Reviewerly/exHarmony huggingface dataset:
Improved parameter optimization method for measurement device independent protocol

The optimal selection of parameters in practical quantum key distribution can greatly improve the key generation rate and maximum transmission distance of the system. Owing to the high cost of global search algorithm, local search algorithm is widely used. However, there are two shortcomings in local search algorithm. One is that the solution obtained is not always the global optimal solution, and the other is that the effectiveness of the algorithm is greatly dependent on the choice of initial value. This paper uses the Monte Carlo method to prove whether the key generation rate function is convex, and also simulates and analyzes the projection of the key generation rate function on each dimension of the parameter, in contrast to the approach in previous article. In order to eliminate the effect of the initial value, this paper proposes the particle swarm local search optimization algorithm which integrates particle swarm optimization algorithm and local search algorithm. The first step is to use the particle swarm optimization to find a valid parameter which leads to nonzero key generation rate, and the second step is to adopt the parameter as the initial value of local search algorithm to derive the global optimal solution. Then, the two algorithms are used to conduct simulation and their results are compared. The results show that the key generation rate function is non-convex because it does not satisfy the definition of a convex function, however, since the key generation rate function has only one non-zero stagnation point, the LSA algorithm can still obtain the global optimal solution with an appropriate initial value. When the transmission distance is relatively long, the local search algorithm is invalid because it is difficult to obtain an effective initial value by random value method. The particle swarm optimization algorithm can overcome this shortcoming and improve the maximum transmission distance of the system at the cost of slightly increasing the complexity of the algorithm.
TECHNOLOGY IN CHILDREN'S SERVICES

From virtual reality for practitioners to the use of big data systems to share information between agencies, technology is changing the way children's services intervenes in the lives of vulnerable children and families
An industry perspective for algorithm performance, testing, and validation

It is well known that ensembles of different models can achieve better performance. Unfortunately, training multiple deep neural networks is time-consuming and expensive. Also, they require more space for storage and more computation for inference, making them unsuitable for applications with limited resources, such as mobile and embedded devices. To address these two fundamental problems in ensemble learning, we propose a novel method called Snapshot-Guided Adversarial Training (SGAT) to automatically accumulate and distill knowledge from the training, where knowledge from the earlier training iterations of the network is distilled, and transferred into its late training iterations via an adversarial learning strategy. To accumulate knowledge from the training process, we employ a cyclic annealing schedule and take a model snapshot at the end of each training interval. Furthermore, we use a shared discriminator to encourage the distillation process. The main advantages of our method are: (1) We can directly train a network with its free snapshots for knowledge distillation, instead of heavily depending on pre-trained models; (2) the inference cost remains the same as using a single model after finishing knowledge transferring; (3) SGAT is a general method and can be applied to the existing network architectures. Our extensive experiments show that SGAT consistently outperforms the standing training method with a clear margin. For example, with the same training budget, it achieves 2.86% more accuracy on average than the baseline when training MobileNet-V2 from scratch on CIFAR-100. Meanwhile, SGAT also achieves better performance in most cases than the existing ensemble method and knowledge distillation. For example, to train MobileNet-V2 from scratch on ImageNet, it gets 0.7% more accuracy than snapshot ensembles, and 0.93% more accuracy than snapshot distillation. More importantly, our accumulated learning strategy makes SGAT achieve much better performance when we increase training time. For example, compared with the standard training method for MobileNet-V2, it gets 3.13% more accuracy on ImageNet.
JPEG Pleno Call for Proposals Responses Quality Assessment

In this paper, the quality evaluation of the responses to the Call for Proposals (CfP) of JPEG Pleno Point Cloud Coding is presented. Three responses to the CfP were evaluated together with the state of the art anchor codecs G-PCC and VPCC from MPEG. The JPEG committee selected a set of eight point clouds that were encoded at different pre-established bitrates. For the subjective evaluation of the responses to the CfP, a set of video sequences were created where the reference and distorted decoded point clouds were rotated about their axes side by side. Furthermore, the objective quality metrics PCQM, PSNR D1, PSNR D2, PSNR Y and PSNR YUV were computed, and compared with the subjective evaluation results. This study revealed that the deep learning solutions outperformed G-PCC but were still below the performance of V-PCC regarding color representation. PCQM showed the best performance in predicting the compression quality.
DATA2VEC-SG: Improving Self-Supervised Learning Representations for Speech Generation Tasks

