Multivariate Time Series

Time series analysis examines the behavior of data over time. Many papers analyze single variable patterns and trends. However, few proposals have been made for analyzing various variables; this is because when the complexity of the analysis increases, the time series are not easy to understand due to their dynamic and heterogeneous nature. We can resort to Visual Analytics, which transforms complex data into simple graphs, which not only support the exploration of multivariate patterns but also assist the user in understanding found patterns. However, as the set of multivariate time series grows, the analysis methods become increasingly complex, making it difficult to obtain knowledge. In this way, we propose a new approach to visual analysis based on guides and oriented to multivariate time series, which reduces the complexity in the analysis process through guidance and direction guides. An interactive visualization tool was implemented using guides called VisWeb, which integrates the proposed guides with Visual Analysis techniques. For optimal visual analysis of multivariate time series with guides and to demonstrate the reduction of complexity, four questions were posed, designed by the domain expert, referring to the workflow of tasks, task guide, history of the sequence of steps, and limitation of visual space. The results showed that the proposed approach adequately satisfies the questions posed, allowing a reduction in complexity.
Semantic-based false alarm detection approach via machine learning

Many automated vulnerability detection tools are widely used to detect potential security vulnerabilities in software systems. However, these tools suffer from a large amount of false alarms due to their conservative estimation, which reduces the usability of these tools. To solve this problem, many approaches are proposed which use machine learning models to filter out false alarms. These approaches mainly learn classifiers by using lexical features or syntactic features extracted from source codes where alarms are located. Yet, they ignore the semantic features of the source codes being analyzed. In this paper, we propose an approach that extracts semantic features to train the machine learning model and then predicts whether a new alarm report is likely to be a false positive. Specifically, we construct the control flow graph (CFG) of functions where alarms locate, use the graph embedding algorithm to capture the semantic information of CFG, and train machine learning classifiers to detect false alarms. We conduct our experiment on the large-scale Juliet Test Suite, and the results show that our approach is more effective than lexical and syntactic approaches.
A R-ASK OFDM Modulation and Demodulation Scheme Against Vocoder Compression

Resisting the compression on signals by the vocoder of the network is a difficult point to achieve end-to-end secure voice communication. However, only the modulation algorithms for traditional GSM voice channels are available in the relevant fields. Most of these algorithms only pass simulation and cannot be applied to actual scenes. This paper proposes an anti-vocoder compression modulation algorithm that combines relative amplitude shift keying with orthogonal frequency division multiple. The data is modulated by the difference of the frequency domain amplitudes of adjacent subcarriers, with a modulation rate of up to 1.5 Kbps and good anti-compression performance. A synchronization frame header is also designed, which has good anti-vocoder suppression characteristics. After the actual network voice channel transmission test, the algorithm modulation error rate is less than 0.5%, and the restored voice has high intelligibility. The overall scheme can be applied to the instant messaging of mainstream network voice software and has practical application value.
Applying Software Engineering Design Principles to Agile Architecture

Most enterprise IT systems are very complex with a combination of COBOL and Java programs running on multiple platforms. What is needed is a solid IT architecture that supports the operation and growth of a cross-platform IT system. It must enable the iterative and incremental development of applications that are foreseen in an agile development process. The design concept of such an architecture with its infrastructure and development tool is presented in this chapter. This design concept is based on the design principles and architectural patterns of software engineering. The architecture is a combination of layered, component-based, and service-oriented architectural patterns. The agile development process is based on a model-driven approach. The architecture and development approaches were first introduced in 2004. Since then, many applications have been developed on time and within budget.
Role of Machine Learning in Credit Card Fraud Detection

Fraud is the occurrence of any activity done using misleading, deceptive, or illegal ways which is done by someone to defraud you of your money (or capital), or otherwise jeopardizes financial well-being of you or your company. Online fraud is defined as fraud done through the use of the internet. Identity theft or outright money fraud are both examples of online fraud. Fraud detection means a collection of activities to avoid the collecting of money/property by misleading pretensions. A number of sectors are today using fraud detection which includes ecommerce vendors and banking agencies. Due to rapid advancements within the digital commerce generation, the mode of payment has moved from cash to digital settlements such as debit/credit card, online wallet payment, and online banking, the market value of fraud detection and prevention is rising year over year. As a result, financial fraud is increasing at a rapid rate for personal gain. Banking/financial firms are developing fraud detection mechanisms to protect consumers from fraudulent transactions in response to this scenario. This paper explores different types of frauds, the limitations of existing detecting systems and the advantages of using machine learning for fraud detection.
FaiRIR: Mitigating Exposure Bias From Related Item Recommendations in Two-Sided Platforms

