Reviewerly/exHarmony huggingface dataset:
Retail Gaze: A Dataset for Gaze Estimation in Retail Environments

The concept of gaze object estimation predicts a bounding box that a person looks steadily. It is a applicable and contemporary technique in the retail industry. However, the existing datasets for gaze object prediction in retail is limited to controlled environments and do not consider retail product category area segmentation annotations. This paper proposes Retail Gaze, a dataset for gaze estimation in real-world retail environments. Retail Gaze is composed of 3,922 images of individuals looking at products in a retail environment, with 12 camera capture angles. Furthermore, we use state-of-the-art gaze estimation models to benchmark the Retail Gaze dataset and comprehensively analyze the results obtained.
Transtl: Spatial-Temporal Localization Transformer for Multi-Label Video Classification

Multi-label video classification (MLVC) is a long-standing and challenging research problem in video signal analysis. Generally, there exist many complex action labels in real-world videos and these actions are with inherent dependencies at both spatial and temporal domains. Motivated by this observation, we propose TranSTL, a spatial-temporal localization Transformer framework for MLVC task. In addition to leverage global action label co-occurrence, we also propose a novel plug-and-play Spatial Temporal Label Dependency (STLD) layer in TranSTL. STLD not only dynamically models the label co-occurrence in a video by self-attention mechanism, but also fully captures spatial-temporal label dependencies using cross-attention strategy. As a result, our TranSTL is able to explicitly and accurately grasp the diverse action labels at both spatial and temporal domains. Extensive evaluation and empirical analysis show that TranSTL achieves superior performance over the state of the arts on two challenging benchmarks, Charades and Multi-Thumos.
Integrating Multiple ASR Systems into NLP Backend with Attention Fusion

Spoken language processing (SLP) systems such as speech summarization and translation can be achieved by cascade models. It combines an automatic speech recognition (ASR) frontend and a natural language processing (NLP) backend including machine translation (MT) or text summarization (TS). With this cascade approach, we can exploit large non-paired datasets to independently train state-of-the-art models for each module. However, ASR errors directly affect the performance of the NLP backend in the cascade approach. In this paper, we reduce the impact of ASR errors on the NLP back-end by combining transcriptions from various ASR systems. Recognizer output voting error reduction (ROVER) is a widely used technique for system combination. Although ROVER improves ASR performance, the combination process is not optimized for backend tasks. We propose a system combination that resembles ROVER using attention fusion to achieve the alignment and the combination of multiple ASR hypotheses. This allows the combination process to be optimized for the backend NLP task without changing the ASR frontend. Our proposed technique is general and can be applied to various SLP tasks. We confirm its effectiveness on both speech summarization and translation experiments.
Gradient Difference-Predictive Coding Network (GD-PredNet): Reducing the Convergence Time for Future Frame Prediction with Infinitesimal Quality Trade-off

Even though future frame prediction in videos is a relatively young unsupervised learning task, it has shown promise by accommodating the networks to effectively learn efficient internal representations in a visual hyperspace. Predictive Coding Network (PredNet) uses future frame predictions as a learning signal and has a legacy background of unconscious inference, free energy, and predictive coding model of the visual cortex; it is still a relatively young network compared to RNNs, CNNs, and so on. Although Rao and Ballard&#x2019;s proposed Predictive Coding (PC) model is aimed at reducing the redundancy within the learned internal representations by a network, and Lotter et al.&#x2019;s design of the PredNet might not be the ideal replication of the PC model, it still shows promise for learning better less-redundant internal representations than other networks. In this paper, we augment PredNet to enhance its performance in future frame prediction. Additionally, we introduce a new measure known as the gradient difference error (GDE) measure based on the gradient difference loss (GDL) function proposed by Mathieu et al. We do this to adapt the GDL function to the context of PredNet since it uses an implicit loss function besides the explicit loss used during training. Our experimental results show that PredNet, when using a combination of the L1 loss function with GDE or GDL, is faster to converge to the best performance while trading off minimal quality of the predictions within a given training window. In doing so, we transform PredNet into Gradient Difference-PredNet (GD-PredNet), and we aim to encourage increased research in Predictive Coding and PredNet.
Critical Review of Design Considerations in Forming a Cloud Infrastructure for SMEs

Cloud based solutions provide a robust and flexible approach to support and improve the digital businesses for better success. This study is focused on the need analysis for the Small-to-Medium Enterprise (SME) companies for the perspective of finding the most suitable and appropriate cloud strategies to obtain the best outcome. Various aspects included in the design of the cloud solution such as scalability, cost-effectiveness, performance and efficiency are discussed. The essential components of the cloud infrastructure framework such as applications, services and orchestration are also explored. Finally, authors have proposed an infrastructural framework focusing on the virtual layer, control layer and orchestration layer of the Cloud.
New bag-of-feature for histopathology image classification using reinforced cat swarm algorithm and weighted Gaussian mixture modelling

