Reviewerly/exHarmony huggingface dataset:
Using Machine Learning Based CNN Architectural Models for Breast Ductal Carcinoma Recognition

The ductal carcinoma has been represented as a diverse illness with several subgroups. Early recognition of a ductal carcinoma kind is critical in determining the patient's course of medical therapy. In the bioinformatics and biomedical, several research projects are being focused on the identification of the ductal carcinomatous cells as malignant benign tumors. In recent years for ductal carcinoma diagnosis, various machines learning (ML) approaches have been employed by the various researchers for the prediction of recurrence and survival of the ductal carcinomatous patients. Furthermore, by identifying essential highlights, ML tools may be used for the determination of the ductal carcinomatous causing malignant cells and tumors based on their relevance complicated data set classification in the mastography. Classification of masses in mammograms remains a significant problem, yet it is critical in assisting radiologists in making correct diagnoses. In this study, we present a deep learning methodology called Convolution Neural Networks (CNN) based categorization. CNN architectural models like as Mobile Net and Inception are used to classify mammography pictures as normal or abnormal.
AirCode: A Robust Object Encoding Method

Object encoding and identification are crucial for many robotic tasks such as autonomous exploration and semantic relocalization. Existing works heavily rely on the tracking of detected objects but have difficulty recalling revisited objects precisely. In this letter, we propose a novel object encoding method, which is named as AirCode, based on a graph of key-points. To be robust to the number of key-points detected, we propose a feature sparse encoding and object dense encoding method to ensure that each key-point can only affect a small part of the object descriptors, leading it to be robust to viewpoint changes, scaling, occlusion, and even object deformation. In the experiments, we show that it achieves superior performance for object identification than the state-of-the-art algorithms and is able to provide reliable semantic relocalization. It is a plug-and-play module and we expect that it will play an important role in various applications.
Machine Learning Based Movie Recommendation System

Recommendation systems plays are an important aspect nowadays due to its wide range of applications. It is widely used by many Companies to deliver better user experience and achieve growth by using it in a right manner. This paper presents information about methodologies and techniques used in recommendation systems. Recommendation systems are classified into 3 distinct types: Collaborative Filtering approach, Content based approach and hybrid approach. In this paper we will represent content based approach, its advantages, and disadvantages and how to implement this technique to recommend movies based on this idea .The paper will also elaborate all other approaches and their techniques with their limitations. We have used content based filtering approach but it could be some other approach too based on the business demand or user experience. So there is further scope for research in this topic.
Scalable Privacy in Multi-Task Image Compression

Learning-based compression systems have shown great potential for multi-task inference from their latent-space representation of the input image. In such systems, the decoder is supposed to be able to perform various analyses of the input image, such as object detection or segmentation, besides decoding the image. At the same time, privacy concerns around visual ana-lytics have grown in response to the increasing capabilities of such systems to reveal private information. In this paper, we propose a method to make latent-space inference more privacy-friendly using mutual information-based criteria. In particular, we show how organizing and compressing the latent representation of the image according to task-specific mutual information can make the model maintain high analytics accuracy while becoming less able to reconstruct the input image and thereby reveal private information.
Cash flow prediction of a bank deposit using scalable graph analysis and machine learning

Cash flow prediction of a bank is an important task as it is not only related to liquidity risk but is also regulated by financial authorities. To improve the prediction, a graph analysis of bank transaction data is promising, while its size, scale-free nature, and various attributes make the task challenging.In this paper, we propose a graph-based machine learning method for the cash flow prediction t ask. Our contributions are as follows. (i) We introduce an extensible and scalable shared-memory parallel graph analysis platform that supports the vertex-centric, bulk synchronous parallel programming paradigm. (ii) We introduce two novel graph features upon the platform: (ii-a) an internal money flow feature based on the Markov process approximation, and (ii-b) an anomaly score feature derived from other graph features.The proposed method is examined with real bank transaction data. The proposed graph features reduce the error of a long-term (31-day) cash flow prediction by 56 % from that of a non-graph-based time-series prediction model. The graph analysis platform can compute graph features from a graph with 10 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> nodes and 593 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> edges in 2 hours 20 minutes.
Automatic Misogyny Detection in Social Media Platforms using Attention-based Bidirectional-LSTM

