Reviewerly/exHarmony huggingface dataset:
Qualitative Analysis of Synthetic Computer Network Data Using UMAP

Evaluating the output of Generative Adversarial Networks (GANs) trained on computer network traffic data is difficult. In this paper, we introduced a strategy using fixed low-dimensional UMAP embeddings of network traffic to compare source and synthetic network traffic qualitatively. We found that UMAP embeddings gave a natural way to evaluate the quality of generated data and infer the fitness of the generating model’s hyperparameters. Further, this evaluation matches with quantitative strategies such as GvR. This strategy adds to the toolbox for evaluating generative methods for network traffic and could be generalized to other tabular data sources which are not easily evaluated.
Fine-Tuning MobileNet for Breast Cancer Diagnosis

Breast cancer can be considered one of the significant causes of death, especially among women worldwide. Therefore, it is essential to detect and diagnose breast cancer as early as possible to reduce the adverse effects on patients and protect women’s health. This study proposes a model applying transfer learning and fine-tuning to classify and detect benign, malignant breast cancer, and normal breast. We train the proposed model with transfer learning from the pre-trained MobileNet model to identify breast cancers and optimize the prediction results. The dataset contains 780 ultrasound images categorized into three classes which are benign breast cancer (437 images), malignant breast cancer (210 images), and normal breast (133 images). The experimental results show that applying the transfer learning and fine-tuning technique from the MobileNet model achieves promising results, with the accuracy and F1-score being 0.9651–0.9648, 0.9412–0.9417, and 0.9060–0.9085, respectively, with three scenarios.
Accelerating NLP for Technologically Underserved Languages: A Corpus for Moroccan Dialect

This paper describes our approach to define a dataset to accelerate the development of Natural Language Processing (NLP) techniques for dialects spoken by technologically underserved populations. We designed and implemented a multimodal solution to collect data, mapping and annotating both the textual representation and the voice recording of utterances targeting specific samples of the native speakers. Our project is inspired from the Moroccan strategy, implemented through Law 55.19, which aims to simplify administrative procedures and formalities in order to strengthen the degree of trust between the public administration and users (citizens). Moreover, this paper describes the format that we defined for these datasets and the results of our experimentation with the Moroccan dialect. The availability of this dataset will be helpful to researchers due to the lack of Moroccan dialect datasets for NLP fields.
Exploring the Role of Mobile Technologies in Higher Education: The Impact of Online Teaching on Traditional Learning

The chapter aims to explore the role of mobile technologies in higher education especially the impact of online teaching on traditional learning. The transformation of the educational setting from online to offline draws limited attention from researchers in the post-pandemic era. The key reason for conducting this chapter is to explore the hidden issues of student coping strategies in the offline learning environment. In addition, the chapter explores the opportunities and limitations of technology usage in higher education. The study utilized a qualitative research approach to conduct the chapter with an extensive literature review. The result shows that with the advanced usage of mobile technology, the academic resources are freely available and accessible to all the learners that can ensure effective teaching and learning, however, the study is conducted among a limited number of respondents in a single country. This may affect the generalization of the study.
Using Outlier Modification Rule for Improvement of the Performance of Classification Algorithms in the Case of Financial Data

This study aims to improve the performance of Data Analytics (DA) algorithms by mining outliers from credit card fraud detection datasets. In doing so, we analyze the performance of data analytics algorithms, such as Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Naïve Bayes (NB) and Support Vector Machine (SVM), by comparing the original and modified datasets in the absence and presence of outliers. To generate modified dataset, this chapter proposes an outlier mining method based on Median (MED) and Median Absolute Deviation (MAD). Performance measures such as accuracy, sensitivity, specificity, detection rate, misclassification error rate, AUC, and pAUC evaluate the performance of the DA algorithms. Empirical findings show that the performance of the DA algorithms on modified dataset shows better results than the original data for both simulated dataset and real-life credit card datasets. This study offers new insights into financial decision makers and stakeholders in the credit card industry.
Sentiment Analysis in Drug Reviews Based on Improved Pre-trained Word Embeddings

