Reviewerly/exHarmony huggingface dataset:
When is the Cleaning of Subjective Data Relevant to Train UGC Video Quality Metrics?

Outlier analysis and spammer detection recently gained momentum in order to reduce uncertainty of subjective ratings in image & video quality assessment tasks. The large proportion of unreliable ratings from online crowdsourcing experiments and the need for qualitative and quantitative large-scale studies in the deep-learning ecosystem played a role in this event. We study the effect that data cleaning has on trainable models predicting the visual quality for videos, and present results demonstrating when cleaning is necessary to reach higher efficiency. To this end, we present and analyze a benchmark on clean and noisy User Generated Content (UGC) large-scale datasets on which we re-trained models, followed by an empirical exploration of the constraint of data removal. Our results show that a dataset presenting between 7 and 30% of outliers benefits from cleaning before training.
HCIL: Hierarchical Class Incremental Learning for Longline Fishing Visual Monitoring

The goal of electronic monitoring of longline fishing is to visually monitor the fish catching activities on fishing vessels based on cameras, either for regulatory compliance or catch counting. The previous hierarchical classification method demonstrates efficient fish species identification of catches from longline fishing, where fishes are under severe deformation and self-occlusion during the catching process. Although the hierarchical classification mitigates the laborious efforts of human reviews by providing confidence scores in different hierarchical levels, its performance drops dramatically under the class incremental learning (CIL) scenario. A CIL system should be able to learn about more and more classes over time from a stream of data, i.e., only the training data for a small number of classes have to be present at the beginning and new classes can be added progressively. In this work, we introduce a Hierarchical Class Incremental Learning (HCIL) model, which significantly improves the state-of-the-art hierarchical classification methods under the CIL scenario.
Residual Graph Attention Network and Expression-Respect Data Augmentation Aided Visual Grounding

Visual grounding aims to localize a target object in an image based on a given text description. Due to the innate complexity of language, it is still a challenging problem to perform reasoning of complex expressions and to infer the underlying relationship between the expression and the object in an image. To address these issues, we propose a residual graph attention network for visual grounding. The proposed approach first builds an expression-guided relation graph and then performs multi-step reasoning followed by matching the target object. It allows performing better visual grounding with complex expressions by using deeper layers than other graph network approaches. Moreover, to increase the diversity of training data, we perform an expression-respect data augmentation based on copy-paste operations to pairs of source and target images. The proposed approach achieves better performance with extensive experiments than other state-of-the-art graph network-based approaches and demonstrates its effectiveness.
Hyprogan: Breaking the Dimensional wall From Human to Anime

Image translation from human faces to anime ones brings a low-end, efficient way to create animation characters for animation industry. However, due to the significant inter-domain difference between anime images and human photos, existing image-to-image translation approaches cannot address this task well. To solve this dilemma, we propose HyProGAN, an exemplar-guided image-to-image translation model without paired data. The key contribution of HyPro-GAN is that it introduces a novel hybrid and progressive training strategy that expands the unidirectional translation between two domains into the bidirectional intra-domain and inter-domain translation. To enhance the consistency between input and output, we further propose a local masking loss to align the facial features between the human face and the generated anime face. Extensive experiments demonstrate the superiority of HyProGAN against state-of-the-art models.
Video Coding for Machines: Large-Scale Evaluation of Deep Neural Networks Robustness to Compression Artifacts for Semantic Segmentation

In the Video Coding for Machines (VCM) context where visual content is compressed before being transmitted to a vision task algorithm, appropriate trade-off between the compression level and the vision task performance must be chosen. In this paper, a Deep Neural Networks (DNN) based semantic segmentation algorithm robustness to compression artifacts is evaluated with a total of 1486 different coding configurations. Results indicate the importance of using an appropriate image resolution to overcome the block-partitioning limitations in existing compression algorithms, allowing 58.3%, 49.8%, 33.5% and 24.3% bitrate savings at equivalent prediction accuracy for JPEG, JM, x265 and VVenC, respectively. Surprisingly, JPEG can achieve 73.41% bitrate reduction with the inclusion of compressed images at training time over VVC Test Model (VTM) with a DNN trained on pristine data, which implies that DNN generalization ability must not be overlooked.
CONSTRUCTIVE CONFLICTS AND ORGANIZATIONAL-LEARNING IN SMALL AND MEDIUM FIRMS IN KOGI STATE