Abstract The progress in digital histopathology for computer-aided diagnosis leads to advancement in automated histopathological image classification system. However, heterogeneity and complexity in structural background make it a challenging process. Therefore, this paper introduces robust and reliable new bag-of-feature framework. The optimal visual words are obtained by applying proposed reinforcement cat swarm optimization algorithm. Moreover, the frequency of occurrence of each visual words is depicted through histogram using new weighted Gaussian mixture modelling method. Reinforcement cat swarm optimization algorithm is evaluated on the IEEE CEC 2017 benchmark function problems and compared with other state-of-the-art algorithms. Moreover, statistical test analysis is done on acquired mean and the best fitness values from benchmark functions. The proposed classification model effectively identifies and classifies the different categories of histopathological images. Furthermore, the comparative experimental result analysis of proposed reinforcement cat swarm optimization-based bag-of-feature is performed on standard quality metrics measures. The observation states that reinforcement cat swarm optimization-based bag-of-feature outperforms the other methods and provides promising results.
A case study Evaluation of Blockchain for digital identity verification and management in BFSI using Zero-Knowledge Proof

The Banking, Financial Services, and Insurance (BFSI) sector represents a significant portion of any developing or developed economies of the world involving all banking, insurance, and non-banking financial institutions. This sector is the biggest buyer of identity management technology solutions and services. This accentuates the pivotal role of a robust identity authentication mechanism in the BFSI sector. The rapid proliferation of digitization in the different financial sectors, disruptive technological innovations around different services, and everchanging user behaviors are revolutionizing the way in which the institution of this sector interacts with their customers, employees, and other stakeholders. The definition of great consumer experience has widened in scope and includes facets like consumer trust, security, real-time, etc. The traditional identity verification system like passwords, pins, biometrics, facial recognition, etc. are prone to vulnerabilities. Blockchain addresses the lacunas in the present system by using a decentralized approach to transform digital identity. The purpose of this research paper is to study the use of blockchain in digital identity verification, the benefits it brings to identity management, and different techno-commercial use cases. This paper will also examine the zero-knowledge proof and the role of cryptography. For this study, a case study technique was used, in which different use cases of blockchain for digital identity management in the BFSI sector have been analyzed. Academics, practitioners, and government officials will benefit from the research article in investigating, implementing, and developing solutions for digital identity verification using blockchain.
Haze Removal in Remote Sensing Images for Improved Data Analysis and Extraction

Remote sensing imageries are extensively utilized for several applications such as categorization of crops, oil spill observation, cultivation of land, and forest fires. However, despite its crucial information content, the visibility of these crucial imageries gets corrupted due to attenuation or smattering because sometimes due to hazy climatic conditions atmospheric elements cause malfunctioning of scenic lights imageries. Therefore, appropriate dehazing schemes are mandatory for clearer imageries. In this paper, the technique that has been utilized is a compound fusion strategy that considers two input hazy imageries and dehazed the imagery by first intensifying the whiteness and contrast of it. For deriving the complete data and preservation of regions for proper perceptibility, significant features have been filtered by figuring three processes of weight maps which include first is for magnifying the luminance, second is for amplifying the quality of color and third is saliency which sharps the special features such as pixels as well as resolution. To reduce the artifacts which arise from the weight maps, the Laplacian pyramid methodology is being utilized. It also discusses some parameters that are responsible for the formation of the haze such as scenic depth, transmission map and atmospheric scattering model. Experimental results have also been revealed that the visibility of the recovered dehazed imageries is considerably improved by computing all steps which are shown in the figures.
Spoken Language Recognition with Cluster-Based Modeling

In this study, we analyze the incorporation of cluster-based modeling into the language recognition systems, in which a single utterance is represented as an embedding, deploying widely used i-vectors and x-vectors. We compare the results obtained with a Cosine Distance Scoring, Gaussian Mixture Model, Logistic Regression, and the Mixture of von Misses-Fisher distributions with the classifiers based on the proposed approach which incorporates cluster-based sub-models. Experimental evaluation is performed on the i-vector embeddings from the NIST 2015 language recognition i-vector machine learning challenge and the x-vector embeddings from the Oriental Language Recognition 2020 Challenge (AP20-OLR). The experimental results clearly show that the proposed approach combined with discriminatively trained Logistic Regression classifier achieves notable improvements over the baseline systems, i.e., those without language sub-models, and that our approach is competitive to other systems reported in the literature.
A Detection And Tracking System For Fisheye Videos From Traffic Intersections

