Reviewerly/exHarmony huggingface dataset:
A Multi-Scale Information Fusion Level Set for Breast Tumor Segmentation

Purpose : Mammography is considered an effective method of examination in early breast cancer screening. Massive work by distinguished researchers of breast segmentation has been proposed. However, due to the blurry boundaries of the breast tumor, the variability of its shape and the overlap with surrounding tissue, the breast tumor’s accurate segmentation still is a challenge. Methods : In this paper, we proposed a novel level set model which based on the optimized local region driven gradient enhanced level set model (OLR-GCV) to segment tumor within a region of interest (ROI) in a mammogram. Firstly, Noise, labels and artifacts are removed from breast images. The ROI is then obtained using the intuitionistic fuzzy C-means method. Finally, we used OLR-GCV method to accurately segment the breast tumor. The OLR-GCV model combines regional information, enhanced edge information and optimized Laplacian of Gaussian (LOG) energy term. The regional and enhanced edge information are used to capture local, global and gradient information of breast images. The optimized Laplacian of Gaussian (LOG) energy term is introduced in the energy functional to further optimize edge information to improve segmentation accuracy. Results : We evaluated our method on the MIAS and DDSM datasets. It yielded a Dice value of 96.86% on the former and 95.51% on the latter. Our method proposed achieves higher accuracy of segmentation than other State-of-the-art Methods. Conclusions : Our method has better segmentation performance, and can be used in clinical practice.
Hybrid Algorithm for Tuning Feature Weights in a Fuzzy Classifier

The paper proposes the use of a hybrid optimization algorithm to tune the weighting coefficients of features in order to increase the quality of fuzzy classifiers in processing imbalanced data. Tuning the weights is intended to adjust the importance of the features in the rule base. A method for calculating fuzzy inference taking into account weight coefficients is proposed. The hybrid is based on a combination of two metaheuristics: gravitational search algorithm and shuffle frog leaping algorithm. It operates in a continuous mode and searches for a vector of weights that maximizes the mean geometric accuracy of the classifier. Experimental results showed improvement in geometric mean accuracy for 33 of 36 imbalanced data sets with two classes compared to the basic fuzzy classifier constructed using the algorithm based on extreme values of features in classes. Although the stage of tuning weights is inferior in efficiency to the stage of optimization of terms, there are tools to improve it further.
MakerCards: Designing An Electronic Component Discovery Tool to Support Remote Physical Computing Education

Traditionally, the setting for maker education has been a physical space where students can engage in hands-on learning and often work collaboratively. However, as many schools adopt a remote learning model due to COVID pandemic conditions, a pressing need has arisen for instructional practices and tools that facilitate project-based distance learning. This work in progress presents the design process of a QR-code enabled learning tool for instructors and students working with electronics in introductory physical computing courses. Through iterative learning design research and deployment in two 3-week micro courses, we uncovered preliminary evidence for the versatility of tactile card decks in supporting disciplinary based learning behaviors, debugging practices, visual and remote communication, as well as the opportunities for enhancing knowledge transfer enabled through digital augmentation.
Research on information literacy evaluation system of Higher Vocational Students under the background of education informatization 2.0

With the wide application of information technology represented by computer and Internet, as well as the rapid development of cloud computing, big data, artificial intelligence and other new technologies, the society has higher and higher requirements for people's information literacy, which also profoundly changes the talent demand and education form. Under the background of the promulgation of "education informatization 2.0 action plan" and the development of new information technology, it is urgent for higher vocational colleges to build a set of students' information literacy evaluation system to adapt to the development of information society. This paper combs the information literacy evaluation system and characteristics at home and abroad, points out the necessity of constructing the information literacy evaluation system of Higher Vocational Students under the background of education informatization 2.0, and analyzes the content of the information literacy evaluation system.
Recursively adaptive perceptual non-local means for video coding

It’s well known that various extents of discontinuous artifacts often occur in reconstructed video. Massive in-loop coding algorithms have been presented to reduce artifacts. However, when bitrate is insufficient, in-loop coding tools alone fail to solve the problem properly. Preprocessing can be served as an effective solution to reduce compression distortion at low bitrate. In this paper, we propose a novel re-cursively adaptive perceptual non-local means (RAP-NLM) preprocessing algorithm based on just noticeable distortion (JND) model. By recursively employing both spatial and temporal non-local content perceptual characteristics, RAP-NLM filter could be adapted to reduce perceptual redundancies, which will help alleviate the artifacts. Experimental results show that our adaptive perceptual preprocessing algorithm can effectively improve the perceived quality of reconstruction video frames.
Crowd counting method based on feature fusion and attention mechanism