This study focuses on constructive conflicts and organizational learning within SMEs in Kogi State. It examines the effects of trust and credibility, teamwork, performance improvement and common goals on constructive conflicts within SMEs in Kogi State; and ascertained the relationship between gender diversity and constructive conflicts within SMEs in Kogi State. The study uses a survey research approach and multi-stage sampling was adopted, the sample size of 367 was selected. Data were analyzed using both descriptive statistics, Stepwise Multiple Regression (SMR) and Pearson Correlation Matrix. Findings show that trust and credibility, teamwork, performance improvement and common goals have strong and significant effects on constructive conflicts within SMEs in Kogi State. Further, it shows that gender diversity has a significant relationship with constructive conflicts within SMEs in Kogi State. The study concluded that manageable amount of conflict is productive and recommended that SME owners and managers should be committed to long-term constructive conflict approaches, and should pay attention to gender diversity as a critical group contingency.
AAN+: Generalized Average Attention Network for Accelerating Neural Transformer

Transformer benefits from the high parallelization of attention networks in fast training, but it still suffers from slow decoding partially due to the linear dependency O(m) of the decoder self-attention on previous target words at inference. In this paper, we propose a generalized average attention network (AAN+) aiming at speeding up decoding by reducing the dependency from O(m) to O(1). We find that the learned self-attention weights in the decoder follow some patterns which can be approximated via a dynamic structure. Based on this insight, we develop AAN+, extending our previously proposed average attention (Zhang et al., 2018a, AAN) to support more general position- and content-based attention patterns. AAN+ only requires to maintain a small constant number of hidden states during decoding, ensuring its O(1) dependency. We apply AAN+ as a drop-in replacement of the decoder selfattention and conduct experiments on machine translation (with diverse language pairs), table-to-text generation and document summarization. With masking tricks and dynamic programming, AAN+ enables Transformer to decode sentences around 20% faster without largely compromising in the training speed and the generation performance. Our results further reveal the importance of the localness (neighboring words) in AAN+ and its capability in modeling long-range dependency.
Virtual Job Fair Information System Design Based on Augmented Reality/Virtual Reality

Organizing a job fair is one of the steps to bridge the needs of companies in finding workers and the needs of job seekers to find work. Creative Media State Polytechnic (Polimedia) as a vocational college provides support to alumni to get decent jobs and according to their fields of expertise. In accordance with one of the Main Performance Indicators (KPI) of higher education, namely graduates get decent jobs [1]. However, currently Polimedia does not have a system that supports the implementation of the job fair or is still done manually. Research with the theme of developing virtual job fair information systems based on augmented reality and virtual reality has two objectives. The first objective is to develop an information system that assists the process of posting job vacancies and allows users to register for available vacancies. Then the second is to facilitate virtual job fairs. Because apart from the impact of the COVID-19 pandemic, holding a virtual job fair can be a distinct advantage for alumni who are looking for work because they can significantly save transportation and accommodation costs. The system development methodology used is the System Development Lyfe Cycle (SDLC) with the waterfall method. The waterfall method is divided into five stages, namely the analysis, design, implementation, testing and support stages. The design phase uses the Unified Modeling Language (UML), namely use case diagrams and entity relationship diagrams, while the implementation phase of augmented reality uses 3D modeling, markers and interface adjustments. In addition, the design of augmented reality and virtual reality applications uses the A-FRAME framework.
Towards solving NLP tasks with optimal transport loss

Loss functions are essential to computing the divergence of a model’s predicted distribution from the ground truth. Such functions play a vital role in machine learning algorithms as they steer the learning process. Most common loss functions in natural language processing (NLP), such as Kullback–Leibler (KL) and Jensen–Shannon (JS) divergences, do not base their computations on the properties of label coordinates. Label coordinates can help encode the inter-label relationships. For the sentiment classification task, strongly positive sentiment is closer to positive than strongly negative sentiment. Incorporating such information in the computations of the probability divergence can facilitate the model’s learning dynamics. In this work, we study an under-explored loss function in NLP — Wasserstein Optimal Transport (OT) — which takes label coordinates into account and thus allows the learning algorithm to incorporate inter-label relations. However, the limited applications of OT-based loss owe to the challenges in defining quality label coordinates. We explore the current limitations of learning with OT and provide an algorithm that jointly learns label coordinates with the model parameters. We show the efficacy of OT on several text classification tasks such as sentiment analysis and emotion recognition in conversation. We also discuss the limitations of the approach. The source codes pertaining to this work are publicly available at: https://github.com/declare-lab/NLP-OT.
Virtual Reality in Education: A Comparative Social Media Data and Sentiment Analysis Study

