Reviewerly/exHarmony huggingface dataset:
Joint approach based quality assessment scheme for compressed and distorted images

The Digital era is improving day by day. We can easily send and receive multimedia data, but it is challenging to know that the data is of actual quality or degraded and compressed. A similar problem arises in the medical imaging domain; it is too tedious to determine whether the image has a particular quality level or not to modify it further. So here we represent one specific method that is termed as “Joint”- Image Quality Estimation Approach as it is a combination of reference-based and no-reference-based Image Quality assessment methods; due to this fact, we termed it “Joint” approach. In some cases, the reference-based image quality assessment methods cannot predict the exact values because we don't know that the reference image that is considered to find the quality of a test image is an actual one or previously compressed. So, this will create a situation where we get the wrong IQA value for the test image. The method proposed by us can overcome this problem. First, we decide the quality of the reference image by using No-reference-based models. Then, we check the final IQA value for a test image with the reference-based models. We created a database of 72 chest images of COVID-19 infected patients and its four-level compressed images for the experiment. Results that are shown in this work are very effective and elaborated with proper justifications.
Estimating the degree of conflict in speech by employing Bag-of-Audio-Words and Fisher Vectors

The automatic detection of conflict situations from human speech has several straightforward applications such as the surveillance of public spaces, providing feedback about employees in call centers, and other roles in human–computer interactions. In this study we examine the potential of different state-of-the-art feature extraction techniques, all developed to be able to efficiently represent a variable-length speech utterance by a fixed-length feature vector. Besides the ‘ComParE functionals’ attribute set, which became the de facto standard feature set in the area of computational paralinguistics (which focuses on the automatic assessment of non-verbal phenomena being present in human speech), we experiment with two methods introduced quite recently: Bag-of-Audio-Words (BoAW) and Fisher Vectors (FV). Using three standard basic, low-level feature sets, we found that, while BoAW proved to be quite sensitive to its meta-parameter settings, with Fisher Vectors we were able to achieve state-of-the-art conflict intensity estimation performance on a public and widely-used corpus. Furthermore, by applying Principal Component Analysis on the frame-level attributes, we managed to achieve a 30% speed-up in the feature extraction step. Interestingly, in contrast with our previous paralinguistic studies, combining the different predictions with these feature extraction approaches, we were unable to achieve any further significant improvement. The highest correlation coefficient values we got on the test set lay in the range 0.850–0.860, while the authors of several previous studies were able to achieve similar values (i.e. 0.849, 0.856 and 0.853). Considering that in this task the target score to be estimated (i.e. the intensity of the conflict being present in the actual clip) is definitely prone to subjectivity and therefore to label noise, current efforts have probably achieved the highest correlation coefficients attainable, and match human performance.
A List-Ranking Framework Based on Linear and Non-Linear Fusion for Recommendation from Implicit Feedback

Although most list-ranking frameworks are based on multilayer perceptrons (MLP), they still face limitations within the method itself in the field of recommender systems in two respects: (1) MLP suffer from overfitting when dealing with sparse vectors. At the same time, the model itself tends to learn in-depth features of user-item interaction behavior but ignores some low-rank and shallow information present in the matrix. (2) Existing ranking methods cannot effectively deal with the problem of ranking between items with the same rating value and the problem of inconsistent independence in reality. We propose a list ranking framework based on linear and non-linear fusion for recommendation from implicit feedback, named RBLF. First, the model uses dense vectors to represent users and items through one-hot encoding and embedding. Second, to jointly learn shallow and deep user-item interaction, we use the interaction grabbing layer to capture the user-item interaction behavior through dense vectors of users and items. Finally, RBLF uses the Bayesian collaborative ranking to better fit the characteristics of implicit feedback. Eventually, the experiments show that the performance of RBLF obtains a significant improvement.
Professional Development of Teachers of Preschool Education Institutions and Distance Learning: Advantages, Difficulties and Prospects