Aiming at the problem of background noise interference and occlusion in complex crowded crowd scenes, a crowd counting network FANet based on feature fusion and attention mechanism is proposed. By introducing a feature fusion layer and a crowd region recognition module, FANet can effectively eliminate the influence of background interference and occlusion, thereby improving counting performance. As a supplement to the feature extraction network, the feature fusion layer aims to fuse low-level texture features and high-level features to avoid a large amount of loss of features, thereby enabling the model to have higher multi-scale information perception capabilities and improving training efficiency. The crowd region recognition module generates a corresponding attention weight map for the image through convolution and up-sampling operations, and based on this, achieves the purpose of suppressing background interference. Finally, the evaluation was conducted on two data sets. The experiment showed that the MAE of the proposed method on ShanghaiTech and UCF-QNRF achieved 1.1%,3% and 1.1% improvement respectively.
Socio-psychological characteristics of students as subjects of communication, depending on their attitude to mediated interaction

The article examines the problem of the relationship between the social and psychological characteristics of students as subjects of communication and their propensity for indirect communication, spending a significant part of their personal time in social networks. A comparative analysis of the severity of difficulties in a real act of communication among students who prefer direct (real) and mediated communication is carried out. The reliability of the results obtained is confirmed by analyzing the average values of the studied group indicators, and using the methods of mathematical statistics (correlation analysis, Student’s t-test) and found a relationship between the propensity of students to communicate indirectly, the time spent communicating in social networks and the severity of difficulties in real interaction, caused by their specific socio-psychological characteristics as subjects of communication. The greatest difficulties in their views are associated with the influence on the emergence of difficulties in communication of such parameters as the intensity of communication, the number of partners, the presence of witnesses of communication, age, gender, status; suspicious, arrogant, disrespectful attitude during the conversation; social-perceptual and expressive-speech characteristics of partners; as well as the skills and abilities of the interlocutor regarding the organization of interaction. As the main conclusion, the researchers note that students who prefer real and mediated interaction have various psychological characteristics that characterize them as subjects of communication.
Seance: Divination of tool-breaking changes in forensically important binaries

The value of memory analysis during digital forensics, incident response, and malware investigations has been realized for over a decade. The power of memory forensics is based on the fact that volatile memory contains a substantial number of artifacts that are simply never recorded to disk or sent across the network in plaintext form. Orderly recovery of this data, known as structured analysis, allows for recovery of the full system state at the time of acquisition. For structured analysis to be successful, a memory analysis framework must have an accurate model of the data structures and algorithms of the target operating system and applications. Unfortunately, acquiring this layout is often a difficult task for even one version of an executable module, and the problem is only compounded when support for a wide variety of versions is desired. This issue can be manifested in several ways, including forensics frameworks being unable to process memory samples containing unsupported versions of executable code or worse, generating erroneous or incomplete results. Given the vital role memory analysis plays in modern investigations, these issues are unacceptable. In this paper, we present Seance, a system that implements automated binary analysis to provide accurate data structure layout information for different versions of targeted executed modules. The results of Seance can be consumed by analysis frameworks to accurately support all versions of a target module.
Hardware Secure Execution and Simulation Model Correlation using IFT on RISC-V

With Heterogeneous architectures and IoT devices connecting to billions of devices in the network, securing the application and tracking the data flow from different untrusted communication channels during run time and protecting the return address is an essential aspect of system integrity. In this work, we propose a correlated hardware and software-based information flow tracking mechanism to track the data using tagged logic. This scheme leverages the open-source benefits of RISC V by extending the architecture with security policies providing precise coarse grain management along with a simulation model with minimal overhead.
Methodology for Specification and Verification of High-Level Requirements with MetAcsl

Specification and formal verification of high-level properties (such as security properties, like data integrity or confidentiality) over a large software product remains an important challenge for the industrial practice. Recent work introduced METACSL, a plugin of the FRAMA-C verification platform, that allows the user to specify high-level properties, called HIgh-Level ACSL REquirements or HILARE, for C programs and transform them into assertions that can then be verified by classic deductive verification. This paper presents a methodology of specification and verification of a wide range of high-level properties with METACSL and illustrates it on several examples. The goal is to provide verification practitioners with detailed methodological guidelines for common patterns of properties in order to facilitate their everyday work and to avoid some frequent pitfalls. The illustrating examples are inspired by very usual kinds of properties and illustrated on two use cases. One of them-on the real-life code of the bootloader module of the secure storage device Wookey-was fully verified using the described approach, demonstrating its capacity to scale to real-life code. The other one-on a microkernel of an OS-was added to illustrate other common properties, where the description of the system was intentionally left very generic.
Intra-Class Cutmix for Unbalanced Data Augmentation

