Reviewerly/exHarmony huggingface dataset:
Fine-grained Visual Classification by Progressive Training via Jigsaw Puzzle Permutation Learning

Fine-grained visual classification remains a challenging task due to small inter-class differences and large intraclass differences. Existing methods consider only local details of information or only global information at a certain stage. However, there methods do not consider the complementary relationship between local information of different granularity and global information of the overall object on the feature maps of different stages of the network. To solve the above problem, we propose a Progressively Trained Jigsaw Puzzle Permutation Learning network (PPL-Net). To obtain the local details of the object, we adopt a progressive training strategy to obtain the semantic information of the object. At different steps, we use different granularity of shuffled versions of the images as input. Then, the classification prediction is performed by fusing the feature representations of different granularities. We obtain the global information of the object by learning jigsaw puzzle visual permutation. Specifically, by introducing the Jigsaw Puzzle Solver module we explore the location information of the different granularity image patches. Recovery of the shuffled version of the image to the original image. Thus, at each step of the network, we supervise the network by complementary relationships between local information of different granularities and global information of the object. Experiments on three standard fine-grained classification datasets (CUB-200-2011, Stanford Cars, FGCV-Aircraft) show that the performance of our method exceeds that of most methods. The code will be available at https://github.com/Zhao-fan/PPL-Net.
Sentiment Analysis about Oximeter as Covid-19 Detection Tools on Twitter Using Sastrawi Library

Sentiment analysis is a technique used to determine whether the opinion statement is positive or negative. A positive opinion means that it tends to be liked and recommended, whereas a negative opinion indicates that it tends to be disliked and not recommended. An oximeter is a device used to check the level of oxygen in the blood. If the oxygen level in the blood is less than 95%, then there is a tendency for the person to be positive for Covid-19. This study tries to trace the sentiment about the oximeter on tweets in 2021 to get information on whether the majority of tweets on Twitter have a positive or negative opinion on the oximeter. The sentiment analysis model was compared between directly processing data in English with data converted to Indonesian at the pre-processing stemming and stopwords removal stages. Data in Indonesian is processed by applying the Sastrawi library. The results obtained are that converting data to Indonesian at the pre-processing stage has better accuracy in sentiment analysis.
Framework for Analysing a Policy-driven Multi-Tenant Kubernetes Environment

Kubernetes (K8s) is gaining adoption in cloud computing for container management, deployment automation, and resource scheduling. As K8s matures, with increased stability and scalability, it is important to study how it can be effectively customized for use in different application scenarios. The focus of our work is on studying one of its main core components, kube-scheduler, which is in charge of scheduling pods on worker nodes. The K8s default scheduler implements the First Come First Serve (FCFS) algorithm as the pods are ordered and sequenced for execution based on the timestamp of when tasks arrive, when no priority is set to the pods. In this paper, we present a Policy-driven Multi-Tenant K8s (PMK) framework to study how policies of multiple tenants on resource requests and job arrivals affect fairness for the tenants individually in terms of makespan, average waiting time, and average turnaround time. PMK allows re-sequencing of tasks, submitted by multiple tenants, via well-known or customized scheduling algorithms before they enter the K8s scheduling queue. Our evaluation uses well-known algorithms such as Round Robin (RR), FCFS and Dominant Resource Fairness (DRF). In addition, we introduce a Cluster-Based Fairness (CBF) scheduling algorithm, which is a variation of DRF. CBF considers overall cluster utilization and resource availability to determine which task to choose from new requests. Our analysis shows that PMK can provide insights to cluster and cloud infrastructure managers on the factors affecting fairness and accordingly in some cases obtain 61.0% improvement in average waiting time for tenants with homogeneous individual demands. In addition, our customized CBF scheduling policy, when used with with PMK on K8s, can reduce overall cluster average waiting time by up to 4%.
NFC-Blockchain Based COVID-19 Immunity Certificate: Proposed System and Emerging Issues

