Reviewerly/exHarmony huggingface dataset:
ANN softmax

Thanks to the popularity of GPU and the growth of its computational power, more and more deep learning tasks, such as face recognition, image retrieval and word embedding, can take advantage of extreme classification to improve accuracy. However, it remains a big challenge to train a deep model with millions of classes efficiently due to the huge memory and computation consumption in the last layer. By sampling a small set of classes to avoid the total classes calculation, sampling-based approaches have been proved to be an effective solution. But most of them suffer from the following two issues: i) the important classes are ignored or only partly sampled, such as the methods using random sampling scheme or retrieval techniques of low recall (e.g., locality-sensitive hashing), resulting in the degradation of accuracy; ii) inefficient implementation owing to incompatibility with GPU, like selective softmax. It uses hashing forest to help select classes, but the search process is implemented in CPU. To address the above issues, we propose a new sampling-based softmax called ANN Softmax in this paper. Specifically, we employ binary quantization with inverted file system to improve the recall of important classes. With the help of dedicated kernel design, it can be totally parallelized in mainstream training framework. Then, we find the size of important classes that are recalled by each training sample has a great impact on the final accuracy, so we introduce sample grouping optimization to well approximate the full classes training. Experimental evaluations on two tasks (Embedding Learning and Classification) and ten datasets (e.g., MegaFace, ImageNet, SKU datasets) demonstrate our proposed method maintains the same precision as Full Softmax for different loss objectives, including cross entropy loss, ArcFace, CosFace and D-Softmax loss, with only 1/10 sampled classes, which outperforms the state-of-the-art techniques. Moreover, we implement ANN Soft-max in a complete GPU pipeline that can accelerate the training more than 4.3X. Equipped our method with a 256 GPUs cluster, the time of training a classifier of 300 million classes on our SKU-300M dataset can be reduced to ten days.
Hands Activities Detection in Egocentric Interactions Using YOLOv5

Hands emerge quite often in egocentric video, and their structure and disposition reveal a lot as to what individuals have been engaged and showing interest to. Earlier researches in hand detection, on the other hand, have depended on significant effect that only operate in limited situations, such as lab settings or with minimal social interaction. This work investigates the recently developed YOLO architectural for object recognition in egocentric video, which offers a good balance of precision and performance. We performed an experiment of assessments utilizing various different architecture configurations and training methods. Our experiment leads to all sorts of conclusions about the most cost - effective methodologies to our strategic objectives, and leads us to further research to enhance detection of smart glasses videos. We tested these approaches on an existing dataset that contains 48 first individual footage of people interacting in situations in real life as well as the bounding box on a pixel level for more than 15,000 hand gestures.
Convolutional Neural Network Model based Analysis and Prediction of Oral Cancer

This research paper deals the analysis and prediction of oral cancer using various machine learning classifiers are used for implementation with the datasets and the performance are analyzed. From the results, it’s observed that the various stage classification of carcinoma are often classified effectively. Machine learning approach is employed for early analysis and detection of carcinoma. By combining different machine learning techniques we will recover leads to early diagnosis of carcinoma.
Real-Time Traffic Classification through Deep Learning

The increasing urbanization of the global population has drawn many researchers’ attention to the field of Intelligent Transportation Systems. Numerous hardware and software technologies have been developed to aid in monitoring and managing the flow of traffic on road networks. As digital cameras become increasingly cheaper and able to produce higher quality images, automated video-based traffic management systems can provide a low cost alternative to conventional (expensive) traffic monitoring systems. In this work we evaluate diverse state-of-the-art deep-learning-based vehicle recognition frameworks on datasets containing surveillance footage of heterogeneous and representative traffic data from Melbourne’s road network. We find that the YOLOv5 family of models offers the optimal balance between detection accuracy, model size, and real-time detection capability for resource-constrained traffic monitoring devices.
VLSI design of multiclass classification using sparse extreme learning machine for epilepsy and seizure detection

An automatic detection system for distinguishing healthy, ictal, and inter-ictal EEG signals is of importance in clinical practice. This paper presents a low-complexity three-class classification VLSI system for epilepsy and seizure detection. The designed system consists of a discrete wavelet transform (DWT)-based feature extraction module, a sparse extreme learning machine (SELM) training module, and a multiclass classifier module. A lifting structure of Daubechies order 4 wavelet is introduced in three-level DWT to save circuit area and speed up the computational time. The SELM which is a novel machine learning algorithm with low hardware complexity and high-performance is used for on-chip training. One-against-one multiclass non-linear SELM is designed for the first time due to its high classification accuracy. The designed system is implemented on an FPGA platform and evaluated using the publicly available epilepsy dataset. The experimental results demonstrate that the designed system achieves high accuracy with low-dimensional feature vectors.
Data-Driven Link Prediction Over Graphical Models