Self-supervised learning has been successfully applied to various speech recognition and understanding tasks. However, for generative tasks such as speech enhancement and speech separation, most self-supervised speech representations did not show substantial improvements. To deal with this problem, in this paper, we propose data2vec-SG (Speech Generation), which is a teacher-student learning framework that addresses speech generation tasks. Our data2vec-SG introduces a reconstruction module into data2vec [1] and enforces the representations to contain not only the semantic information but also the acoustic knowledge to generate clean speech waveforms. Experimental results demonstrate that the proposed framework boosts the performance of various speech generation tasks including speech enhancement, speech separation, and packet loss concealment. Meanwhile, the learned representation is also capable of helping other downstream tasks, which is demonstrated by the good performance in the speech recognition task in both clean and noisy conditions.
Unsupervised Extractive Summarization With Heterogeneous Graph Embeddings for Chinese Documents

In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document. Previous studies focus more on employing statistical approaches or pre-trained language models (PLMs) to extract sentence embeddings, while ignoring the rich information inherent in the heterogeneous types of interaction between words and sentences. In this paper, we are the first to propose an unsupervised extractive summarizaiton method with heterogeneous graph embeddings (HGEs) for Chinese documents. A heterogeneous text graph is constructed to capture different granularities of interactions by incorporating graph structural information. Moreover, our proposed graph is general and flexible where additional nodes such as keywords can be easily integrated. Experimental results demonstrate that our method consistently outperforms the strong baseline in three summarization datasets.
A Study on the Integration of Pipeline and E2E SLU Systems for Spoken Semantic Parsing Toward Stop Quality Challenge

Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken semantic parsing system for the quality track (Track 1) in Spoken Language Understanding Grand Challenge which is part of ICASSP Signal Processing Grand Challenge 2023. We experiment with both end-to-end and pipeline systems for this task. Strong automatic speech recognition (ASR) models like Whisper and pretrained Language models (LM) like BART are utilized inside our SLU framework to boost performance. We also investigate the output level combination of various models to get an exact match accuracy of 80.8, which won the 1st place at the challenge.
AUTONOMOUS BEACH CLEANING ROBOT CONTROLLED BY MOBILE APPLICATION WITH REAL TIME VIDEO FEED AND OBJECT DETECTION10.56726/IRJMETS36938

Beach pollution is a growing environmental concern that poses severe threats to marine ecosystems and human health.Traditional beach cleaning methods are labour-intensive and often inefficient in covering large areas or detecting small debris.This paper presents an autonomous beach cleaning robot equipped with a 6wheel drive system and a rotating cylindrical sieve, designed to address the limitations of manual beach cleaning efforts.The robot is powered by a Raspberry Pi 4B, which controls its movements and processes video feed from an onboard camera module.The system incorporates a pre-trained object detection model to identify and locate garbage on the beach in real-time.A user-friendly mobile application, developed using the Flutter framework, allows users to control the robot remotely and view the video feed with detected garbage.The proposed solution demonstrates the potential for effectively and efficiently cleaning beaches through a combination of autonomous navigation, advanced garbage detection, and user-friendly remote control, paving the way for more sustainable and accessible beach cleaning efforts.
Deep learning based two-fold segmentation model for liver tumor detection

Liver Tumour (LT) develops when healthy cells undergo abnormal DNA changes that cause them to grow and divide uncontrollably. In manual examination, evaluation might be changed by the unique perception of the observers, which depends on their expertise and subjectivity. Therefore, computer-aided intelligent tools are established to eliminate subjectivity and increase the performance. To overcome these challenges, a novel Two-fold Segmentation of Liver Tumour (TFSLT) model for accurately detecting the liver tumour using computed tomography (CT) images. Initially, the CT images are pre-processed using Normalized-Modified Anisotropic Diffusion Filtering (NMADF) Algorithm to reduce the noise artifacts. These pre-processed CT images are taken as input to the Canny Edge Detector (CED) for detecting the edges of the liver. Based on these edges, the first-fold segmentation process is performed using the Jaccard metric-based Watershed (JMWS) algorithm to accurately segment the liver region. Improved Deep Neural Network (IDNN) is utilized to classify the LT into normal, Hepatocellular carcinoma (HCC), Cholangio carcinoma (CC) and Metastatic tumour (MT). Modified Elephant Herd Optimization (MEHO) algorithm for the MEHO algorithm for selecting the features of the images. Finally, the Improved Expectation-Maximization (IEM) Algorithm as second-fold segmentation process to segment the different abnormal classes. The performance of the proposed TFSLT approach is assessed using the specific metrics like recall, precision, specificity, accuracy and F1 score. The experimental findings reveal that the proposed TFSLT approach achieves a better accuracy range of 99.57% for detecting LT in its early stages.
Structural design and mechanical analysis for space-borne electronic equipment