Sentiment analysis (SA) is the process of ranking documents based on their polarity (positive, negative, or neutral). It has a wide range of applications, including opinion mining, health informatics, and recommender systems. In the pharmaceutical industry, automatic analysis of people’s online opinions can be used to evaluate a large number of user opinions and extract useful information regarding drug efficacy and risk factors, which can be used to improve pharmacovigilance systems. Approaches for sentiment analysis have evolved over time from simple rules to powerful machine learning techniques such as deep learning, which has emerged as an emerging technology in several natural language processing (NLP) applications. Sentiment analysis is aware of this accomplishment, and various deep learning-based systems have subsequently demonstrated their effectiveness over previous approaches, achieving peak scores on typical sentiment analysis datasets. As transformers have revolutionized the field of NLP, achieving peak scores for different NLP tasks, we propose to combine a pre-trained model called BERT (Bidirectional Encoder Representations from Transformers) with static embedding model, which has not been studied before. The obtained vectors were used as input features for a Bidirectional Gated Recurrent Unit (BiGRU). The experimental results reveal that the proposed model outperforms the state of the art in sentiment analysis of drug reviews.
Nyon: A Ubiquitous Fall Detection Device for Elders

Falls are one of the main causes of mortality and morbidity in the elderly worldwide. This had let to the research and development of electronic fall-detection systems. We propose a complete fall-detection system, that combines a wearable device (called Nyon) and a message microservice (for email and SMS) to alert caregiver every time a fall occurs. The wearable uses a simple threshold method and has the capability of search and switch between Wi-Fi and Bluetooth, using the available communication technology when a fall occurs. The results have shown that the wearable autonomy is adequate for a daily use and the server microservices are reliable and deliver a message to the caregiver every time a fall alert occurs. Several improvements are planned to increase the autonomy and range of the wearable device.
gScoreCAM: What Objects Is CLIP Looking At?

Large-scale, multimodal models trained on web data such as OpenAI's CLIP are becoming the foundation of many applications. Yet, they are also more complex to understand, test, and therefore align with human values. In this paper, we propose gScoreCAM—a state-of-the-art method for visualizing the main objects that CLIP is looking at in an image. On zero-shot object detection, gScoreCAM performs similarly to ScoreCAM, the best prior art on CLIP, yet 8 to 10 times faster. Our method outperforms other existing, well-known methods (HilaCAM, RISE, and the entire CAM family) by a large margin, especially in multi-object scenes. gScoreCAM sub-samples $$k = 300$$ channels (from 3,072 channels—i.e. reducing complexity by almost 10 times) of the highest gradients and linearly combines them into a final "attention" visualization. We demonstrate the utility and superiority of our method on three datasets: ImageNet, COCO, and PartImageNet. Our work opens up interesting future directions in understanding and de-biasing CLIP.
3D Pose Based Feedback for Physical Exercises

Unsupervised self-rehabilitation exercises and physical training can cause serious injuries if performed incorrectly. We introduce a learning-based framework that identifies the mistakes made by a user and proposes corrective measures for easier and safer individual training.
From Sparse to Dense: Semantic Graph Evolutionary Hashing for Unsupervised Cross-Modal Retrieval

In recent years, cross-modal hashing has attracted an increasing attention due to its fast retrieval speed and low storage requirements. However, labeled datasets are limited in real application, and existing unsupervised cross-modal hashing algorithms usually employ heuristic geometric prior as semantics, which introduces serious deviations as the similarity score from original features cannot reasonably represent the relationships among instances. In this paper, we study the unsupervised deep cross-modal hash retrieval method and propose a novel Semantic Graph Evolutionary Hashing (SGEH) to solve the above problem. The key novelty of SGEH is its evolutionary affinity graph construction method. To be concrete, we explore the sparse similarity graph with clustering results, which evolve from fusing the affinity information from code-driven graph on intrinsic data and subsequently extends to dense hybrid semantic graph which restricts the process of hash code learning to learn more discriminative results. Moreover, the batch-inputs are chosen from edge set rather than vertexes for better exploring the original spatial information in the sparse graph. Experiments on four benchmark datasets demonstrate the superiority of our framework over the state-of-the-art unsupervised cross-modal retrieval methods. Code is available at: https://github.com/theusernamealreadyexists/SGEH .
Exposing Face Forgery Clues via Retinex-Based Image Enhancement

Public concerns about deepfake face forgery are continually rising in recent years. Existing deepfake detection approaches typically use convolutional neural networks (CNNs) to mine subtle artifacts under high-quality forged faces. However, most CNN-based deepfake detectors tend to over-fit the content-specific color textures, and thus fail to generalize across different data sources, forgery methods, and/or post-processing operations. It motivates us to develop a method to expose the subtle forgery clues in RGB space. Herein, we propose to utilize multi-scale retinex-based enhancement of RGB space and compose a novel modality, named MSR, to complementary capture the forgery traces. To take full advantage of the MSR information, we propose a two-stream network combined with salience-guided attention and feature re-weighted interaction modules. The salience-guided attention module guides the RGB feature extractor to concentrate more on forgery traces from an MSR perspective. The feature re-weighted interaction module implicitly learns the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive experiments on several benchmarks show that our method outperforms the state-of-the-art face forgery detection methods in detecting severely compressed deepfakes. Besides, our method also shows superior performances on cross-datasets evaluation.
SCOAD: Single-Frame Click Supervision for Online Action Detection