In the case of the training dataset suffering from heavy class-imbalance, deep learning algorithms may perform poorly. Due to the data-poor, the neural network cannot fully learn the representation of minority classes. In this paper, we proposed a data augmentation strategy called Intra-Class Cutmix for unbalanced datasets. Our algorithm can enhance the learning ability of neural networks for minority classes by mixing the intra-class samples of minority classes, and correct the decision boundary affected by unbalanced datasets. Although the method is simple, for unbalanced datasets, our method can be used as a supplement to traditional data augmentation methods (such as Randomerasing, Cutmix, etc.) to further enhance the performance of the network. In addition, Intra-Class Cutmix is also suitable for advanced re-balancing strategies. We conducted experiments on the CIFAR-10, CIFAR-100 and Fashion-MNIST datasets. Our results proved the effectiveness and universality of our method.
A Novel CNN-LSTM Hybrid Architecture for the Recognition of Human Activities

The problem of human activity recognition (HAR) has been increasingly attracting the efforts of the research community, having several applications. In this paper we propose a multi-modal approach addressing the task of video-based HAR. Our approach uses three modalities, i.e., raw RGB video data, depth sequences and 3D skeletal motion data. The latter are transformed into a 2D image representation into the spectral domain. In order to extract spatio-temporal features from the available data, we propose a novel hybrid deep neural network architecture that combines a Convolutional Neural Network (CNN) and a Long-Short Term Memory (LSTM) network. We focus on the tasks of recognition of activities of daily living (ADLs) and medical conditions and we evaluate our approach using two challenging datasets.
Overview of Privacy Protection Data Release Anonymity Technology

The collection of digital information by governments, companies and individuals creates tremendous opportunities for knowledge and information-based decision-making. Driven by mutual benefit and laws and regulations, there is a need for data exchange and publication between all parties. However, data in its original form usually contains sensitive information about individuals and publishing such data would violate personal privacy. Privacy Protection Data Distribution (PPDP) provides methods and tools to release useful information while protecting data privacy. In recent years, PPDP has received extensive attention from the research community, and many solutions have been proposed for different data release scenarios. How to ensure the availability of data under the premise of protecting user privacy is the core problem to be solved in this field. This paper studies the existing achievements of privacy protection data release anonymity technology, focusing on the existing anonymity technology in three aspects of high-dimensional, high-deficiency, and complex relational data, and analyzes and summarizes them.
A Dictionary-based Oversampling Approach to Clinical Document Classification on Small and Imbalanced Dataset

Medical document classification is one of the prominent research problems in document classification domain. As medical discharge notes are collected from real patients, they are often imbalanced. Moreover, these datasets are usually too small for data-hungry models (specially in rare disease cases). Both of these issues can lead to poor classification performance. In this work a new probabilistic dictionary-based data augmentation approach is proposed to address these issues by oversampling on the minority class. This method works by creating new documents with high variety by using the extracted synonyms from WordNet with awareness of synonyms' similarities with the original word. To verify the effectiveness of the proposed oversampling approach, three different machine learning methods are used to learn classifiers from the augmented clinical text datasets generated by the oversampling approach. The experimental results show that the proposed method not only provides better classification accuracy than the imbalanced dataset case, but also can outperform some existing augmentation methods on the dataset of 2008 Integrating Informatics with Biology and the Bedside (I2B2) obesity challenge.
Performance Comparison of SimBlock to NS-3 Blockchain Simulators

Blockchain is a technology that is in the hype and growing rapidly. Data that has entered the blockchain cannot be changed and tampered with, and this feature makes blockchain technology guaranteed in its integrity and security. However, evaluating a blockchain network configuration, consensus, design, or architecture has its problems due to the vast area of the blockchain network and the massive number of nodes. A simulator is the right choice for this case. Blockchain simulators that exist today include SimBlock and NS-3 blockchain simulator from Arthur Gervais. This study compares the two simulators' performance in terms of CPU, memory, and simulation time usage. Equalize all the two simulators' parameters, using the same configuration for the virtual machines used. After that, calculate the CPU and memory usage using the Sysstat program on Ubuntu, while the simulation time is taken from both simulator's output. This simulator evaluated by varying the target block from 500 to 5000 with 1000 nodes on the network. The experiment result of SimBlock CPU usage was ± 10% higher than NS-3, while SimBlock memory usage was also ± 14% greater than NS-3. The result of NS-3 simulation time requires the highest time of 55188.3s, while Simblock only takes 68.242s for the same number of blocks and nodes. The difference in CPU power is because NS-3 simulates the mining process. In contrast, the SimBlock mining process is not simulated but calculated using the user input value on the average mining power.
Training and Evaluation of Word Embedding Models for Azerbaijani Language

