Reviewerly/exHarmony huggingface dataset:
A convex selective segmentation model based on a piece-wise constant metric-guided edge detector function

The challenge of segmentation for noisy images, especially those that have light in their backgrounds, still exists in many advanced state-of-the-art segmentation models. Furthermore, it is a tedious activity and significantly difficult to segment such images. In this article, we offer and propose a novel variational model for the concurrent restoration and segmentation of noisy images that have intensity inhomogeneity and high contrast background illumination and light. The suggested concept combines the multi-phase segmentation technology with the statistical approach in terms of local region knowledge and details of circular regions that are, in fact, centered at every pixel to enable in-homogeneous image restoration. Nevertheless, the suggested approach is expressed as a fuzzy set and is resolved using the multiplier alternating direction minimization approach. Through several tests and numerical simulations with plausible assumptions, we have evaluated the correctness, accuracy, and resilience of the suggested model over various kinds of real and synthesized images in the existence of intensity inhomogeneity and light in the background. Additionally, the findings are contrasted with those from cutting-edge two-phase and multi-phase methods, proving the superiority of our proposed approach for images with noise, background light, and inhomogeneity. We observed that the three models considered in the comparison to our proposed model fail to properly segment the object of interest. Furthermore, the Jaccard similarity metric shows the capability of our model to handle accurate segmentation of complex shapes with intensity.
Low-Resource Language Based Cognitive Psychology in English Language Analysis: A State-of-art for China's Higher Vocational Colleges

The present study aims at identifying the academic and professional needs of vocational students to enhance their English communication skills that are considered as the prerequisites for employment in China. In view of the demands of globalization, course designers and academicians design the curriculum to meet the challenges of the current scenario. This study aims at finding out the gap between student's needs and the existing syllabus to provide suggestions for the better implementation of the English communication skills course for vocational students by analysing the prescribed syllabus of Woyingzhichang and Zibo Vocational Institute in China. The data for analysis were collected from both students and teachers using a structured questionnaire. It is identified that student need fewer changes in syllabus and English teaching skills in the schools of China.
Efficient Bi-objective SQL Optimization for Enclaved Cloud Databases with Differentially Private Padding

Hardware-enabled enclaves have been applied to efficiently enforce data security and privacy protection in cloud database services. Such enclaved systems, however, are reported to suffer from I/O-size (also referred to as communication-volume)-based side-channel attacks. Albeit differentially private padding has been exploited to defend against these attacks as a principle method, it introduces a challenging bi-objective parametric query optimization (BPQO) problem and current solutions are still not satisfactory. Concretely, the goal in BPQO is to find a Pareto-optimal plan that makes a tradeoff between query performance and privacy loss; existing solutions are subjected to poor computational efficiency and high cloud resource waste. In this article, we propose a two-phase optimization algorithm called TPOA to solve the BPQO problem. TPOA incorporates two novel ideas: divide-and-conquer to separately handle parameters according to their types in optimization for dimensionality reduction; on-demand-optimization to progressively build a set of necessary Pareto-optimal plans instead of seeking a complete set for saving resources. Besides, we introduce an acceleration mechanism in TPOA to improve its efficiency, which prunes the non-optimal candidate plans in advance. We theoretically prove the correctness of TPOA, numerically analyze its complexity, and formally give an end-to-end privacy analysis. Through a comprehensive evaluation on its efficiency by running baseline algorithms over synthetic and test-bed benchmarks, we can conclude that TPOA outperforms all benchmarked methods with an overall efficiency improvement of roughly two orders of magnitude; moreover, the acceleration mechanism speeds up TPOA by 10-200×.
Bayesian learning of network structures from interventional experimental data

Summary Directed acyclic graphs provide an effective framework for learning causal relationships among variables given multivariate observations. Under pure observational data, directed acyclic graphs encoding the same conditional independencies cannot be distinguished and are collected into Markov equivalence classes. In many contexts, however, observational measurements are supplemented by interventional data that improve directed acyclic graph identifiability and enhance causal effect estimation. We propose a Bayesian framework for multivariate data partially generated after stochastic interventions. To this end, we introduce an effective prior elicitation procedure leading to a closed-form expression for the directed acyclic graph marginal likelihood and guaranteeing score equivalence among directed acyclic graphs that are Markov equivalent post intervention. Under the Gaussian setting, we show, in terms of posterior ratio consistency, that the true network will be asymptotically recovered, regardless of the specific distribution of the intervened variables and of the relative asymptotic dominance between observational and interventional measurements. We validate our theoretical results via simulation and we implement a Markov chain Monte Carlo sampler for posterior inference on the space of directed acyclic graphs on both synthetic and biological protein expression data.
Report on the Core: Spotlight on International Activities program, American Library Association Annual Conference, June 2022