The important growth of social media and online gaming sites in recent years have increased the challenge of online moderation to keep the internet safe and without toxic content. Today, machine learning techniques play an important role in detecting inappropriate content and help moderate online interaction. Text classification using Natural Language Processing (NLP) methods has been extensively studied using deep learning models and transformers which have shown impressive results. Despite this, specific classification tasks on limited datasets still need to be improved. In this paper, we propose an approach based on an Attention-Based Bidirectional LSTM model and a combination of custom features to enhance automatic misogyny identification (AMI) on social media. We present a multi-lingual study of the phenomena by carrying out different classification experiments. Our study focuses on selecting most important features to improve the model for misogyny detection. The proposed model outperforms many state-of-the-art approaches across multiple datasets.
MC-OCR Challenge 2021: Towards Document Understanding for Unconstrained Mobile-Captured Vietnamese Receipts

The Mobile capture receipts Optical Character Recognition (MC-OCR) [14] challenge deliver two tasks: Receipt Image Quality Evaluation and Key Information Extraction. In the first task, we introduce a regression model to map various inputs, for instance the probability of the output OCR, cropped text boxes, images to actual label. In the second task, we propose a stacked multi-model as a solution to solve this problem. The robust models are incorporated by image segmentation, image classification, text detection, text recognition, and text classification. Follow this solution, we can get vital tackle various noise receipt types: horizontal, skew, and blur receipt.
DeepEmotex: Classifying Emotion in Text Messages using Deep Transfer Learning

Transfer learning has been widely used in natural language processing through deep pretrained language models, such as Bidirectional Encoder Representations from Transformers and Universal Sentence Encoder. Despite the great success, language models get overfitted when applied to small datasets and are prone to forgetting when fine-tuned with a classifier. To remedy this problem of forgetting in transferring deep pretrained language models from one domain to another domain, existing efforts explore fine-tuning methods to forget less. We propose DeepEmotex an effective sequential transfer learning method to detect emotion in text. To avoid forgetting problem, the fine-tuning step is instrumented by a large amount of emotion-labeled data collected from Twitter.We conduct an experimental study using both curated Twitter data sets and benchmark data sets. DeepEmotex models achieve over 91% accuracy for multi-class emotion classification on test dataset. We evaluate the performance of the fine-tuned Deep-Emotex models in classifying emotion in EmoInt and Stimulus benchmark datasets. The models correctly classify emotion in 73% of the instances in the benchmark datasets. The proposed DeepEmotex-BERT model outperforms Bi-LSTM result on the benchmark datasets by 23%. We also study the effect of the size of the fine-tuning dataset on the accuracy of our models. Our evaluation results show that fine-tuning with a large set of emotion-labeled data improves both the robustness and effectiveness of the resulting target task model.
Mathematical Modeling of Eight-Cable-Driven Parallel Robot

A mathematical model of a parallel eight-cable-driven robot for use in the mobile construction complex is proposed. The dynamic loads on the cable system are studied. Deviations of the mobile platform of the robot are estimated by linear and angular coordinates. A method is proposed for including activation functions in the mathematical model of the cable system, which provides a representation of the structural nonlinearity of the cable-driven robot.
Artificial Intelligence Based Real-Time Attendance System Using Face Recognition

The attendance system is important for school, college, university, office, and factory. In the academic field, attendance is recorded and updated manually in the teacher’s notebook or any other application. In general, these types of systems waste time for both students and teachers. Also, other Job sectors use fingerprint attendance for arrival and departure, but the time between arrival and departure the employee can go outside anytime that he wants. So, this problem we can overcome using the unique feature of every human being on his face. In this proposed system, facial recognition technology is used to automatically take attendance. Sometimes when many people can come together. This system can able to detect multiple faces at a time. In this system, Face detection is done using DLib and ResNet-34 is used for face recognition. Using two cameras arrival and departure times of an individual are calculated sequentially. 96.03% accuracy observed by the camera-1 to recognize the face and at day time when light is off camera-2 shows the best accuracy 96.62%.
A Data-Driven Approach to Improve 3D Head-Pose Estimation

Head-pose estimation from images is an important research topic in computer vision. Its many applications include detecting focus of attention, tracking driver behavior, and human-computer interaction. Recent research on head-pose estimation has focused on developing models based on deep convolutional neural networks (CNNs). These models are trained using transfer-learning and image augmentation to achieve better initiation states and robustness against occlusions. However, methods that use transfer-learning networks are usually aimed at general image recognition and offer no in-depth study of transfer learning from more task-related networks. Additionally, for the head-pose estimation, robustness against heavy occlusion, and noise such as motion blur and low-brightness are vital. In this paper, we propose a new image-augmentation approach that significantly improves the estimation accuracy of the head-pose model. We also propose a task-related weight initialization to further improve the estimation accuracy by studying internal activations of models trained for face-related tasks such as face-recognition. We test our head-pose estimation model on three challenging test sets and achieve better results to state-of-the-art methods.
Passive User Authentication Utilizing Behavioral Biometrics for IIoT Systems