Vaccine requirements are becoming more mandatory in several countries as public health experts and governments become more concerned about the COVID-19 pandemic and its variants. In the meantime, as the number of vaccine requirements grows, so does the counterfeiting of vaccination documents. Fake vaccination certificates are steadily growing, being sold online and on the dark web. Due to the nature of the COVID-19 pandemic, there is a need of robust authentication mechanisms that support touch-less technologies like Near Field Communication (NFC). Thus, in this paper, a blockchain-NFC based COVID-19 Digital Immunity Certificate (DIC) system is proposed. The vaccination data are first encrypted by the Advanced Encryption Standard (AES) algorithm on Hadoop Distributed File System (HDFS) and then uploaded to the blockchain. The proposed system is based on the amalgamation of NCF and blockchain technologies which can mitigate the issue of fake vaccination certificates. Furthermore, the emerging issues of employing the proposed system are discussed with future directions.
Sharp Elements and Apartness in Domains

Working constructively, we study continuous directed complete posets (dcpos) and the Scott topology.Our two primary novelties are a notion of intrinsic apartness and a notion of sharp elements.Being apart is a positive formulation of being unequal, similar to how inhabitedness is a positive formulation of nonemptiness.To exemplify sharpness, we note that a lower real is sharp if and only if it is located.Our first main result is that for a large class of continuous dcpos, the Bridges-Vît ¸ǎ apartness topology and the Scott topology coincide.Although we cannot expect a tight or cotransitive apartness on nontrivial dcpos, we prove that the intrinsic apartness is both tight and cotransitive when restricted to the sharp elements of a continuous dcpo.These include the strongly maximal elements, as studied by Smyth and Heckmann.We develop the theory of strongly maximal elements highlighting its connection to sharpness and the Lawson topology.Finally, we illustrate the intrinsic apartness, sharpness and strong maximality by considering several natural examples of continuous dcpos: the Cantor and Baire domains, the partial Dedekind reals and the lower reals.
GrabCut Image Segmentation Based on Local Sampling

In traditional image segmentation, the GrabCut image segmentation algorithm is a popular and effective method. The current GrabCut image segmentation algorithm is based on the Gaussian mixture model of the global foreground and background of the image. Still, it cannot achieve good results when the foreground and background are similar. In this paper, we propose a method for local sampling of the foreground and background. This method samples the foreground and background around unknown pixels based on the distance. It has an advantage when the foreground and background are similar. The experimental results show that the GrabCut method with local sampling can achieve good results when many colors appear in the foreground and background at the same time.
Comparative study of Face Mask Recognition using Deep Learning and Machine learning classifiers

With around 194 million cases and around 4 million reported deaths, affecting 220 countries [1], Coronavirus (COVID-19) is still prevalent. Wearing facemasks in crowded areas is one of the undemanding and effective measures among the multitude of preventive guidelines provided by the World Health Organization (WHO). However, unruly humans are present; monitoring if people are wearing facemasks in dense areas is taxing and cumbersome. In this paper, we have experimented two ways of tackling facemask detection for comparison purposes: (1) by using transfer learning on four pretrained State-Of- The-Art (SOTA) models - Inception-V3, Resnet-50, VGG-16, and Densenet-121, (2) using these SOTA models as feature extractors and training ML classifiers (Support Vector Machine (SVM), Decision Tree, and Gaussian Naive Bayes) on them. Simulated Face Mask Dataset (SMFD) is used to train and validate all of the models, including data augmentation to enhance data samples. The SOTA models displayed exceptional validation accuracy (greater than 90%), with VGG-16 and ResNet-50 performing the best. Similarly, all combinations of SOTA-ML models have remarkable performance with the Densenet-121-SVM model obtaining highest accuracy with lesser training time.
Motion artifact reduction in abdominal MRIs using generative adversarial networks with perceptual similarity loss

The respiratory motion and the resulting artifacts are considered to be a big problem in abdominal Magnetic Resonance Imaging (MRI), often burdening hospitals with unnecessary cost of time and money. Many deep learning techniques have recently been developed for removing these respiratory motion artifacts. However, fine-details, such as the vessels in liver MRIs, are often over-smoothed by many models, while these details are most important for medical diagnosis. Therefore, more realistic-looking reconstructed images are needed. To achieve this goal, in this paper, we design a Generative Adversarial Networks (GAN)-based model, and use the perceptual loss as part of our training loss to further enhance the images’ perceptual quality. To ensure the proper convergence of the GAN model, we add a pre-training step before the adversarial training. In addition, due to lack of image pairs of the clean image and its corresponding motion distorted image, we develop a motion simulation method via the k-space to mimic the motion generated artifacts. Benefiting from all these methods, our proposed model achieves high perceptual quality with an MSE of 0.002, an SSIM of 0.942, a PSNR of 33.894 and a perceptual distance metric of 0.015. Finally, it generates motion artifact reduced images with clearer and better fine-details, thus providing to radiologists more realistic MRI images for improved diagnosis. The code is available at [Code for Motion Reduction].
A contrast improved OR and XOR based (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3720" altimg="si445.svg"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:math>) visual cryptography scheme without pixel expansion