Click to increase image sizeClick to decrease image size Column Editor: Jessica Morales.This column will feature reports on what is going on in the rapidly changing, everfascinating field of Technical Services. Each quarterly issue will consist of reports on new developments including integrated library systems, next generation catalogs, management of electronic resources; conference happenings and reports from professional meetings; what’s new in technical services publications; as well as reports from technical services professionals on their research and projects. Such reports, announcements, and brief articles for consideration for inclusion should be sent to: Jessica Morales, Editor, “Technical Services Report,” jmorale9@nd.edu.
A Deep Learning Approach for Author Profiling using Word Embeddings

Abstract: The task of author profiling involves predicting various characteristics of an author based on their writing style, such as their age, gender, native language, and personality traits. The PAN2013 shared task focused on author profiling in social media, where participants were tasked with predicting the gender and age of Twitter users based on their tweets. In recent years, deep learning approaches have become popular for author profiling. Two popular models are GloVe and FastText are used by the researchers to generate word embeddings. GloVe is a word embedding model that represents words as vectors in a highdimensional space, while FastText takes into account subword information to represent words. Both models have been shown to be effective for various natural language processing tasks. For the PAN2013 task, participants used various deep learning models with GloVe and FastText embeddings to predict the age and gender of Twitter users. Some approaches used a combination of multiple models to improve the performance. In this article, we focused on improving the accuracy of age and gender classification on the PAN2013 dataset, which is a benchmark corpus for author profiling. We utilized deep learning models such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) classifiers to classify authors based on their age and gender. We also used pre-trained word embeddings such as FastText and GloVe to represent the text data. Our results showed that the LSTM model achieved an accuracy of 57.53% for age classification and 60.48% gender classification, while the CNN model achieved an accuracy of 59.32% for age classification and 52.21% for gender classification. We observed that these models have been shown to be effective for various natural language processing tasks and can be used for other author profiling tasks as well.
Super-Resolution of Images using Contrastive Self-Learning

Abstract: Super-resolution (SR) techniques aim to enhance the resolution and quality of low-resolution images. In recent years, deep learning-based approaches have achieved remarkable success in this field. However, training deep SR models often requires large-scale datasets and computationally expensive operations, limiting their practicality. Different approaches have been proposed to tackle computation and time constraints. Our novel approach involves a combination of knowledge distillation and contrastive loss function to train a compact and efficient contrastive self-distillation (CSD) model. In the framework, a teacher network is initially trained on a large dataset using a traditional supervised learning approach. The teacher network learns to generate high-resolution images from low-resolution inputs. Subsequently, a student network is initialized with the same architecture as the teacher network but with fewer parameters. The student network is then trained to mimic the behavior of the teacher network by utilizing a contrastive loss. The contrastive loss is formulated by constructing positive and negative pairs of low-resolution and high-resolution image patches. The proposed CSD method achieves competitive performance compared to state-of-the-art SR methods while requiring significantly fewer parameters and computational resources. Furthermore, it demonstrates improved generalization capability by effectively reconstructing details in real-world images.
Post-mortem digital forensics analysis of the Zepp Life android application

This paper studies the post-mortem digital forensic artifacts left by the Android Zepp Life (formerly Mi Fit) mobile application when used in conjunction with a Xiaomi Mi Band 6. The Mi Band 6 is a low-cost smart band device with several sensors that allow for health and activity monitoring, collecting metrics such as heart rate, blood oxygen saturation level, and step count. The device communicates via Bluetooth Low Energy with the Zepp Life application, which displays its data, provides some controls, and acts as a bridge to the Internet. We study, from a digital forensics perspective, the Android version of the mobile application in a rooted smartphone. For this purpose, we analyze the data repositories, namely its databases and XML files, and correlate the data on the smartphone with the corresponding usage of the Mi Band device. The paper also presents two open-source scripts we have developed to ease the task of forensic practitioners dealing with Zepp Life/Mi Band 6: ZL_std and ZL_autopsy. The former refers to a Python 3 script that extracts high-level views of Zepp Life data through the command-line, whereas the latter is a module that integrates ZL_std functionalities within the popular open-source Autopsy digital forensic software. Data stored on the Android companion device of a Mi Band 6 might include GPS coordinates, events and alarms, and biometric data such as heart rate, sleep time, and fitness activity, which can be valuable digital forensic artifacts.
An adversarial learning based image steganography with security improvement against neural network steganalysis