Passive authentication is of great importance for security guarantee in industrial Internet of Things (IIoT) systems. Based on the behavioral biometrics from sequential operation actions in IIoT, this article proposes a nonintrusive and passive authentication framework for continuous user authentication against the impersonation attack. We first provide experimental results to demonstrate the discriminability and stability for the intrinsic features of sequential operation actions, and then leverage the Kalman filtering and wavelet techniques for noise elimination and the singular value decomposition method for the dimensionality reduction of feature space. We further exploit the one-class classification technique to formulate the authentication decision process as a hidden Markov model (HMM). Extensive experiments are conducted to illustrate the authentication performance of the passive authentication framework in terms of the false acceptance rate, false rejection rate, and equal-error rate. We also investigate the related authentication efficiency issues in terms of the usability to the operation-action sequence length, the scalability to the number of features and user space, and the sensitivity to the operation action features.
Reservoir-Based Distributed Machine Learning for Edge Operation of Emitter Identification

This paper has several contributions, all motivated by the operational aspects of in-situ retrainable Specific Emitter Identification (SEI) for authentication of mobile emitters at the Edge, tactical or IoT. The paper first provides a review of the prior work (DLR) that uses our design of reservoir delay loops (DL) to implement low-power, high accuracy and high-reliability classifiers of signals represented as time series of samples, capable of in-situ training at the Edge. We analyze those DLR properties that enable seamless authentication of mobile emitters on a larger scale using radio frequency (RF) fingerprints. Delay loops project the SEI inputs into a space where different input classes are linearly separable, allowing the use of a linear classifier for emitter identification. Moreover, the architecture of split loops enables a more effective linear separation, constraining the number of weight coefficients, which is important for efficient integration of locally trained DLRs into a global SEI model (D-DLR). D-DLR enables mobile edge platforms to authenticate and then track emitters. To authenticate mobile devices across large regions, D-DLR is trained in a distributed fashion with very little additional processing and a small communication cost, all while maintaining accuracy. We illustrate how to merge locally trained DLR SEI classifiers, and how to reliably detect unseen emitters using a simple multi-layer perceptron to which the DLR weights have been transferred.
Voice Liveness Detection for Voice Assistants Through Ear Canal Pressure Monitoring

The voice assistants are important input devices in future smart homes. Thanks to the great performance that is provided by current voice recognition systems, current voice assistants can understand various commands in different language from users and connect with other devices in the same local network to perform corresponding actions. However, the voices are not secure due to its nature. Even if we secure voice assistant using the voiceprint, attackers can still steal the victim's voices and replay them to voice assistants for attacking purpose. In this paper, we propose a new voice liveness detection system that is specifically designed for voice assistants. The key insight behind our system is that users will open their mouth when they say some phonemes. Such opening mouth activities will impact the air pressure in the ear canal if the ear canal is an enclosed space. Therefore, we can detect the liveness of the voices on the side of voice assistants by cauterizing the correlations between each sentence and the air pressure. Experiments with ten volunteers show that our system can accurately accept voice commands from legitimate users with accuracy of 94.8% and 97%. Moreover, our system can effectively defend current voice assistant devices from replay attacks with accuracy of 99.25% and 99.5%.
Detecting Discrimination Risk in Automated Decision-Making Systems with Balance Measures on Input Data

Bias in the data used to train decision-making systems is a relevant socio-technical issue that emerged in recent years, and it still lacks a commonly accepted solution. Indeed, the "bias in-bias out" problem represents one of the most significant risks of discrimination, which encompasses technical fields, as well as ethical and social perspectives. We contribute to the current studies of the issue by proposing a data quality measurement approach combined with risk management, both defined in ISO/IEC standards. For this purpose, we investigate imbalance in a given dataset as a potential risk factor for detecting discrimination in the classification outcome: specifically, we aim to evaluate whether it is possible to identify the risk of bias in a classification output by measuring the level of (im)balance in the input data. We select four balance measures (the Gini, Shannon, Simpson, and Imbalance ratio indexes) and we test their capability to identify discriminatory classification outputs by applying such measures to protected attributes in the training set. The results of this analysis show that the proposed approach is suitable for the goal highlighted above: the balance measures properly detect unfairness of software output, even though the choice of the index has a relevant impact on the detection of discriminatory outcomes, therefore further work is required to test more in-depth the reliability of the balance measures as risk indicators. We believe that our approach for assessing the risk of discrimination should encourage to take more conscious and appropriate actions, as well as to prevent adverse effects caused by the "bias in-bias out" problem.
Dynamic Parallel Pyramid Networks for Scene Recognition

