Reviewerly/exHarmony huggingface dataset:
Identity-Aware Contrastive Knowledge Distillation for Facial Attribute Recognition

Facial attribute recognition (FAR) is an important and yet challenging multi-label learning task in computer vision. Existing FAR methods have achieved promising performance with the development of deep learning. However, they usually suffer from prohibitive computational and memory costs. In this paper, we propose an identity-aware contrastive knowledge distillation method, termed ICKD, to compress the FAR model. A nonlinear weight-sharing mapping (NWSM) mechanism is firstly designed to avoid the difficulty of directly matching features of the teacher and student networks due to the lower representation ability of the student network. Furthermore, an identity-aware contrastive distillation (ICD) loss is employed to guide the student network to effectively learn the mutual relations between samples with multiple attributes. In addition, an adjustable ladder distillation (ALD) loss is developed to automatically adjust the importance of different distillation points with the progress of training. Extensive experiments demonstrate that our method can significantly improve the performance of student networks and outperforms the existing FAR methods on the public challenging datasets.
Information theoretic measures on the two-photon transitions of hydrogen atom embedded in weakly coupled plasma environment

Abstract The quantum information theoretic measures in terms of Shannon entropy and Fisher entropy (both in position and momentum spaces) on the ground, excited as well as virtual states arising out of the two-photon transitions (1 s → nl ; n = 2 − 4, l = 0, 2) of H atom embedded in classical weakly coupled plasma environment are done for the first time. Fourth order time dependent perturbation theory is adopted within a variational framework for calculating the two photon excitation energies and their respective wavefunctions from an analysis of the pole positions of the non linear response of the system. The representation of virtual state follows from an analysis of the linear response at such poles using a novel method developed by us. Ground and perturbed state wave functions of appropriate symmetries are represented by linear combination of Slater-type orbitals. The analytic form of the momentum space wave functions of ground, excited and virtual states are determined by taking Fourier transformation of the respective position space wave functions. The quantum information measures give interesting insights on the delocalization patterns of the all the real and virtual states under question w.r.t. the increase in plasma strength. The estimated data values are found to be in excellent agreement with the few existing in literature for the ground as well as excited states participating in the two-photon transitions. Such data for the virtual states are completely new and can be set as benchmark for future works in related disciplines.
Corrigendum to “The regression models for lifelong learning competencies for teacher trainers”

[This corrects the article DOI: 10.1016/j.heliyon.2023.e13749.].
Which Classifier to Deploy? A Hybrid Sampling Approach for Evaluating Classifiers with Unbalanced Data in Medical Expert Systems

To develop medical expert systems (MES), researchers and practitioners usually apply a machine learning (ML) classifier they expect to be the best through ML effect estimation. A standard classifier can perform well on balanced datasets, but its performance often dramatically decreases when applied to unbalanced datasets. To address these challenges, this paper proposes a hybrid sampling approach for evaluating classifiers with unbalanced data in MES. In this framework, the mean responses of the classification experiments are employed to estimate the ML effect. Meanwhile, uniform designs are used in hybrid sampling (HSUD) to retrieve the optimal parameters. The efficacy of HSUD is validated through applications on six real-world clinical datasets (unbalanced) using six ML algorithms (including zero rule). The ratio of the minority to the majority ranges from 0.09 to 0.80 in the six datasets, and the sample size ranges from 187 to 15151. The results suggest that our approach is more effective than existing unbalanced data learning methods. In particular, the AUC with HSUD is 2.48% and 8.60% higher than those with proportional and balanced sampling methods in the testing, respectively. Moreover, HSUD is efficient in choosing classifiers with unbalanced data in MES.
Exponential unitary integrators for nonseparable quantum Hamiltonians

Quantum Hamiltonians containing nonseparable products of non-commuting operators, such as $\hat{\bf x}^m \hat{\bf p}^n$, are problematic for numerical studies using split-operator techniques since such products cannot be represented as a sum of separable terms, such as $T(\hat{\bf p}) + V(\hat{\bf x})$. In the case of classical physics, Chin [Phys. Rev. E $\bf 80$, 037701 (2009)] developed a procedure to approximately represent nonseparable terms in terms of separable ones. We extend Chin's idea to quantum systems. We demonstrate our findings by numerically evolving the Wigner distribution of a Kerr-type oscillator whose Hamiltonian contains the nonseparable term $\hat{\bf x}^2 \hat{\bf p}^2 + \hat{\bf p}^2 \hat{\bf x}^2$. The general applicability of Chin's approach to any Hamiltonian of polynomial form is proven.
Parameters of some BCH codes over $ \mathbb{F}_q $ of length $ \frac{q^m-1}{2} $