Steganographic schemes based on distortion functions are designed to embed messages while minimizing the desired distortion function. Conversely, steganalysis techniques like CNN-based methods attempt to identify the presence of a message in the image. This study presents a steganography method based on the application of adversarial learning theories to conventional distortion-based steganography systems. A function called saliency map, which gives each pixel a value based on its influence over the steganalysis classifier’s output is employed to accomplish this. This number establishes the importance of each pixel in deceiving the steganalyzer. The image pixels are divided into two groups based on the saliency map to deceive the steganalyzer. Then, the two groups are combined by adjusting the embedding costs of the more significant group in line with the sign of the loss function. Additionally, a saliency map is used to manage the ratio of increasing or reducing embedding costs. The message is then embedded into the image using the traditional distortion-based steganography method S-UNIWARD while using new costs. Experimental results show that the suggested method can reduce the perturbation of produced stego images. Furthermore, the results reveal that 95.1% of generated images by the proposed method deceive the steganalyzer, indicating high performance compared to the baseline methods. In addition, we investigated the performance of the proposed method against an unaware and an aware steganalyzer.
An encodable superconducting nanowire trigger

As a competitive technology, the superconducting nanowire single-photon detector (SNSPD) is developing from a single-pixel to array architecture; however, it is a major challenge for reading array detectors. We propose an encodable trigger through the thermal coupling of two adjacent superconducting nanowires, which can read a current pulse with microampere amplitude and code the current pulses of different positions with different output resistances simultaneously. At the same time, the influence of external noise on superconducting devices can be avoided for isolated circuits. As a demonstration, the response pulses of a 4-pixel SNSPD are read using this trigger in the absence of an amplifier, and the photon flux distribution of different pixels is accurately reconstructed, where the total power consumption is approximately 0.8 μW. The superconducting trigger is promising for application in integrated superconducting electronics and quantum optics in the future.
DualTaxoVec: Web user embedding and taxonomy generation

Learning web user embedding based on interaction data in the context of taxonomy is a way of studying the correlation between two web users. Such user embedding is important for further user analysis. Interaction data is made up of users and the items they interact within a domain, which is a group of entities with a basic common property. Usually a taxonomy of these items that users interact with is a hierarchical category structure for a domain. However, the taxonomy is not totally suitable for a particular task. To solve this problem, we propose a dual-way method DualTaxoVec, which learns the user embedding based on the taxonomy of the user interaction items. Meanwhile, it automatically constructs the taxonomy for the items that adapts the domain of users. It is composed of user–item and item–user tracks to construct the taxonomy and embed users in a dual-way. According to the experimental results, the validity and effectiveness of the DualTaxoVec has been demonstrated.
Machine Learning based Predictive Analysis and Algorithm for Analysis Severity of Breast Cancer

&amp;nbsp;Breast cancer is the 2nd frequent occurrence of cancer among women, after skin cancer, according to the American Cancer Society. By using mammography, it is possible to detect breast cancer before it has spread to other parts of the body. It primarily affects females, though males can be affected as well. Early identification of breast cancer improves survival chances significantly, however, the detection procedure remains difficult in clinical studies. To solve this problem, a Machine Learning (ML) algorithm is used to detect breast cancer in mammogram images. In this study, 100 images from the mini-MIAS mammogram database were used, 50 of which were malignant and 50 of which were benign breast cancer mammograms. Before training the model, the sample image datasets are pre-processed using numerous techniques. The required features are then extracted from the sample images using Feature Extraction (FE) techniques, such as Daubechies (DB4) and HAAR. Finally, the extracted features are fed into ML classifiers such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Random Forest (RF) to create a model. Several performance metrics are used to evaluate FE and classification. According to the results of the analysis, the HAAR FE with the RF model is the ideal combination, with an accuracy level of 91%.&lt;br&gt;
Deep Learning Based Object Detection Using Mask R-CNN