In times of fast-changing information, in order to keep teachers’ knowledge and skills up to date and to comply with one of the tenets of sustainable development – lifelong learning – specialists in the sector should develop professionally. This will not only meet professional needs for new information, knowledge and skills, help teachers to perform their professional duties effectively in a rapidly changing environment, but will also help them to be competitive in the labour market. The purpose of this study was to investigate the advantages of applying distance learning for the professional development of preschool teachers, to identify the challenges that specialists of this industry face in the process of distance learning, and to determine the prospects for the development and improvement of this form of education. In the course of the study the authors used a set of general scientific theoretical and empirical research methods, such as analysis, statistical data processing, synthesis, surveying and generalisation. The key advantages of distance learning are highlighted, including individuality, flexibility, asynchrony, openness and self-organisation. It has been revealed that the remote format of education today is characterised by a number of problematic aspects in the implementation process, in particular the insufficient level of technical equipment, the lack of an adequate competence component in the professional training of teachers and the disruption of communication between the subjects of the educational process. It has been found that the majority of postgraduate institutes have courses designed for the professional development of preschool teachers and use both full-time and distance learning. The likelihood of technological, organisational and pedagogical difficulties for the trainees of the upgrade training courses has been determined. The study revealed that most teachers are generally satisfied with this form of training, but not all are ready to implement it in their work. The practical value of the study consists in summarising the characteristic features of distance education on the basis of theoretical analysis and questionnaires
Machine learning based computer aided diagnosis system for classification of breast masses in mammograms

Abstract Breast cancer continues to be the most common cancer in the fastest developing and the developed nations. Early detection by using mammography has been proven as the best prognosis. Computer Aided Diagnosis (CAD) systems are being used as second reader for the analysis and interpretation of mammogram images. In the last two decades, although breast cancer incidence has increased by many folds but unfortunately the progress in this field has almost stagnated. Therefore, the CAD systems need to be improved to be considered useful. In this study, a machine learning based CAD system for segmentation and classification of breast masses have been proposed. The IRMA Version of DDSM dataset has been used for experimentation and evaluation of the proposed system. Exact breast masses were segmented from manually extracted ROIs of 700*700 pixels by employing an improved seeded region growing algorithm. Various geometry and texture features were computed from the segmented mass lesions and corresponding ROIs respectively. The classification performances of nine state-of-the-art classifiers namely K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Gaussian Mixture Model (GMM), Multi-class Support Vector Machine (mSVM), Decision Tree (DT), Discriminate Analysis (DA), Naive Bayes (NB), Random Forest (RF), Ensemble Tree (ET) have been investigated in this study. On evaluating the experimental results for all the classifiers, highest classification accuracy is obtained with SVM classifier. The experimental results reveal that the proposed improved seeded region growing approach has been proven helpful in improving the classification performance of the proposed system.
Speech Augmentation Based Unsupervised Learning for Keyword Spotting

In this paper, we investigated a speech augmentation based unsupervised learning approach for keyword spotting (KWS) task. KWS is a useful speech application, yet also heavily depends on the labeled data. We designed a CNN-Attention architecture to conduct the KWS task. CNN layers focus on the local acoustic features, and attention layers model the long-time dependency. To improve the robustness of KWS model, we also proposed an unsupervised learning method. The unsupervised loss is based on the similarity between the original and augmented speech features, as well as the audio reconstructing information. Two speech augmentation methods are explored in the unsupervised learning: speed and intensity. The experiments on Google Speech Commands V2 Dataset demonstrated that our CNN-Attention model has competitive results. Moreover, the augmentation based unsupervised learning could further improve the classification accuracy of KWS task. In our experiments, with augmentation based unsupervised learning, our KWS model achieves better performance than other unsupervised methods, such as CPC, APC, and MPC.
Design of uninterruptible patrol system for electric power equipment based on digital twin technology

In order to improve the effect of uninterruptible patrol of power equipment and ensure the rapid response of patrol results, the uninterruptible patrol system of power equipment is optimized by using digital twin technology. In the hardware system, patrol workstation and mobile terminal are refitted. S3C2416 core is used to replace the core components of traditional ARM controller. Radio frequency identification units are added, and some connection circuits of the system are also adjusted. Under the support of hardware and database, the digital twin power equipment working model is built. The patrol data can be collected and transmitted in real time in this model. Experiment shows that the patrol results are closer to the actual working state of the power equipment, and it has obvious advantages in response time and concurrency number.
Educational Desktop for Remote Modeling of Wireless Computer Networks subject to Constraints