Online action detection based on supervised learning requires heavy manual annotation, which is difficult to obtain and may be impractical in real applications. Weakly supervised online action detection (WOAD) can effectively mitigate the problem of substantial labeling costs by using video-level labels. In this paper, we revisit WOAD and propose a weakly supervised online action detection using click-level labels for training, named Single-frame Click Supervision for Online Action Detection (SCOAD). Comparatively, click-level labels can effectively improve prediction accuracy by carrying a small amount of temporal information without massively increase the difficulty and cost of annotation. Specifically, SCOAD includes two joint training modules, i.e., Action Instance Miner (AIM) and Online Action Detector (OAD). To provide more guidance for training network as accuracy as possible, AIM mines pseudo-action instances under the supervision of click labels. Meanwhile, we generate video similarity instances offline by the similarity between video frames and use it to perform finer granularity filtering of error instances generated by AIM. OAD is trained jointly with AIM for online action detection by the pseudo frame-level labels converted from the filtered pseudo-action instances. We conduct extensive experiments on two benchmark datasets to demonstrate that SCOAD can effectively mine and utilize the small amount of temporal information in click-level labels. Code is available at https://github.com/zstarN70/SCOAD.git .
Two-Stage Multimodality Fusion for High-Performance Text-Based Visual Question Answering

Text-based visual question answering (TextVQA) is to answer a text-related question by reading texts in a given image, which needs to jointly reason over three modalities—question, visual objects and scene texts in images. Most existing works leverage graph or sophisticated attention mechanisms to enhance the interaction between scene texts and visual objects. In this paper, observing that compared with visual objects, the question and scene text modalities are more important in TextVQA while both layouts and visual appearances of scene texts are also useful, we propose a two-stage multimodality fusion based method for high-performance TextVQA, which first semantically combines the question and OCR tokens to understand texts better and then integrates the combined results into visual features as additional information. Furthermore, to alleviate the redundancy and noise in the recognized scene texts, we develop a denoising module with contrastive loss to make our model focus on the relevant texts and thus obtain more robust features. Experiments on the TextVQA and ST-VQA datasets show that our method achieves competitive performance without any large-scale pre-training used in recent works, and outperforms the state-of-the-art methods after being pre-trained.
Task Scheduling and Energy-Aware Workflow in the Cloud Through Hybrid Optimization Techniques

Task Scheduling is very complex in terms of resource utilization for the workload distributed on Virtual Machines. It is a very typical issue for the task of scheduling distributed on virtual machines, which is the most specific problem in cloud computing. Without scheduling, load balancing is not proper for balancing distributed on a virtual machine because maximum makes the unbalancing. So, it will be indispensable for which the reduced response time, makespan time with scheduling. The first experiment aims to compare traditional Ant Colony Optimization algorithms (ACO), the second experiment to solve the maximum global optimization problems by Genetic Algorithms (GA), and the third experimental series the performance of Particle Swarm Optimization algorithms (PSO) for increasing the resources’ utilization for synthetically and actual trace data, respectively. Furthermore, the result shows simulation by clouds in this paper and problems identified by NIA algorithms.
Assessing the Impact of Artificial Intelligence in e-Commerce Portal: A Comparative Study of Amazon and Flipkart

This era is a technologically advanced civilization. The day when everything had to be done manually is long gone. Today, technology plays a significant part in our everyday lives. Artificial Intelligence is one of the technologies (AI). It becomes a part of daily life and alters how individuals do their business. In certain cases, we even fail to recognize that we are employing AI. It may be observed in the form of wearable technology, self-driving automobiles, smartphone applications, and home automation gadgets. Everything that is a part of it is transformed. The most cutting-edge technology in use today is Artificial Intelligence. The global business landscape has also been revolutionized by the e-commerce sector. Additionally, India is one of the e-commerce marketplaces with one of the fastest rates of growth, and in the years to come, it might become a major draw for AI-based technologies. AI is also used in e-commerce, as is evident. In the world of e-commerce, AI is essential. AI will bring about a significant technical shift for the e-commerce sector. In the last ten years, the use of AI in the e-commerce sector has dramatically risen. AI is being used by the e-commerce sector to process a vast database of progressing consumers, interact with thinking chatbots, and assist with searching, categorizing, and selecting the right product. AI is more effective and accurate, and it allows for the large-scale collection, processing, and inference of data. Competitors in the e-commerce space are utilizing AI to develop customer-centric strategies, retarget potential consumers, develop more efficient sales processes, voice-powered search, increase customer recommendations, address bogus reviews, etc. With particular reference to Amazon.com and Flipkart.com, this research study will precisely explain what Artificial Intelligence (AI) is, how it is employed in the e-commerce sector, and how it will be useful in examining how these two competitors are employing AI.
Scaling Identifiers and their Metadata to Gigascale: An Architecture to Tackle the Challenges of Volume and Variety