Nowadays, an image must be verified or analyzed keenly for further processing methods. Few numbers of images can be analyzed manually for a particular object or a human being. But when millions and millions of images are present in a dataset, and every single one of them must be verified and classified based on the objects present in the image, it is necessary to find an algorithm or a technique to assist this process. Of many types and applications of object detection, this research aims at pedestrian detection. Pedestrian detection is a special way that only aims to detect the human beings in the uploaded image. The Mask R-CNN algorithm is used in pedestrian detection. The Mask R-CNN model is a Deep Learning (DL) model that can detect a certain object from an image and can also be used for image augmentation. The Mask R-CNN stands for Mask Regional Convolutional Neural Network (CNN). This is one of the classification techniques which can be designed using the Convolutional Network theories. The Mask R-CNN algorithm is the updated version of the Faster RCNN. The main difference between the faster R-CNN and the Mask R-CNN is that the mask R-CNN can bind the borders of the object detected while the faster R-CNN uses a box to identify the object. One of the major advantages of mask R-CNN is that it can provide high-quality image augmentation and is also one of the fastest image segmentation algorithms. This model can be easily implemented when compared to other object detection techniques. Python contains various inbuilt dependencies which can be installed with the help of repositories. These dependencies are used to design the model. The model is then trained and tested with various inputs for better accuracy. Image segmentation is used in various fields like medical imaging, video surveillance, traffic control, etc., so the mask R-CNN technique would be an extremely efficient DL algorithm.&lt;br&gt;
Hi-ROS: Open-source multi-camera sensor fusion for real-time people tracking

This paper presents Hi-ROS (Human Interaction in ROS), an open source framework focused on real-time accurate assessment of human motion. The system offers a series of tools to track multiple people in real-time by exploiting a calibrated camera network. No assumptions are made about the typology or number of cameras, nor about the body pose estimation algorithm used to extract the 3D poses of the people in the scene. The tools provided by Hi-ROS include a Skeleton Tracker to ensure temporal consistency of the detected poses, a Skeleton Merger to fuse the tracks from multiple cameras, thus limiting flickering phenomena, a Skeleton Optimizer to ensure limb length consistency, and a Skeleton Filter to perform real-time smoothing of the detected joint trajectories. Accuracy, tracking robustness, and real-time performance of the proposed system were evaluated on a public dataset, containing both single-person and multi-person sequences with up to 4 people interacting. The results obtained using different subsets of the proposed tools show how the complete Hi-ROS pipeline provides accurate and reliable estimates also in challenging scenarios, with a reduction of the RMSE of up to 27 % with respect to a pure tracking approach. This work aims to push forward the development of unobtrusive human–robot interaction applications, multi-person automated posture analyses, rehabilitation performance assessments, and any possible application enabled by real-time accurate assessment of human motion via markerless motion capture.
The Impact of Chinas Independent Director System on Corporate Financial Fraud: The Case of Kangmei Pharmaceutical Fraud

The case of financial fraud of Kangmei Pharmaceutical is known as one of the most serious fraud cases ever in A-share. This paper analyzes the cause, course, and result of the financial fraud of Kangmei Pharmaceutical. Data analysis of the share price of Kangmei Pharmaceutical is conducted to investigate the impact of this incident on the market. The case study method, information asymmetry theory, and principal-agent theory are used to illustrate the impact of independent directors in the financial fraud case and the insights from the fraud case of Kangmei Pharmaceutical.
Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives

Summary Machine learning (ML) approaches have been applied in the diagnosis and prediction of haematological malignancies. The consideration of ML algorithms to complement or replace current standard of care approaches requires investigation into the methods used to develop relevant algorithms and understanding the accuracy, sensitivity and specificity of such algorithms in the diagnosis and prognosis of malignancies. Here we discuss methods used to develop ML algorithms and review original research studies for assessing the use of ML algorithms in the diagnosis and prognosis of lymphoma.
A Study of Knowledge Discovery and Pattern Recognition Based on Large-Scale Sentiment Data in Online Education for College Students

With the development of the internet, online education platforms have become the main means of acquiring knowledge for people. To improve the efficiency of user analysis, this paper proposes a framework for user sentiment recognition based on course review information of online education platforms. Firstly, according to the characteristics of the review data of online platforms, the authors use crawlers and open-source word separation tools to complete data collection and collation; secondly, the authors use TextCNN (text convolutional neural networks) and BILSTM (bi-long-short-term-memory) to combine to build a feature layer fusion text sentiment classification model. The results show that the average precision of the model built is 95.1% for the three sentiment classifications. Finally, a test of the reviews of new courses on online education platforms is conducted. The sentiment recognition rate of all three types of new courses exceeds 90%. Therefore, the proposed model provides new ideas for user analysis.