The positive link prediction problem is formulated in a system identification framework: We consider dynamic graphical models for autoregressive moving-average (ARMA) Gaussian random processes. For the identification of the parameters, we model our network on two different time scales: A quicker one, over which we assume that the process representing the dynamics of the agents can be considered to be stationary, and a slower one in which the model parameters may vary. The latter accounts for the possible appearance of new edges. The identification problem is cast into an optimization framework which can be seen as a generalization of the existing methods for the identification of ARMA graphical models. We prove the existence and uniqueness of the solution of such an optimization problem and we propose a procedure to compute numerically this solution. Simulations testing the performances of our method are provided.
MIA

Since online discussion platforms can limit the perception of social cues, effective collaboration over videochat requires additional attention to conversational skills. However, self-affirmation and defensive bias theories indicate that feedback may appear confrontational, especially when users are not motivated to incorporate them. We develop a feedback chatbot that employs Motivational Interviewing (MI), a directive counseling method that encourages commitment to behavior change, with the end goal of improving the user's conversational skills. We conduct a within-subject study with 21 participants in 8 teams to evaluate our MI-agent 'MIA' and a non-MI-agent 'Roboto'. After interacting with an agent, participants are tasked with conversing over videochat to evaluate candidate résumés for a job circular. Our quantitative evaluation shows that the MI-agent effectively motivates users, improves their conversational skills, and is likable. Through a qualitative lens, we present the strategies and the cautions needed to fulfill individual and team goals during group discussions. Our findings reveal the potential of the MI technique to improve collaboration and provide examples of conversational tactics important for optimal discussion outcomes.
Style Transfer of Image Target Region Based on Deep Learning

With the rapid development of style transfer technology today, the global style transfer technology has basically taken shape, but in the actual application process, there are problems such as the inability to transfer the local style of the target area of the picture. Therefore, in response to such situations, this paper proposes a deep learning-based style transfer framework for image target regions. Firstly, the content map is the main input, and the mask map generated by the image mask technology is used as the specific condition input. Then, the transfer network style transfer area of the specific condition input is only the target area, and the non-target area is not in the style transfer area. Finally, the transfer network style transfer input via specific conditions realizes the image style transfer of the target area. At the same time, experiments were conducted on the public data set COCO 2014. The experimental results show that the target region style transfer network model based on deep learning proposed in this paper has a better local style transfer effect, and the transfer time has been improved.
GMMT: A Revocable Group Merkle Multi-tree Signature Scheme

G-Merkle (GM) (PQCrypto 2018) is the first hash-based group signature scheme where it was stated that multi-tree approaches are not applicable, thus limiting the maximum number of supported signatures to \(2^{20}\). DGM (ESORICS 2019) is a dynamic and revocable GM-based group signature scheme that utilizes a computationally expensive puncturable encryption for revocation and requires interaction between verifiers and the group manager for signature verification. In this paper, we propose GMMT, a hash-based group signature scheme that provides solutions to the aforementioned challenges of the two schemes. GMMT builds on GM and adopts a multi-tree construction that constructs new GM trees for new signing leaves assignment while keeping the group public key unchanged, Compared to a single GM instance which enables \(2^{20}\) signature, GMMT allows growing the multi-tree structure adaptively to support \(2^{64}\) signatures under the same public key. Moreover, GMMT has a revocation mechanism that attains linkable anonymity of revoked signatures and has a logarithmic verification computational complexity compared to the linear complexity of DGM. The group manager in GMMT requires storage that is linear in the number of members while the corresponding storage in DGM is linear in the number of signatures supported by the system. Concretely, for a system that supports \(2^{64}\) signatures with \(2^{15}\) members and provides 256-bit security, the required storage of the group manager is 1 MB (resp. \(10^{8.7}\) TB) in GMMT(resp. DGM).
Multi-domain residual encoder–decoder networks for generalized compression artifact reduction