Abstract With the development of a new generation of radar performance requirements, the chip integration and module assembly density of onboard electronic equipment have been greatly improved. The integration of the system is becoming more complex. This paper designs a kind of electronic equipment with a superposition structure. Separate functional modules are strung together by a screw. Each module unit has the same sectional size. The conventional cabinet form structure is abandoned. The main contradiction of volume, weight, and reliability is solved well. The mechanical properties of electronic equipment are analyzed by the finite element method, and the structure of the fixed lug is optimized. The result shows that the electronic equipment meets the requirements of structural safety margin. The structural design is safe and reliable.
An Error Term in the Central Limit Theorem for Sums of Discrete Random Variables

Abstract We consider sums of independent identically distributed random variables whose distributions have $d+1$ atoms. Such distributions never admit an Edgeworth expansion of order $d$, but we show that for almost all parameters the Edgeworth expansion of order $d-1$ is valid and the error of the order $d-1$ Edgeworth expansion is typically of order $n^{-d/2}.$
SITE RESPONSE ANALYSIS BASED ON BOREHOLE ACCELERATION RECORDS FROM LOCATION TOWER, OHRID 3D SEISMIC NETWORK

The Location Tower is one of the three sites from the 3D seismic network originally installed in the 80’s which is recently re-established and enabled for real-time monitoring and recording acceleration data. The Location Tower is consisted of one surface and three downhole instruments up to 125 meters down the bedrock; a nine story building with two instruments installed on 6th and 9th storey &amp; 4 instruments installed at the foundation level. In the period of 2021-2022 several small to moderate earthquakes have been recorded with the system. This study presents selected results and comparisons of equivalent linear analysis of the site using real recorded acceleration data. The soil profile is defined based on number of geophysical and geotechnical investigations at the location, both in-situ and laboratory tests. Obtained results are good starting point for further non-linear site response analysis at the location which can be validated with stronger recorded earthquakes in future.
W2KPE: Keyphrase Extraction with Word-Word Relation

This paper describes our submission to ICASSP 2023 MUG Challenge Track 4, Keyphrase Extraction, which aims to extract keyphrases most relevant to the conference theme from conference materials. We model the challenge as a single-class Named Entity Recognition task and developed techniques for better performance on the challenge: For the data preprocessing, we encode the split keyphrases after word segmentation. In addition, we increase the amount of input information that the model can accept at one time by fusing multiple preprocessed sentences into one segment. We replace the loss function with the multi-class focal loss to address the sparseness of keyphrases. Besides, we score each appearance of keyphrases and add an extra output layer to fit the score to rank keyphrases. Exhaustive evaluations are performed to find the best combination of the word segmentation tool, the pre-trained embedding model, and the corresponding hyperparameters. With these proposals, we scored 45.04 on the final test set.
Multilayer Subspace Learning With Self-Sparse Robustness for Two-Dimensional Feature Extraction

Two-dimensional (2D) feature extraction techniques are specifically designed for reducing the dimension of data in matrix representation. Existing methods mostly rely on bilateral projections of matrices. This rasterized manner critically limits the freedom of feature combinations, and thus degrades the fitting ability of models. The robustness of previous techniques is also unsatisfactory due to the insufficiency of discerning outliers with severe damages. In this work, we propose a novel bilinear subspace learning model to achieve flexible and robust two-dimensional feature extraction. The features are exploited in a multilayer bilinear low-rank space under a collaborative orthogonality constraint, and a self-sparse robust strategy is imposed. The model greatly extends the projection space and relaxes the restriction in conventional orthogonal space. We accordingly design a tactful and efficient optimization method based on the coordinate descent method, optimally addressing the proposed model. Experimental results demonstrate the excited improvements of our extracted features in image classification task.
Conversation-Oriented ASR with Multi-Look-Ahead CBS Architecture

During conversations, humans are capable of inferring the intention of the speaker at any point of the speech to prepare the following action promptly. Such ability is also the key for conversational systems to achieve rhythmic and natural conversation. To perform this, the automatic speech recognition (ASR) used for transcribing the speech in real-time must achieve high accuracy without delay. In streaming ASR, high accuracy is assured by attending to look-ahead frames, which leads to delay increments. To tackle this trade-off issue, we propose a multiple latency streaming ASR to achieve high accuracy with zero look-ahead. The proposed system contains two encoders that operate in parallel, where a primary encoder generates accurate outputs utilizing look-ahead frames, and the auxiliary encoder recognizes the look-ahead portion of the primary encoder without look-ahead. The proposed system is constructed based on contextual block streaming (CBS) architecture, which leverages block processing and has a high affinity for the multiple latency architecture. Various methods are also studied for architecting the system, including shifting the network to perform as different encoders; as well as generating both encoders’ outputs in one encoding pass.
Detection and Classification of Leukemia And Myeloma Using Soft Computing Techniques