The educational resource considered in this article is a remote access workplace designed to study the structures of computer networks based on combined combinatorial block design, taking into account regular or irregular constraints on connections, as well as key systems based on combinatorial block diagrams, taking into account structural constraints and restrictions on the use of keys. A computer network based on the combined combinatorial block design has internal subnets corresponding to its blocks and external subnets corresponding to its dual blocks. The structures of these networks are specified by regular constraints. Random constraints correspond to possible failures of communications or failures of computers. A computer network equipped with a key system based on a combined combinatorial block design has key pools of nodes, the composition of which corresponds to blocks, while dual blocks include numbers of nodes containing the corresponding keys. Structural restrictions correspond to the exclusion of individual nodes, and the restrictions of the key system consist in the exclusion of individual keys from all node pools (if the key is compromised) or from individual pools (if it is not installed in them). The listed types of restrictions are typical for wireless sensor networks and their key systems. The educational resource allows you to remotely simulate processes in computer networks of both types, taking into account the specified restrictions. Such laboratory workshops play an important role and are an obligatory part of the educational process in the professional training of masters in applied mathematics and informatics.
DefendR - An Advanced Security Model Using Mini Filter in Unix Multi-Operating System

DefendR is a Security operation used to block the access of the user to edit or overwrite the contents in our personal file that is stored in our system. This approach of applying a certain filter for the sensitive or sensitive data that are applicable exclusively in read-only mode. This is an improvisation of security for the personal data that restricts undo or redo related operations in the shared file. We use a mini-filter driver tool. Specifically, IRP (Incident Response Plan)-based I/O operations, as well as fast FSFilter callback activities, may additionally all be filtered with a mini-filter driver. A mini-filter can register a preoperation callback procedure, a postoperative Each of the I/O operations it filters is filtered by a callback procedure. By registering all necessary callback filtering methods in a filter manager, a mini-filter driver interfaces to the file system indirectly. When a mini-filter is loaded, the latter is a Windows file system filter driver that is active and connects to the file system stack
Quantum Optimal Control: Practical Aspects and Diverse Methods

Quantum controls realize the unitary or nonunitary operations employed in quantum computers, quantum simulators, quantum communications, and other quantum information devices. They implement the desired quantum dynamics with the help of electric, magnetic, or electromagnetic control fields. Quantum optimal control (QOC) deals with designing an optimal control field modulation that most precisely implements a desired quantum operation with minimum energy consumption and maximum robustness against hardware imperfections as well as external noise. Over the last 2 decades, numerous QOC methods have been proposed. They include asymptotic methods, direct search, gradient methods, variational methods, machine learning methods, etc. In this review, we shall introduce the basic ideas of QOC, discuss practical challenges, and then take an overview of the diverse QOC methods.
CCgen: Injecting Covert Channels into Network Traffic

Covert channels are methods to convey information clandestinely by exploiting the inherent capabilities of common communication protocols. They can be used to hide malware communication as part of cyber attacks. Here, we present CCgen, a framework for injecting covert channels into network traffic that includes modules for common covert channels at the network and transport layer and allows a smooth integration of novel covert channel techniques. Our tool—openly available and implemented in Python—enables the operation on-the-fly in live communications as well as the manipulation of network traffic packet captures. We evaluate a first prototype by generating a varied assortment of covert channels based on state-of-the-art techniques and check their detectability with Suricata, a popular, open-source intrusion prevention and detection system. The injected covert channels remain mostly undetected. Our proposal fills a gap within the diversity of openly available tools for cybersecurity research and education. It builds a flexible environment for experts to test analysis algorithms, thus also enabling advanced training in applied network steganography.
Parallel‐branch network for 3D human pose and shape estimation in video

Abstract Human pose and shape estimation have developed rapidly, where a skinned multi‐person linear (SMPL) approach performs excellent recently. However, the prior template of the human body in the SMPL model is fixed, thus a deviation may be resulted in the reconstructed body shape if a human body acts sharp movements such as sporting or dancing. To address this problem, we propose a parallel‐branch network including a designed spatial–temporal (ST) branch and a SMPL branch. The ST branch essentially performs the 2D‐to‐3D lifting for more accurate joint prediction, by the designed spatial transformer and temporal transformer. The 3D joints from the ST branch are used to supervise the 3D joints from the SMPL branch and further correct the deviation of the SMPL model. Experiments on some popular benchmarks like 3DPW and MPI‐INF‐3DHP show that our method has better performance than other methods with video input. Our code is available at https://automation.seu.edu.cn/wcx/list.htm
A spatial augmented reality based circuit experiment and comparative study with the conventional one