A fundamental requirement for designing compression artifact reduction techniques is to restore the artifact free image from its compressed version regardless of the compression level. Most existing algorithms require the prior knowledge of JPEG encoding parameters to operate effectively. Although there are works that attempt to train universal models to deal with different compression levels, some JPEG quality factors (QF) are still missing. To overcome these potential limitations, in this paper, we present a generalized JPEG-compression artifact reduction framework that relies on improved QF estimator and rectified networks to take into account all possible QF values. Our method, called a generalized compression artifact reducer (G-CAR), first predicts QF by analyzing luminance patches with high activity. Then, based on the estimated QF, images are adaptively restored by the cascaded residual encoder–decoder networks learned in multiple domains. Results tested on six benchmark datasets demonstrate the effectiveness of our proposed model.
Automated Medical Symptoms Summarization and Patient Profiling System

This article talks about an interactive intelligent agent that was built using the techniques of natural language processing (NLP) and computer vision (CV). Called the DocAssist, this rule-based AI chatbot is a virtual assistant that tries to imitate the doctors' physician assistant. It is an NLP tool for Automated Medical Symptoms Summarization and Patient Logging System, that would help medical practitioners save some valuable time and energy going through a patient's healthcare portfolio. It functions by asking for the required details and accepting speech inputs from the patients. The bot hence collects all the information such as name, age, gender, symptoms being experienced, and previous medical history, and then by using different techniques of NLP, namely regular expressions (Regex) analysis, FastAi text classification, entity extraction, punctuating, and noun chunking, it tries to extract only the useful information (keywords and entities). All the details are then curated and stored in a text file for the doctors quick perusal. Moreover, the patients also get an option to upload any supporting images (pictures or scanned documents), the corresponding super-resolution form of which is later sent to the doctor.
Investigation of Different G2P Schemes for Speech Recognition in Sanskrit

In this work, we explore the impact of different grapheme to phoneme (G2P) conversion schemes for the task of automatic speech recognition (ASR) in Sanskrit. The performance of four different G2P conversion schemes is evaluated on the ASR task in Sanskrit using a speech corpus of around 15.5 h duration. We also benchmark the traditional and neural network based Kaldi ASR systems on our corpus using these G2P schemes. Modified Sanskrit library phonetic (SLP1-M) encoding scheme performs the best in all Kaldi models except for the recent end-to-end (E2E) models trained with flat-start LF-MMI objective. We achieve the best results with factorized time-delay neural networks (TDNN-F) trained on lattice-free maximum mutual information (LF-MMI) objective when SLP1-M is employed. In this case, SLP1 achieves a word error rate (WER) of 8.4% on the test set with a relative improvement of 7.7% over SLP1. The best E2E models have a WER of 13.3% with the basic SLP1 scheme. The use of G2P schemes employing schwa deletion (as in Hindi, which uses the same Devanagari script as Sanskrit) degrades the performance of GMM-HMM models considerably.
Analysis of Address Linkability in Tornado Cash on Ethereum

Abstract Tornado Cash, the most popular non-custodial coin mixer on Ethereum, is widely used to protect the privacy of addresses. However, some inappropriate transaction behaviors in Tornado Cash mixing mechanism lead to the risk of privacy leakage. More specifically, the malicious attackers can link multiple addresses of the same users according to the transaction data. Motivated by the above problem, this paper systematically analyzes the privacy issues of Tornado Cash for the first time. In this paper, we give the macroscopic analysis of Tornado Cash based on the on-chain data and formalize two types of transaction patterns. Focus on the presented transaction patterns, we propose three heuristic clustering rules to link the users’ addresses, which reduce the size of users’ anonymity set. Finally, we perform the experiment on real Tornado Cash transaction data to describe the effectiveness of the proposed clustering rules.
A Proposed Curriculum for an Introductory Course on Interactive Digital Narratives in Virtual Reality

Immersive Media programs of study are being developed and enacted at many higher education institutions. It is proposed that a course on Interactive Digital Narratives (IDN) in Virtual Reality (VR) can familiarize undergraduate students of diverse backgrounds with the foundational technical, design, and development tenets of immersive storytelling. Course curriculum balances IDN design and immersive storytelling strategies with VR project management, user experience and interface design, spatial audio, digital scenography, introductory programming, and rudimentary artificial intelligence. The course connects technical and media affordances to theories of IDN to provide an introductory understanding of IDN in VR. The proposed course ran in the spring of 2021 at a small liberal arts college. The paper presents the course's 15-week curriculum. An evaluation that includes student work, insights lessons, and resources is provided.
Unsupervised Multi-Subclass Saliency Classification for Salient Object Detection