Traditional pinhole camera models use rectilinear lenses and yield a smaller field of view (FOV). In order to survey wide areas in traffic intersections, multiple Closed-Circuit TeleVision (CCTV) cameras are needed. A fisheye camera lens can provide a larger FOV than a traditional camera without blind spots, but the images are often distorted. It is a valuable tool in both vehicle detection and tracking systems. In this research we propose a system for detecting and tracking vehicles in fisheye videos obtained from a traffic intersection. We first calibrated the fisheye videos into perspective views, then we trained several detection models on the collected image datasets. After training, we applied tracking algorithms on the video combined with detection results. We tested the performance of our system using mean average precision, center location error and processing time. Our experiments have shown that the different combinations of detection and tracking algorithms have tradeoffs in accuracy and processing time.
Using Blockchain Technology for a Sustainable Agri-food Supply Chain in Thailand

Agri-foods that generally made purchased at traditional markets are now increasingly made purchased at modem trade market chains - supermarkets, particularly developing country. This offers particular opportunity for food producers if they can adhere to the exacting supplier standards and systems dictated by supermarket chains particularly quality assurance. Rural farmers can find themselves excluded from dealing with such bodies because they do not have the resource to implement the necessary systems to guarantee process quality. There is increasing need to develop more efficient and effective logistics operation in order to better integrate Thailand&#x2019;s agri-food industry particularly food producer into this important business sector. The blockchain in its guise as distributed ledger&#x2019;s allow encrypted information regarding each transaction to be securely shared between the relevant parties and offers a new approach for enhancing transparency and traceability between business and food producer, as well as the government organisations. This paper introduces the challenges currently faced by food producers and demonstrates how blockchain could potentially integration and improve service reliability for food producers.
A neural refinement network for single image view synthesis

Recent years have seen an increasing interest in single image view synthesis. It remains however a challenging task due to the lack of comprehensive colour and depth information from different views. In this paper, we propose a novel view synthesis approach that incorporates a Neural Image Refinement Network (NIRN) and generates both depth and colour images for the target view in an end-to-end manner. The appearance of the colour image greatly benefits from the generated depth image as it provides an intermediate projection relationship for the object in the 3D world. Since the direct application of geometric projection mapping will result in empty regions and/or distortions, our approach proposes to embed a novel refinement network into the view synthesis pipeline for improved performance. Experimental results on three publicly available datasets demonstrate that our NIRN outperforms other state-of-the-art view synthesis methods.
Machine Vision System of Emergency Vehicle Detection System Using Deep Transfer Learning

Accidents can happen at any time and in any location, so emergency vehicles are essential in any emergency or life-threatening circumstance. However, due to lots of people owning cars, traffic jam is a severe problem in many cities. These traffic jams have an impact on emergency vehicles, particularly ambulances, as well as other vehicles such as fire trucks and police cars. The purpose of this research is to develop an emergency vehicle detection system that will assist law enforcement in mandating traffic when emergency vehicles are on the road. The researcher used deep learning, specifically the YOLov3 technique in developing the detection system wherein it will utilize CNN in implementation. The highest mAP value out of 25 models was obtained by the detection system is 98.78% by model 21.
A Character-Level Span-Based Model for Mandarin Prosodic Structure Prediction

The accuracy of prosodic structure prediction is crucial to the naturalness of synthesized speech in Mandarin text-to-speech system, but now is limited by widely-used sequence-to-sequence framework and error accumulation from previous word segmentation results. In this paper, we propose a span-based Mandarin prosodic structure prediction model to obtain an optimal prosodic structure tree, which can be converted to corresponding prosodic label sequence. Instead of the prerequisite for word segmentation, rich linguistic features are provided by Chinese character-level BERT and sent to encoder with self-attention architecture. On top of this, span representation and label scoring are used to describe all possible prosodic structure trees, of which each tree has its corresponding score. To find the optimal tree with the highest score for a given sentence, a bottom-up CKYstyle algorithm is further used. The proposed method can predict prosodic labels of different levels at the same time and accomplish the process directly from Chinese characters in an end-to-end manner. Experiment results on two real-world datasets demonstrate the excellent performance of our span-based method over all sequence-to-sequence baseline approaches.
A Federated Blockchain Approach for Fertility Preservation and Assisted Reproduction in Smart Cities