Recently, natural language representation models have attracted an increasing amount of attention from researchers. Various approaches have been proposed for learning these continuous vector representations. In this work, we will analyze the effectiveness of various word embeddings learning approaches for Azerbaijani language. Mainly, we will concentrate on two methodologies: (1) word2vec and (2) GloVe. We have trained both models on the text corpus of cleaned, Azerbaijani news articles and parsed books. Moreover, we have created intrinsic analogy tasks as introduced by Mikolov et al. for Azerbaijani. For the evaluation of word vector models in Azerbaijani, the intrinsic analogy tasks, as well as, two separate extrinsic evaluation tasks are performed. This work is one of the initial reports on the evaluation of word embeddings on intrinsic as well as extrinsic evaluation tasks for Azerbaijani, which is a low resource, agglutinative language.
Private DNA Sequencing: Hiding Information in Discrete Noise

When an individual's DNA is sequenced, sensitive medical information becomes available to the sequencing laboratory. A recently proposed way to hide an individual's genetic information is to mix in DNA samples of other individuals. We assume these samples are known to the individual but unknown to the sequencing laboratory. Thus, these DNA samples act as "noise" to the sequencing laboratory, but still allow the individual to recover their own DNA samples afterward. Motivated by this idea, we study the problem of hiding a binary random variable X (a genetic marker) with the additive noise provided by mixing DNA samples, using mutual information as a privacy metric. This is equivalent to the problem of finding a worst-case noise distribution for recovering X from the noisy observation among a set of feasible discrete distributions. We characterize upper and lower bounds to the solution of this problem, which are empirically shown to be very close. The lower bound is obtained through a convex relaxation of the original discrete optimization problem, and yields a closed-form expression. The upper bound is computed via a greedy algorithm for selecting the mixing proportions.
Color image analysis with novel trinion decimal-order polar harmonic transforms

Polar harmonic transforms (PHTs) are a kind of continuous orthogonal moments (COMs) that have excellent image description capability. To improve the anti-noise and reconstruction performance of PHTs, integer-order PHTs are generalized to order by a set of novel decimal-order polar harmonic transforms (DPHTs). On this basis, in combination with trinion theory, the DPHTs are extended to trinion decimal-order polar harmonic transforms (TDPHTs) applicable to color images. The color image is processed as a whole, and the internal relations among three components of color images are fully reserved and utilized. Compared with quaternion color image processing, trinion color image processing effectively avoids information redundancy and improves computational efficiency. Color image reconstruction and zero-watermarking algorithm experiments show that TDPHTs have excellent color image description ability and robustness.
RMCA-LSA: A Method of Monkey Brain Extraction

The traditional level set algorithm selects the position of the initial contour randomly and lacks the processing of edge information. Therefore, it cannot accurately extract the edge of the brain tissue. In order to solve this problem, this paper proposes a level set algorithm that fuses partition and Canny function. Firstly, the idea of partition is fused, and the initial contour position is selected by combining the morphological information of each region, so that the initial contour contains more brain tissue regions, and the efficiency of brain tissue extraction is improved. Secondly, the canny operator is fused in the energy functional, which improves the accuracy of edge detection of rhesus monkey brain tissue while retaining the advantage of the traditional level set algorithm in processing an uneven gray image. Experimental results show that the algorithm can accurately extract the brain tissue of rhesus monkeys with an accuracy of up to 86%.
Bottleneck Feature Extraction in Punjabi Adult Speech Recognition System

In this paper, the bottleneck feature extraction technique with MLP is used on Punjabi adult speech recognition. Nowadays, neural networks are most widely used approaches for training and testing the system. It helps to recognize the back probabilities among various phoneme set. This input info includes at some point get wrapped, and it becomes difficult to prepare them on Hidden Markov Model (HMM) based state-of-the-art synthesis. Here, context-based model is trained on Deep Neural Network (DNN) and after that on Bottleneck-Neural Network (BN-NN) system with the use of Multi-layer Perceptron (MLP). The baseline ASR is performed with different environment conditions on different modelling system. To improve the performance of a system, MLP-based supervised learning method utilizing for adjoining voice outlines related data to change the design of profound neural system DNN by extracting the bottleneck features. Finally, the MLP are used as input for the DNN-HMM and BN-NN state-of-the-art system. This paper presents the larger improvement obtained by applying the MLP feature vector with the relative improvements of 4.03% which is achieved on the Punjabi ASR with varying the several attributes associated with BN-NN and DNN-HMM modelling approaches.