It is essential to consider the public’s viewpoints when it comes to significant issues, such as the adoption and integration of technologies in education. This study aims at analyzing and comprehending the public’s perspectives, sentiments and attitudes towards the use of virtual reality in general and in educational settings. After setting the necessary data requirements, 10,457,344 related tweets from Twitter were identified and retrieved. The data was then analyzed using text mining and sentiment analysis. Based on the results, the public positively perceived the use of virtual reality and mostly expressed emotions of anticipation, trust and joy when referring to its use in education. Finally, the role of virtual reality as an effective educational tool that can enhance students’ engagement, motivation and academic performance was highlighted.
Model-Free Adaptive Control Based on Fractional Input-Output Data Model

Memory properties of fractional-order operators are considered for an input-output data model for highly uncertain nonlinear systems. The model arises by relating the fractional-order variation of the output to the fractional-order variation of the input; the instantaneous gain is computed through a fuzzy inference network, whose output consequences are adapted online on a gradient descent rule. The fractional-order nature of the proposed model relaxes the stringent conditions on data-driven schemes, allowing instantaneous changes in the output signal with a null variation in the controller. The main contribution consists of taking advantage of the memory properties of fractional-order operators and the flexibility of fuzzy logic rules to construct a data-driven model for highly uncertain discrete-time nonlinear systems. The relevance of the proposed method is assessed through experiments in a real-world scenario.
Generalization Performance of Quantum Metric Learning Classifiers

Quantum computing holds great promise for a number of fields including biology and medicine. A major application in which quantum computers could yield advantage is machine learning, especially kernel-based approaches. A recent method termed quantum metric learning, in which a quantum embedding which maximally separates data into classes is learned, was able to perfectly separate ant and bee image training data. The separation is achieved with an intrinsically quantum objective function and the overall approach was shown to work naturally as a hybrid classical-quantum computation enabling embedding of high dimensional feature data into a small number of qubits. However, the ability of the trained classifier to predict test sample data was never assessed. We assessed the performance of quantum metric learning on test ants and bees image data as well as breast cancer clinical data. We applied the original approach as well as variants in which we performed principal component analysis (PCA) on the feature data to reduce its dimensionality for quantum embedding, thereby limiting the number of model parameters. If the degree of dimensionality reduction was limited and the number of model parameters was constrained to be far less than the number of training samples, we found that quantum metric learning was able to accurately classify test data.
Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

The problem of improving the methodological and algorithmic support of the complexing process by developing models of adaptive redundant complexing of weighted interval data has been solved. The object of this study is the process of complexing interval data obtained from several independent sources; the subject is the algebraic methods of excessive complexing of weighted interval data. The relevance of the task is due to the severity of the problem of consolidating homogeneous data in order to obtain more accurate and relevant information about the object or process under study. Models have been developed that, unlike the existing ones, make it possible a posteriori to take into account the accuracy of experts at the preliminary stage of expert evaluation. A single analytical form of the model for processing weighted interval and point estimates with the possibility of structural and parametric tuning is proposed. It allows one to increase the degree of automation of processing expert assessments under conditions of interval uncertainty. Recommendations for the practical application of the proposed models have been formulated. Options for parametric configuration of preference functions were indicated depending on the characteristics of weighted interval estimates. The commonality of the limiting cases of the proposed models with previously known ones is proved. The example shows the shift of the integrated assessment to the side of more accurate assessments at the previous stage of source assessment. The adaptability of the proposed models is illustrated. At the same time, a slight, on average, about 10 %, expansion of the complexed interval relative to the primary ones was registered. The built models and algorithms can be used in automated expert systems, as well as in cascade models of information processing and compression.
Machine Learning Model for the Prediction of Hemorrhage in Intensive Care Units

Objectives: Early hemorrhage detection in intensive care units (ICUs) enables timely intervention and reduces the risk of irreversible outcomes. In this study, we aimed to develop a machine learning model to predict hemorrhage by learning the patterns of continuously changing, real-world clinical data.Methods: We used the Medical Information Mart for Intensive Care databases (MIMIC-III and MIMIC-IV). A recurrent neural network was used to predict severe hemorrhage in the ICU. We developed three machine learning models with an increasing number of input features and levels of complexity: model 1 (11 features), model 2 (18 features), and model 3 (27 features). MIMIC-III was used for model training, and MIMIC-IV was split for internal validation. Using the model with the highest performance, external verification was performed using data from a subgroup extracted from the eICU Collaborative Research Database.Results: We included 5,670 ICU admissions, with 3,150 in the training set and 2,520 in the internal test set. A positive correlation was found between model complexity and performance. As a measure of performance, three models developed with an increasing number of features showed area under the receiver operating characteristic (AUROC) curve values of 0.61–0.94 according to the range of input data. In the subgroup extracted from the eICU database for external validation, an AUROC value of 0.74 was observed.Conclusions: Machine learning models that rely on real clinical data can be used to predict patients at high risk of bleeding in the ICU.
Application of The Naïve Bayes Classifier Algorithm to Classify Community Complaints