Persistent identifiers are applied to an ever-increasing variety of research objects, including software, samples, models, people, instruments, grants, and projects, and there is a growing need to apply identifiers at a finer and finer granularity. Unfortunately, the systems developed over two decades ago to manage identifiers and the metadata describing the identified objects no longer scale. Communities working with physical samples have grappled with these three challenges of the increasing volume, variety, and variability of identified objects for many years. To address this dual challenge, the IGSN 2040 project explored how metadata and catalogues for physical samples could be shared at the scale of billions of samples across an ever-growing variety of users and disciplines. In this paper, we focus on how we scale identifiers and their describing metadata to billions of objects and who the actors involved with this system are. Our analysis of these requirements resulted in the definition of a minimum viable product and the design of an architecture that not only addresses the challenges of increasing volume and variety but, more importantly, is easy to implement because it reuses commonly used Web components. Our solution is based on a Web architectural model that utilises <a href="http://Schema.org">Schema.org</a>, JSON-LD, and sitemaps. Applying these commonly used architectural patterns on the internet allows us to not only handle increasing variety but also enable better compliance with the FAIR Guiding Principles.
Modeling of Joint Motion Planning of Group of Mobile Robots and Unmanned Aerial Vehicle

This paper studies the process of forming an algorithm for the actions of a group of mobile robots, which allows to effectively carry out a landing of an unmanned aerial vehicle on their formation in a given area. Building a communication field to transmit data from the drones to a group of robots is investigated, the planning of the local trajectory of the robots is described, and obstacles are taken into account. And the formation of robots taking into account obstacles and the creation of a formation of robots is considered. As a result of the review of current planning methods and their comparative analysis, the method that most effectively copes with similar problems is determined—the method of tangential avoidance. Authors of the paper adapt this method to the conditions of a problem to be solved and offer its modified version as a new method. To confirm the performance of the method, a series of experiments is conducted, which show that the developed method can successfully cope with the formation of trajectories of mobile robots at randomly determined locations of obstacles and robots.
Research on Statistics and Conversion Method of Resistive Leakage Current Live Detection Results of Zinc Oxide Arrester

The resistance leakage current detection of zinc oxide arrester is an important means to obtain the status data of the arrester. Affected by factors such as the manufacturer, model, type of testing instrument, temperature, humidity and other factors of the zinc oxide arrester, the distribution of resistive leakage current has obvious dispersion, which affects the accuracy of the evaluation results. Therefore, this paper statistically analyzes the relationship between the resistive current of 110kV zinc oxide arrester and the ambient temperature of 8°C~30°C, humidity of 45%~80%. The results show that there is a linear positive correlation between resistive current and temperature and humidity. Based on this, a conversion method for on-site testing data is proposed in this paper. Converted to uniform environmental conditions, the influence of environmental factors is reduced to a certain extent, which helps to improve the accuracy of the status evaluation results of zinc oxide arrester.
Design of Fruit and Vegetable Produce Traceability System Based on Dual Chain and Dual Storage Blockchain

The "double chain and double storage" blockchain model for the fruit and vegetable supply chain was developed to address the ongoing issues with the agricultural quality control system. The "main chain + privacy chain" double chain model is used to limit storage growth, and the "database + block chain" method of storing on-chain and off-chain traceability data is intended to increase the blockchain's query efficiency. Based on Hyperledger Fabric. Designed a fruit and vegetable produce traceability system based on Hyperledger Fabric. The system was constructed using smart contract technology to filter and restrict the data upload. The experimental comparison analysis of the model is also conducted. The findings demonstrate that the dual-chain dual-storage model based on Hyperledger, which takes into account the openness, security, and information privacy of each link enterprise of the transaction information, can significantly increase the blockchain storage query efficiency.