BCH codes, known for their error-correcting capability and efficient encoding and decoding algorithms, are an important class of cyclic codes over finite fields.Currently, BCH codes have been widely applied in consumer devices, communication systems, and data storage systems.However, there is still a lot of unsolved work in exploring the parameters of BCH codes, such as the dimensions and minimum distances.The objective of this paper is to study narrow-sense BCH codes of length -1 2 over F .The parameters of narrow-sense BCH codes of such length with designed distances+ -1 2 are presented, respectively, where 1 ≤ ≤ ⌊ +6 4 ⌋ and 2 ≤ ≤ 4.
LBDTM: Lightweight Blockchain with Dynamic Trust Management

Blockchain technology is increasingly being used in situations that call for DECENTRALIZED trustworthiness due to its decentralized, traceable and tamper-proof characteristics. However, existing blockchain systems are power-intensive and low-throughput, and it is challenging to protect against malicious internal node attacks. All these problems limit the application of blockchain in more scenarios. To address these challenges, we propose a blockchain system based on trust management. We design a trust management model for the blockchain system which can evaluate the trustworthiness of nodes and assign dynamic initial values of trust. In order to guarantee system security and transaction efficiency simultaneously, we propose an improved Raft consensus algorithm. The experimental results and analysis show that the trust management model and improved Raft algorithm is helpful to improve safety and efficiency of the blockchain system.
Underwater image enhancement based on zero-shot learning and level adjustment

Light is scattered and partially absorbed while traveling through water, hence, underwater captured images often exhibit issues such as low contrast, detail blurring, color attenuation, and low illumination. To improve the visual performance of underwater imaging, herein, we propose a two-step method of zero-shot dehazing and level adjustment. In the newly developed approach, the original image is fed into a “zero-shot” dehazing network and further enhanced by an improved level adjustment methodology combined with auto-contrast. By conducting experiments, we then compare the performance of the proposed method with six classical state-of-the-art methods. The qualitative results confirm that the proposed method is capable of effectively removing haze, correcting color deviations, and maintaining the naturalness of images. We further perform a quantitative evaluation, revealing that the proposed method outperforms the comparison methods in terms of peak signal-to-noise ratio and structural similarity. The enhancement results are also measured by employing the underwater color image quality evaluation index (UCIQE), indicating that the proposed approach exhibits the highest mean values of 0.58 and 0.53 on the two data sets. The experimental results collectively validate the efficiency of the proposed methodology in enhancing underwater blurred images.
GITAAR-GIT based Abnormal Activity Recognition on UCF Crime Dataset

Data and computer science advancements in recent years have greatly benefited people’s day-to-day lives. On the other hand, criminals are embracing new technologies to bolster and broaden their operations. There is a great deal of promise in applying the Deep Learning (DL) paradigm to the analysis of highly structured data. However, the availability of public datasets in the crime detection area is low and task-specific, making it difficult to study and develop DL-assisted solutions that are both accurate and robust. This study aims to adapt the popular UCF-crime dataset for use with video subtitling and propose a hybrid model GITAAR (Generative Image-totext Transformer for abnormal activity recognition), a new transformer-architecture for video-caption-generation. In this paper, UCF crime dataset that compares a recently suggested video captioning system against a large number of state-of-theart methods, describing both the qualitative and quantitative aspects.
A Fuzzy Inference and Posture Detection Based Soldering Motion Monitoring System

Recently, governments are promoting the employment of persons with disabilities, and employers must ensure safety in the workplace. On the other hand, a single manager has difficulty in preventing accidents and injuries caused by human error. In addition, in order to learn soldering techniques, persons with disabilities need to repeat the same process. In this paper, we present a web system for depth camera based soldering motion monitoring. Experimental results show the danger level of soldering motion based on fuzzy inference. We also implement the web system and show the visualization results of the soldering worker state. From experimental results, the proposed system is able to monitor the soldering workers.
Concurrent multipath quantum entanglement routing based on segment routing in quantum hybrid networks