Unsatisfactory public services encourage the public to submit complaints/ reports to public service providers to improve their services. However, each complaint/ report submitted varies. Therefore, the first step of the community complaint resolution process is to classify every incoming community complaint. The Ombudsman of The Republic of Indonesia annually receives a minimum of 10,000 complaints with an average of 300-500 reports per province per year, classifies complaints/ community reports to divide them into three classes, namely simple reports, medium reports, and heavy reports. The classification process is carried out using a weight assessment of each complaint/ report using 5 (five) attributes. It becomes a big job if done manually. This impacts the inefficiency of the performance time of complaint management officers. As an alternative solution, in this study, a machine learning method with the Naïve Bayes Classifier algorithm was applied to facilitate the process of automatically classifying complaints/ community reports to be more effective and efficient. The results showed that the classification of complaints/ community reports by applying the Naïve Bayes Classifier algorithm gives a high accuracy value of 92%. In addition, the average precision, recall, and f1-score values, respectively, are 91%, 93%, and 92%.&#x0D;
Automatic Modulation Classification: Convolutional Deep Learning Neural Networks Approaches

Automatic modulation classification (AMC), which plays critical roles in both civilian and military applications, is investigated in this paper through a deep learning approach. A lot of research has been done on feature-based (FB) AM algorithms in particular. Convolutional neural networks (CNN)-based robust AMC approach is developed in this paper to address the difficulty that current FB AMC methods are often intended for a limited set of modulation and lack of generalisation capacity. In total, 11 different modulation types are taken into consideration. Conventional AMCs can be categorized into maximum likelihood (ML)-based (ML-AMC) and feature-based AMC. This paper proposes a robust Convolutional neural network (CNN)-based automatic modulation classification (AMC) technique. The suggested technique can classify the received signals without feature extraction, and it can learn the features from them automatically. A comparison study was done for the proposed CNN-based AMCs with two different optimizers at two different signal-to-noise ratios to select the best one of them based on the performance.
OTNet: A Small Object Detection Algorithm for Video Inspired by Avian Visual System

Small object detection is one of the most challenging and non-negligible fields in computer vision. Inspired by the location–focus–identification process of the avian visual system, we present our location-focused small-object-detection algorithm for video or image sequence, OTNet. The model contains three modules corresponding to the forms of saliency, which drive the strongest response of OT to calculate the saliency map. The three modules are responsible for temporal–spatial feature extraction, spatial feature extraction and memory matching, respectively. We tested our model on the AU-AIR dataset and achieved up to 97.95% recall rate, 85.73% precision rate and 89.94 F1 score with a lower computational complexity. Our model is also able to work as a plugin module for other object detection models to improve their performance in bird-view images, especially for detecting smaller objects. We managed to improve the detection performance by up to 40.01%. The results show that our model performs well on the common metrics on detection, while simulating visual information processing for object localization of the avian brain.
Security Evaluation of Side Channel Attacks on CPS Architectures

"Smart-Embedded Systems," or Cyber-Physical Systems (CPS), bring together physical, computational, and network resources into a single integrated system. These resources are put to work to provide a solid basis for a better quality of life and a more upscale standard of living. CPSs (Cyber-Physical Systems) are vital in supporting the need for smart products (e.g., airports, homes, cities, hospitals). Control the three sorts of resources Doi: physical, cognitive, and network. In order to uncover the essential knowledge in the various real-life segments, e.g., distribution, power transmission and telecommunication, CPSs stimulates human-to-human interaction via this regulatory measure Information security is a critical concern in today's technological world. Due to the complexity of the components and middleware in CPS, it is difficult to keep them safe from cyberattacks while yet allowing them to operate quickly and effectively. An in-depth explanation of CPS, their issues (including cyber threats), characteristics, and associated technology is provided in this paper Aside from security and performance, we also describe the prevalent Side Channel Attacks (SCA) e.g., Differential Power Analysis (DPA) and Simple Power Analysis (SPA), on cryptographic techniques (such as RSA and AES) and the countermeasures that may be used to protect against these attacks.