Abstract How to intuitively illustrate phenomena while retaining good operation experiences is a key issue in experimental learning. Virtual experiments with desktop environments, handheld devices, or headsets can show invisible phenomena for students. However, they are either visuo‐tactile inconsistent in space or with heavy physical burdens, causing bad experiences. A spatial augmented reality (SAR) based circuit experiment is developed. It allows students to interact with 3D (three dimensional) printed tangible objects without wearing any devices, having low physical burdens. Objects' poses are tracked using Microsoft Azure Kinect and inertial measurement unit. Virtual phenomena are projected onto the tangible objects and tabletop accordingly. Physical input and virtual output space are completely fused from students' view. It also offers efficient operation manners to students. A questionnaire comparing user experiences between the SAR and conventional experiment reveals that the former has a better learning experience.
ActiveSense: A Novel Active Learning Framework for Human Activity Recognition

One of the persistent challenges in building machine-learned models for mobile health applications of fine-grained activity is the generation of accurate annotations with well-defined start/end time labels. Large amounts of unlabeled data exist, and annotation is often labor-intensive and costly. Moreover, it is not clear whether labeling all the data is even necessary to building the most effective machine-learned model. Active learning approaches harness model uncertainty by selecting the most informative samples, reducing the time and effort in labeling unnecessary segments of the data. Model uncertainty, however, is strongly linked to classifier performance, introducing bias in sample selection and impacting model generalizability. In this paper, we propose and study the effects of a new active learning framework on the Necksense dataset which harnesses intrinsic uncertainty as well as model uncertainty by utilizing the Area Under the Margin (AUM) statistic, leading to a significant reduction in the number of samples needed to annotate. We also show that we are able to design a more generalizable model training on 0.15% (n=192 samples) of the data compared to the original model trained on 85% (n=104,681 samples) of the data.
Threat Matrix: A Fast Algorithm for Human–Machine Chinese Ludo Gaming

Chinese Ludo, also known as Aeroplan Chess, has been a very popular board game for several decades. However, there is no mature algorithm existing for human–machine gambling. The major challenge is the high randomness of the dice rolls, where the algorithm must ensure that the machine is smarter than a human in order to guarantee that the owner of the game machines makes a profit. This paper presents a fast Chinese Ludo algorithm (named “Threat Matrix”) that we have recently developed. Unlike from most chess programs, which rely on high performance computing machines, the evaluation function in our program is only a linear sum of four factors. For fast and low-cost computation, we innovatively construct the concept of the threat matrix, by which we can easily obtain the threat between any two dice on any two positions. The threat matrix approach greatly reduces the required amount of calculations, enabling the program to run on a 32-bit 80 × 86 SCM with a 100 MHz CPU while supporting a recursive algorithms to search plies. Statistics compiled from matches against human game players show that our threat matrix has an average win rate of 92% with no time limit, 95% with a time limit of 10 s, and 98% with a time limit of 5 s. Furthermore, the threat matrix can reduce the computation cost by nearly 90% compared to real-time computing; memory consumption drops and is stable, which increases the evaluation speed by 58% compared to real-time computing.
Noise-Tolerant Zeroing Neural Dynamics for Solving Hybrid Multilayered Time-Varying Linear Equation System

Resource allocation problem in a wireless sensor network can be formulated as time-varying optimization, which can be further converted as time-varying linear equation system (TVLES). Hybrid multilayered time-varying linear equation system (HMTVLES) involving hybrid multilayers and time-variation characteristic is a complicated and challenging problem. Recently it has been solved by zeroing neural dynamics (ZND) method under ideal conditions, i.e., without noises. However, noises are ubiquitous, immanent and unavoidable in real-time systems. In this work, we propose a noise-tolerant zeroing neural dynamics (NTZND) model for solving HMTVLES. It can deal with different kinds of noises such as constant noise, linear-increasing noise, and random noise. Theoretical analyses guarantee the precision of NTZND model in the presence of different kinds of noises. In addition, a general NTZND model is proposed based on general activation function. Besides, classical ZND method and gradient neural dynamics (GND) method are also investigated and compared. Numerical experimental results are presented to verify the theoretical results of proposed models.
CBNet: Demand-aware tree topologies for Reconfigurable Datacenter Networks

This paper studies the design of demand-aware network topologies: networks that dynamically adapt themselves toward the demand they currently serve, in an online manner. While demand-aware networks may be significantly more efficient than demand-oblivious networks, frequent adjustments are still costly. Furthermore, a centralized controller of such networks may become a bottleneck. We present CBNet (Counting-Based self-adjusting Network), a demand-aware network that relies on a distributed control plane supporting concurrent adjustments, while significantly reducing the number of reconfigurations. CBNet comes with formal guarantees and is based on concepts of self-adjusting data structures. We evaluate CBNet analytically and empirically and we find that CBNet can effectively exploit locality structure in the traffic demand.