Related item recommendations (RIRs) are ubiquitous in most online platforms today, including e-commerce and content streaming sites. These recommendations not only help users compare items related to a given item but also play a major role in bringing traffic to individual items, thus deciding the exposure that different items receive. With a growing number of people depending on such platforms to earn their livelihood, it is important to understand whether different items are receiving their desired exposure. To this end, our experiments on multiple real-world RIR datasets reveal that the existing RIR algorithms often result in very skewed exposure distribution of items, and the quality of items is not a plausible explanation for such skew in exposure. To mitigate this exposure bias, we introduce multiple flexible interventions [fair related item recommendation (FaiRIR)] in the RIR pipeline. We instantiate these mechanisms with two well-known algorithms for constructing RIRs—rating singular value decomposition (SVD) and item2vec—and show on real-world data that our mechanisms allow for a fine-grained control on exposure distribution, often at a small or no cost in terms of recommendation quality, measured in terms of relatedness and user satisfaction.
Human Following Robot using Ultrasonic Sensor

Human Following robots have been investigated and actively developed throughout the decades in current contemporary age because to their numerous uses in everyday life and manufacturing. The human robot must use a variety of strategies, including a robot control algorithm, human target identification, and obstacle avoidance. Ultrasonic sensors, infrared sensors, voice recognition sensors, laser range sensors, camera charging-coupled devices (CCD), and other human-following robotic processes have been proposed. These innovations can distinguish between a versatile robot and a person in the general region. We provide another method for identifying the location of a human robot using an ultrasonic and infrared camera in this study, which is an important step in the robot's human evolution. In our undertaking, the robot is outfitted with a ultrasonic sensor which catches and identifies a person. A basic application carried out progressively utilizing a PI regulator shows benefits of the proposed technique.
Synthesis-Guided Feature Learning for Cross-Spectral Periocular Recognition

A common yet challenging scenario in periocular biometrics is cross-spectral matching - in particular, the matching of visible wavelength against near-infrared (NIR) periocular images. We propose a novel approach to cross-spectral periocular verification that primarily focuses on learning a mapping from visible and NIR periocular images to a shared latent representational subspace, and supports this effort by simultaneously learning intra-spectral image reconstruction. We show the auxiliary image reconstruction task (and in particular the reconstruction of high-level, semantic features) results in learning a more discriminative, domain-invariant subspace compared to the baseline while incurring no additional computational or memory costs at test-time. The proposed Coupled Conditional Generative Adversarial Network (CoGAN) architecture uses paired generator networks (one operating on visible images and the other on NIR) composed of U-Nets with ResNet-18 encoders trained for feature learning via contrastive loss and for intra-spectral image reconstruction with adversarial, pixel-based, and perceptual reconstruction losses. Moreover, the proposed CoGAN model beats the current state-of-art (SotA) in cross-spectral periocular recognition. On the Hong Kong PolyU benchmark dataset, we achieve 98.65% AUC and 5.14% EER compared to the SotA EER of 8.02%. On the Cross-Eyed dataset, we achieve 99.31 % AUC and 3.99% EER versus SotA EER of 4.39%.
Short Text Classification Model Based on BERT and Fusion Network

Abstract: Aiming at short texts lacking contextual information, large amount of text data, sparse features, and traditional text feature representations that cannot dynamically obtain the key classification information of a word polysemous and contextual semantics. this paper proposes a pre-trained language model based on BERT. The network model B-BAtt-MPC (BERT-BiLSTM-Attention-Max-Pooling-Concat) that integrates BiLSTM, Attention mechanism and Max-Pooling mechanism. Firstly, obtain multi-dimensional and rich feature information such as text context semantics, grammar, and context through the BERT model; Secondly, use the BERT output vector to obtain the most important feature information worth noting through the BiLSTM, Attention layer and Max-Pooling layer; In order to optimize the classification model, the BERT and BiLSTM output vectors are fused and input into Max-Pooling; Finally, the classification results are obtained by fusing two feature vectors with Max-Pooling. The experimental results of two data sets show that the model proposed in this paper can obtain the importance and key rich semantic features of short text classification, and can improve the text classification effect.