Abstract: Blood cancer is a type of cancer that affects the blood cells. Medical image processing technology is essential in both early disease identification and cancer cell analysis. Blood cancer can impact the lymph nodes, bone marrow, blood cells, lymph nodes, and other lymphatic system components. A primary cause of blood cancer is an unusual and excessive amount of white blood cellular proliferation. Traditional cancer cell detection is time-consuming and inaccurate to a large extent, hence an automated approach based on soft computing techniques is presented to predict cancer cell presence and identify two types of blood cancer which are leukemia and myeloma. The dataset for Myeloma is acquired from TCIA (The Cancer Imaging Archive) repository and the Leukemia dataset comes from Kaggle-Blood Cell Images, both of which are available to the public. The datasets are already pre-processed. In our study, we have compared different hybrid models like DenseNet with XGBoost, InceptionResNet with SVM, etc from which the combination of VGG-19 for feature selection and SVM for classification gives the best performance. We have achieved Classification Accuracy of 96.4%(0.964), Precision(0.964), F1 Score(0.964) and Recall(0.964) for SVM.
Raw Ultrasound-Based Phonetic Segments Classification Via Mask Modeling

Ultrasound tongue imaging is widely used in clinical linguistics and phonetics. Recently, deep neural networks, especially convolutional neural networks, have been widely used in the interpretation and analysis of ultrasound tongue images (UTI). Despite achieving satisfactory performance, deep models rely on a large amount of manually labeled data, which is often difficult to obtain in practical settings. To address this issue, this paper focuses on how to utilize a large amount of unlabeled UTI data to improve the performance of UTI classification task. Specifically, we explore self-supervised learning with masking modeling strategy. By predicting the masked part, our pre-trained model enables the neural network to infer contextual information. Then, we fine-tune the pre-trained model with a small amount of labeled data. Compared with the previous competing algorithms, our method can improve the classification accuracy by an average of 13.33% in four different scenarios.
A Fusion-Based and Multi-Layer Method for Low Light Image Enhancement

The results obtained by many current low light image enhancement algorithms exhibit unnatural effects and insufficient contrast. This paper proposes a low light image enhancement algorithm using a fusion-based and multi-layer model. We decompose the illumination of the input image into high-frequency and low-frequency components. Then, we adjust the illumination using two different correcting functions based on a priori statistics and decompose them iteratively until the final distribution is uniform. Finally, we obtain enhanced results by multiplying all the decomposed reflectance with the last layer of corrected illumination. The experiments show that our proposed method achieves excellent image naturalness, contrast, and quality.
Desenvolvimento e avaliação da plataforma web TAEP4.0 para apoiar projetos educacionais baseados na Educação 4.0

ABSTRACTEducation 4.0 is considered a learning paradigm that seeks toadapt and transform the ways of teaching and learning throughtechnological resources. In order to bring Education 4.0 closer toschools, the web platform TAEP4.0 was proposed, which has a setof steps and resources to support teaching and learning processesaimed at the challenges of the 21st Century. Therefore, this paperpresents the development and evaluation of this web platform.The evaluation was carried out by a specialist in Informatics inEducation. As part of the results, it was identified that theplatform could help document teachers' decisions and support theprocess of monitoring and evaluating the activities carried out bystudents.
Do Offenders [Fraudsters] ‘Collaborate and Listen’? A Quantitative Analysis of Fraudsters’ Decision-making Processes on Active Cybercrime Marketplaces

Research suggests that an offender–victim overlap within the physical and online environment exists. Therefore, it is unsurprising that online fraudsters have victimized each other (i.e., offender-victims) online. However, research is limited regarding the influence that the situational environment (e.g., online and cyberspace) has on online offender [fraudster] decision-making processes and their interactions with other online offenders. The current study uses criminological and communications theories to explore the influence of the situational environment on offenders’ interactions within illicit online marketplaces. A series of bivariate analyses were run to examine the relationship between the independent variables and the three dependent variables. Then, a series of negative binomial regression models were used to estimate the relationship between the dependent and independent variables. The findings suggest a positive correlation between messages from fraudsters and offender victims and emphasize the role individuals’ socially engineered (e.g., deceptive and manipulative) interactions impact targets, including offender victims.
Anti-Phishing Tools: A Thorough Comparison of Features and Performance

Abstract: Phishing attacks are a common form of cyber-attacks that can result in data breaches, financial losses, and harm an organization's reputation. The purpose of the study is to evaluate and compare three popular anti-phishing tools' effectiveness in detecting and preventing such attacks. The research method involves conducting a study to identify best performing antiphishing tool. The findings show that some tools have high accuracy; others have a lower false positive rate. Overall, this research paper provides useful information about the effectiveness of anti-phishing tools and can help organizations select the most appropriate tool for their specific needs.