Scene recognition is considered a challenging task of image recognition, mainly due to the presence of multiscale information of global layout and local objects in a given scene. Recent convolutional neural networks (CNNs) that can learn multiscale features have achieved remarkable progress in scene recognition. They have two limitations: 1) the receptive field (RF) size is fixed even though a scene may have large-scale variations and 2) they are computing and memory intensive, partially due to the representation of multiscales. To address these limitations, we propose a lightweight dynamic scene recognition approach based on a novel architectural unit, namely, a dynamic parallel pyramid (DPP) block, that can adaptively select RF size based on multiscale information from the input regarding channel dimensions. We encode multiscale features by applying different convolutional (CONV) kernels on different input tensor channels and then dynamically merge their output using a group attention mechanism followed by channel shuffling to generate the parallel feature pyramid. DPP can be easily incorporated with existing CNNs to develop new deep models, called DPP networks (DPP-Nets). Extensive experiments on large-scale scene image datasets, Places365 standard, Places365 challenge, the Massachusetts Institute of Technology (MIT) Indoor67, and Sun397 confirmed that the proposed method provides significant performance improvement compared with current state-of-the-art (SOTA) approaches. We also verified general applicability from compelling results on lightweight models of MobileNetV2 and ShuffleNetV2 on ImageNet-1k and small object centralized benchmarks on CIFAR-10 and CIFAR-100.
Text Mining and Automated Scoring

Natural Language Processing (NLP) is playing an increasingly important role in learning and assessments. Some typical applications of NLP in education include automated scoring, automated item generation, conversation-based assessments, writing assistants, text mining for education, and so on. In this chapter, we aim at introducing some basics of NLP through two typical applications in educational contexts, text mining and automated scoring. We hope readers can get an overall picture of NLP and get familiarized with some basic tools for handling natural language data, which may serve as stepping stones for their future work with NLP.
Overview of the Basic Principles of Blockchain

In the Internet, the authenticity of data between computer nodes is difficult to be guaranteed. However, as the underlying implementation of bitcoin, blockchain adopts decentralized and distributed ledger to allow false transactions and data tampering, and ultimately provides a reliable and trustworthy solution for bitcoin transactions on the unreliable and untrusted Internet. This paper introduces the basic principles of blockchain from the aspects of the relationship between blockchain and bitcoin, two mathematical algorithms involved in blockchain, the logical structure of blockchain, the types of blockchain, and the application of blockchain. This paper explains the core concepts of blockchain, such as decentralization, trustlessness, mining, proof of work, points out several problems that need to be paid attention to in the practical development of blockchain application, and puts forward the approach of integrating blockchain and traditional database to solve the practical application.
Holodeck: Immersive 3D Displays Using Swarms of Flying Light Specks [Extended Abstract]

Unmanned Aerial Vehicles (UAVs) have moved beyond a platform for hobbyists to enable environmental monitoring, journalism, film industry, search and rescue, package delivery, and entertainment. This paper describes 3D displays using swarms of flying light specks, FLSs. An FLS is a small (hundreds of micrometers in size) UAV with one or more light sources to generate different colors and textures with adjustable brightness. A synchronized swarm of FLSs renders an illumination in a pre-specified 3D volume, an FLS display. An FLS display provides true depth, enabling a user to perceive a scene more completely by analyzing its illumination from different angles.
Mining Method of Voltage Sag Association Rules Based on Multi-sources Monitoring Data

With the development of digital technology, multi-platform monitoring data have changed from processed objects into basic resources. This paper summarizes the rules of voltage sag events caused by lightning and mines the association rules with multi-platform data. These rules improve the safety and reliability of the distribution network However, there is a lack of a method to mine the association rules with lightning and voltage sags. By using data of lightning location system and power quality monitoring system, an association rule mining method, which consists of two main steps, is proposed: 1) establish a database based on multi-platform and build a decision table by discrete attributes, 2) propose the improved AprioriTid algorithm considering nonuniform data to mine rules. A comparative study of multi-platform monitoring data is carried out on the use of the traditional and improved AprioriTid algorithms. And the accuracy of the proposed algorithm is validated by measured data.