Numerous bottom-up salient object detection algorithms formulate the problem as a classification task. For an input image, these methods usually utilize prior cues to select some regions as training set, and learn a classifier to classify all regions into foreground/background. However, such binary classification based approaches suffer from accuracy problems in some complex scenes. To this end, we propose a novel framework, namely Multi-Subclass Classification with Label Distribution Learning (MSCLDL). Specifically, prior knowledge is firstly employed to build a training set from input image, in which each sample is associated with one of two class labels. Previous works usually learn directly a binary classification model from training set. Different with them, we further decompose two classes into a certain number of subclasses, each sample is thus described by one of multiple subclass labels. Based on the multi-subclass training set, we learn a label distribution model to predict the subclass label of each image region. Furthermore, the saliency value of each image region could be computed via exploring the relationship class and subclass labels. The MSCLDL could overcome the limitation of existing classification-based algorithms in some challenging scenes. Finally, a novel refinement technology is presented to further refine the saliency map obtained by MSCLDL. We compare the proposed method and other state-of-the-art methods on four benchmark datasets, the superiority of our model is adequately demonstrated via the experimental results analysis.
Data warehouse for machine learning: application to breast cancer diagnosis

The diagnosis and treatment of breast cancer are very much studied in the field of medical research. Indeed, the early detection of this pathology allows for the best possible treatment and minimization of the after-effects. The business process "Diagnosis of breast cancer in women" is perfectly mastered by the practitioner and the medical profession and is widely documented in the literature. We propose a vision of "analysis of the existing" which allows to discover this process to the computer engineer in charge of the implementation of a system for aid in diagnosis and prognosis of the disease. This step is indispensable to discover the data generated during the business process and to discover in a later step all the characteristics that govern the disease. We define a system of diagnosis and prognosis of the disease Diaprog which uses the data of the classified and archived care records. We say that we use the historical data generated during the main process. We distinguish for this purpose between a production database and a data warehouse. The latter is a database fed from the various production databases accessible and made available in our system. In the case of our work, we call this data warehouse DDA (Diagnosis Data Archive). The data warehouse thus defined is a component of the medical diagnosis aid system Diaprog whose functional architecture also implements a learning module that allows the discovery of relationships between the characteristics that govern the disease.
Implementing CRYSTAL-Dilithium on FRDM-K64

This document presents the performance of the lattice-based signature scheme Dilithium for FRDM-K64 ARM Cortex-M4. Dilithium is a recent post-quantum cryptographic scheme, such as a candidate in the third round of the NIST. The Cortex-M4 is considered a suitable microcontroller for post-quantum cryptography so that FRDM-K64 is used to execute the Dilithium scheme. The PQM4 library and Greconici, Kannwischer, and Sprenkels implementation are compared and improved to achieve a low speed of 17% in NIST Security Level 2.
Compliance Checking for Transactive Energy Contracts using Smart Contracts

Through transactive energy (TE) platforms, prosumers can enter into a contractual agreement with an Independent Electricity System Operator (IESO) to buy and sell energy. Accordingly, the TE contract holders are liable for contractual violations. Manual compliance checking of such transactions is infeasible due to large number of market rules as well as the plethora of executing TE contracts. Moreover, the TE system big data (e.g., offers, bids, and transaction activities) need to be maintained on a transparent, reliable, and secure plat-form. This paper presents a compliance checking method for transactive energy markets based on the IESO (in Ontario, Canada) market rules by using smart contracts that assure the integrity, reliability, and transparency of energy transactions’ data with a permissioned blockchain. The performance of the blockchain network is evaluated through transaction latency and resource utilization. In addition, an acceptance test is successfully conducted to validate the correctness of the platform in terms of trading workflow, runtime status of the TE contracts, and the quality of market clearing results.
Smart Job Recruitment Automation Using Location Based Filtering

Knowledge is both theoretical and practical information, facts and skills we acquire through experience or learning. Our knowledge increases as we gain more experience. To. The employers often receive an enormous number of applications which is usually unstructured resumes that are different to process and analyze manually. The traditional method of recruitment is becoming inefficient. Because of the rapid advancement of Internet technology, many businesses have switched to an online recruitment approach. To overcome the drawbacks there are several automatic recruitment systems have been proposed. The online recruitment system has proved to be more effective in processing Candidate resumes and matching them to their relevant job posts. The effectiveness of the proposed system is need for a location-based services that can recommend users for jobs within their location. The advantage of this system is to reduce the traveling distance, save time, easy process and make offers confidential. Therefore, this website access the location of job seekers. It helps job seekers to apply for the job which is nearby their location and also satisfy the company requisite.