In the future, quantum networks will be an important development direction. Quantum networks should be studied considering quantum's unique physical properties. In quantum networks with limited resources, decoherence will lead to qubits loss, which restricts the time that data qubits can be stored in memory before use. To realize the network function, it is very important to transmit data qubits as quickly as possible. In order to ensure timeliness and make use of entanglement resources as much as possible, this paper proposes a concurrent multipath entanglement routing scheme based on segment routing for quantum hybrid networks. In this scheme, centralized routing is used to calculate the “segments” of quantum relay path, and distributed routing is used to transmit control messages between the “segments”. The “concurrent” mechanism is fully adopted in classical messages passing, path nodes BSM (Bell State Measurement) execution and multipath shunt transmission. This scheme has obvious advantages in multipath concurrent transmission, traffic diversion, reducing transmission time and providing redundancy.
TRO-CP-ABE: a secure and flexible layer with traceability and easy revocation in ciphertext-policy attribute-based encryption

Attribute-based encryption (ABE) is a very efficient way of authorising users to access confidential data in organisations without public-key validation from external trusted authorities or complex login processes. Ciphertext-policy attribute-based encryption (CP-ABE) is an improvement over ABE in which access is granted to users based on their attributes if they satisfy the access policy of the ciphertext defined by the data owner. CP-ABE poses many challenges like tracing the malicious user, revoking the access of users, collusion attacks: where users may combine keys to gain access to unauthorised data, and key escrow: when authorities have complete access to protected data due to saved private keys. Our proposed solution is an added security layer on top of the underlying CP-ABE which solves the four security concerns mentioned above. The TRO-CP-ABE system also gives great flexibility to the system designer on the use of key generation algorithms and access structures as required.
Title Pending 9164

Agreement systems often allow alternatives: This family has/have lost everything. Therefore typology requires a means for generalizing over them. Instances like PLURAL live are frequently termed ‘semantic agreement’ (vs. ‘syntactic agreement’ for SINGULAR lives), but this notion has proved difficult. The challenge is to encompass the full typological range of alternative agreements. These include the core instances: (i) hybrid nouns like family; and (ii) constructional mismatches, such as conjoined nominal phrases, but also less obvious phenomena: (iii) split hybrids where neither alternative is straightforwardly semantic, both appear related to form, and (iv) examples like Scandinavian ‘pancake sentences’, which stretch semantic agreement towards pragmatics. These different types are comparable in that (i) the alternatives are realized by the normal agreement forms; and (ii) they are subject to the Agreement Hierarchy. Hence they demand a common treatment. To achieve this, I first unpack the Agreement Hierarchy constraint into the agreement target positions and the directionality implied by ‘semantic agreement’. I show how the latter arises from mismatches between the agreement information available from different sources. In the core instances, the information from one source is, typically, more evidently semantic than the other. But in other instances, this is less clear. I argue that it is more parsimonious to treat these less obvious phenomena as falling under the constraint of the Agreement Hierarchy, and as being part of the pattern of a Hierarchy of Agreement Sources, which gives different degrees of ‘generalized semantic agreement’. This reworking offers a more robust underpinning to the Agreement Hierarchy, and fits into a current trend in typology. A typology that works is no longer sufficient: we examine and justify the defining criteria, and relate them to the underlying attributes of the domain.
Hybrid synthetic data generation pipeline that outperforms real data

Fine-tuning a pretrained model with real data for a machine learning task requires many hours of manual work, especially for computer vision tasks, where collection and annotation of data can be very time-consuming. We present a framework and methodology for synthetic data collection that is not only efficient in terms of time taken to collect and annotate data, making use of free- and open-source software tools and 3D assets but also beats the state-of-the-art against real data, which is the ultimate test for any similar-to-real approach. We test our approach on a set of image classes from ObjectNet, which is a challenging image classification benchmark test dataset that is designed to be similar in many respects to ImageNet but with a wider variety of viewpoints, rotations, and backgrounds, which can make it more difficult for transfer learning problems. The novelty of our approach stems from the way we create complex backgrounds for 3D models using 2D images laid out as decals in a 3D game engine, where synthetic images are captured programmatically with a large number of systematic variations. We demonstrate that our approach is highly effective, resulting in a deep learning model with a top-1 accuracy of 72% on the ObjectNet data, which is a new state-of-the-art result. In addition, we present an efficient strategy for learning rate tuning that is an order of magnitude faster than regular grid search.
A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy

Since the Fuzzy C-Means algorithm is incapable of considering the influence of different features and exponential constraints on high-dimensional and complex data, a fuzzy clustering algorithm based on non-Euclidean distance combining feature weights and entropy weights is proposed. The proposed algorithm is based on the Fuzzy C-Means soft clustering algorithm to deal with high-dimensional and complex data. The objective function of the new algorithm is modified with the help of two different entropy terms and a non-Euclidean way of computing the distance. The distance calculation formula enhances the efficiency of extracting the contribution of different features. The first entropy term helps to minimize the clusters' dispersion and maximize the negative entropy to control the clustering process, which also promotes the association between the samples. The second entropy term helps to control the weights of features since different features have different weights in the clustering process. Experiments on real-world datasets indicate that the proposed algorithm gives better clustering results than other algorithms. The experiments demonstrate the proposed algorithm's robustness by analyzing the parameters' sensitivity and comparing the computational distance formulas. In summary, the improved algorithm improves classification performance under noisy interference and high-dimensional datasets, increases computational efficiency, performs well in real-world high-dimensional datasets, and encourages the development of robust noise-resistant high-dimensional fuzzy clustering algorithms.
IoT enabled healthcare monitoring system using convolutional neural network

Recent development in the Internet of Things (IoT) based smart mobile healthcare monitoring provides better dimensionalities and smart services. The introduction of IoT-based wearable sensor devices circumvents the death of millions of people with regular monitoring and better treatments. This type of monitoring system also increases the data and it is necessary to store the data in any service-provided platform and the cloud is one among them all. The security of data is also a concern while transmitting the data. Further, the prediction of diseases from the collected data is also an intricate process. To address these issues, we propose a novel deep learning-based approach known as Convolutional Neural network-based disease prediction, and the security of the data stored in the cloud can be acquired by the Rivest-Shamir-Adleman (RSA) cryptography algorithm. The performance of our proposed work is analyzed with some state-of-art works. The comparative study shows that the proposed work achieves better security and also the prediction accuracy of our proposed is higher.
Importance of Coping with Cyber Security Challenges in E Commerce Business

E-commerce is a platform where customers and firms meet on the internet to buy and sell things. It is transforming the way people transact from the comfort of home at a mouse click. More and more people preferring online shopping, is indeed a boon for ecommerce sites. However, cybercrime is also on an exponential rise, imposing a major threat. Credit card frauds, innovative phishing attacks, bad bots are a few challenges and there is a plethora of others resulting in not only loss in revenue but reputational damage to the e-commerce organization. Confidence in digital security has been essentially eroded due to major data leaks. Cyber Security is like air, vital for existence. The foundation pillars of cyber security are confidentiality which ensures that the customer's critical information like bank card details is safe, integrity which implies maintaining the accuracy and reliability of data throughout the ecommerce site surfing and transaction activity, and availability of requested data 24*7 on the website. Ecommerce industry needs to keep cyber security challenges at the center of all management decisions to ensure continuity, resilience and agility of the ongoing processes. The major area of concern is data privacy-security of customers' data. The paper discusses and analyses various types of cyber-attacks on leading ecommerce sites, and vulnerabilities frequently affecting the e-commerce web applications and suggests mitigation measures based on current industry standards. This should lead to better and more comprehensive management decision support in the e-commerce industry.
Accuracy Improvement by Occurrence Probability of Service Identification based on SNI

Identifying the service from IP flows is beneficial for a variety of purposes, such as traffic priority control or achieving zero-rating services. Identification methods based on IP addresses and port numbers have limitations in accuracy, and methods based on inspection of packet payloads have been discussed. Since many recent packets are encrypted with TLS, it is important to study a method that can be applied to packets encrypted with TLS. As a method applicable for TLS encrypted data, a method based on the occurring SNI and Bayesian inference was proposed. This method searches the previously investigated occurrence SNI database for the SNI occurrence vector that perfectly matches the SNI occurrence vector of the IP flow to be identified. This method gives up identification if there is no occurrence SNI vector pair that perfectly matches. This giving up identification is considered to decrease its accuracy. In this paper, we propose a method for improving the accuracy by excluding SNIs that are not important for identification. This exclusion reduces giving up identification. We consider SNIs that have a low occurrence probability and SNIs that occur only in the traffic to be identified as non-important SNIs, and identify the service without these non-important SNIs. Our evaluation shows that the proposed method can achieve higher identification accuracy than existing methods.
Design tools to evaluate the topic of friction force in the direction of developing students’ quality and capacities

The article presents modern trends in testing and assessing learning outcomes in the direction of developing the quality and capacity of high school students. The basic signs of difference between assessing learners' ability and assessing learners' knowledge and skills are also studied and synthesized. A tool to test and evaluate the topic of frictional force in the direction of developing the quality and capacity of students in teaching Physics has been designed. Based on the experimental results of pedagogy at Tuong Duong 1 High School, the effectiveness of the proposed tools has been evaluated, thereby proposing solutions to improve the efficiency of testing and assessment in the direction of developing the quality and competence of students.