Modern life is making people infertile. Giving birth later in life is wreaking havoc on our fertility and threatening human survival. Smart cities intend to optimize the quality of life of their citizens by utilizing technology for smarter living. This research first identifies the requirements and business opportunities of using advanced technology for smarter fertility preservation and assisted reproduction in smart cities. A federated blockchain approach is proposed for the alliance of integrated commercial egg banks (ICEBs). In particular, we designed a membership fee rebate (MFR) mechanism that offers incentives for blockchain creations in the egg banking alliance. We formulated the MFR problem into a leader–followers Stackelberg game whose objectives are (1) to maximize the benefits of forming the alliance (the leader) and (2) to maximize the benefits in each ICEB (the follower). We developed an iterative scheme that utilizes mathematical programming techniques to solve the two-level, Stackelberg game problem. With a given set of parameters of the alliance and membership fee function, and the average number of blocks generated for an oocyte, the iterative scheme achieves the optimal solution for the MFR rate per block created. A numerical example demonstrates the feasibility and applicability of the proposed iterative scheme. Numerical results show that it achieves good solutions in adding a small to medium-sized new ICEB to the existing alliance. The proposed federated approach lays the foundation for developing a blockchain-based egg banking platform.
Time-Frequency and Geometric Analysis of Task-Dependent Learning in Raw Waveform Based Acoustic Models

End-to-end raw-waveform modelling with learnable feature extraction front-ends has shown promising results in various speech/audio tasks. Despite its varied success, there have not been many attempts to understand how spectral/temporal feature integration from raw inputs helps recognize task-dependent information. Towards this aim, this work presents data-dependent and data-independent methods for understanding the modelling behavior of acoustic models. The first method employs time-frequency analysis to visualize input-specific response spectra as a function of short-time front-end block processing. The second method employs geometric properties of layer-wise weights to quantify the impact of architectural choices on signal propagation and trainability of the model. We demonstrate potential of the proposed methods with help of case studies on speech classification, speaker identification, and spoofing classification tasks.
Multilingual Second-Pass Rescoring for Automatic Speech Recognition Systems

Second-pass rescoring is a well known technique to improve the performance of Automatic Speech Recognition (ASR) systems. Neural Oracle Search (NOS), which selects the most likely hypothesis from an N-best hypothesis list by integrating information from multiple sources, such as the input acoustic representations, N-best hypotheses, additional first-pass statistics, and unpaired textual information through an external language model, has shown success in rescoring for RNN-T first-pass models. Multilingual first-pass speech recognition models often outperform their monolingual counterparts when trained on related or low-resource languages. In this paper, we investigate the use of the NOS rescoring model on a first-pass multilingual model and show that similar to the first-pass model, the rescoring model can be made multilingual. Our first-pass multilingual model does not require a language-id and we make a realistic assumption that an estimate of the language-id would be available for second-pass rescoring. We conduct comprehensive experiments on two sets of languages, one consisting of related low-resource languages, and the other with a high-resource language added to the first set to analyze the performance of the multilingual NOS rescorer under different settings. Our experimental results show that, multilingual NOS can improve the first-pass multilingual model resulting in average word error rate reduction of 9.4% in the first case, and 8.4% in the second, and out-performing the monolingual counterparts in both cases.
Learning Adjustable Image Rescaling with Joint Optimization of Perception and Distortion

The performance of image super-resolution (SR) have been greatly advanced by deep learning techniques recently. Most models are only optimized for the ill-posed upscaling task while assuming a predefined downscaling kernel for low-resolution (LR) inputs. Additionally, there exists a conflict between the objective and perceptual qualities of upscaled outputs for optimizing these models. To achieve an effective trade-off between these two qualities, the current methods are either inflexible as the model is optimized for a fixed tradeoff, or inefficient as it needs to interpolate weights or images from two separately trained models. Based on the invertible rescaling net (IRN) which learns image downscaling and upscaling together, we propose a joint optimization method to train just one model that could achieve adjustable trade-off between perception and distortion for upscaling at inference time. Additionally, it’s shown in experiments that this jointly optimized model could produce results with better accuracy while maintaining high perceptual quality compared to one optimized for perceptual quality only.
Bahdanau Attention Based Bengali Image Caption Generation

In the past few years, many works are done in object detection using images and machine translation. Inspired by those works we introduced Bahdanau Attention Based Bengali Image Caption Generation (BABBICG) that generate automatically bangla caption based on images. The Conventional encoder-decoder architectures performance curse will reduce by Bahdanau Attention and achieving momentous improvements over encoder-decoder architectures. In this work, we extract features from images using InceptionV3 neural network and generate caption using RNN decoder. We used Gated Recurrent Unit (GRU) approach as RNN. We evaluate the model using BanglaLekhaImageCaptions dataset from Mendeley Data that can help to generate bangla caption.