Visual cryptography scheme (VCS) is a branch of secret sharing, in which a secret image is encrypted into n noise-like shares. In the recovering phase, the secret image can be reconstructed by stacking sufficient shares. It's obvious that VCS with multiple decryptions can further widen the range of application. However, the investigations on the existing scheme with multiple decryptions are not sufficient. In this paper, we develop a novel contrast improved OR and XOR based (k,n)-VCS without pixel expansion. Significantly, we give a general simplified calculation formula to compute the theoretical contrast of the proposed scheme. In addition, if there are no computing devices, then we can reconstruct the secret image by stacking the shares directly. Meanwhile, we recover the secret image perfectly by performing XOR operation when computing devices are available. Since the proposed scheme is based on the parity basis matrices, our scheme has no pixel expansion. Finally, Theoretical analysis and experimental results are conducted to evaluate the efficiency and security of the proposed scheme.
Arabic handwritten text line segmentation using a multi-agent system and a directed CNN

Segmentation of text lines is a critical phase in Arabic handwriting recognition systems. Incorrect line segmentation generates errors in later phases, leading to total changes in the meaning of the text. Line segmentation in printed Arabic documents is an easier task compared to handwritten ones. Segmentation of lines from Arabic handwriting is difficult due to many challenging issues including overlaps, touches, skew/tilt in lines, diacritics, etc. To implement this phase, this paper proposes a robust approach (A2) that is based on a multi-agent system and Convolutional Neural Networks (CNN). The proposed approach is an enhanced version of the approach proposed in [1] (A1). In A1, the agents rely mainly on a morphological analysis algorithm to segment lines. Instead of that, the new approach adapts the External Features-based CNN (EFNet) architecture [2] inside the multi-agent system in order to enhance the performance of the segmentation algorithm. In terms of segmentation score, A2 brought better results compared to A1 on two benchmark databases (KHATT and HAPD). It also outperformed other recent methods in the literature.
Fuzzy logic driven security requirements engineering process

Security requirements are non-functional requirements which are employed to protect the information from unauthorized users. After the requirements elicitation process, a system may have several requirements; and practically it is not possible to provide security for all the requirements because of the budget and other constraints of an organization. So, it is important to identify the selected set of software requirements. Selection of software requirements based on different criteria like cost and security is a multi-criteria decision making (MCDM) problem in which different stakeholders are involved and they specify their preferences on software requirements using linguistic variables. Based on our review, we found that existing methods like multilateral, problem frame, goal oriented, and common criteria do not support MCDM methods for the identification of those requirements which need more security during the development process. Therefore, to address this issue, in this paper, we present a fuzzy logic driven security requirements engineering process. Finally, the proposed methodology is explained with the help of an example.
Instant Basketball Defensive Trajectory Generation

Tactic learning in virtual reality (VR) has been proven to be effective for basketball training. Endowed with the ability of generating virtual defenders in real time according to the movement of virtual offenders controlled by the user, a VR basketball training system can bring more immersive and realistic experiences for the trainee. In this article, an autoregressive generative model for instantly producing basketball defensive trajectory is introduced. We further focus on the issue of preserving the diversity of the generated trajectories. A differentiable sampling mechanism is adopted to learn the continuous Gaussian distribution of player position. Moreover, several heuristic loss functions based on the domain knowledge of basketball are designed to make the generated trajectories assemble real situations in basketball games. We compare the proposed method with the state-of-the-art works in terms of both objective and subjective manners. The objective manner compares the average position, velocity, and acceleration of the generated defensive trajectories with the real ones to evaluate the fidelity of the results. In addition, more high-level aspects such as the empty space for offender and the defensive pressure of the generated trajectory are also considered in the objective evaluation. As for the subjective manner, visual comparison questionnaires on the proposed and other methods are thoroughly conducted. The experimental results show that the proposed method can achieve better performance than previous basketball defensive trajectory generation works in terms of different evaluation metrics.
Exploring the Classification of Traditional Chinese Bibliographies through Interactive Visualization

Traditional Chinese bibliographies can be regarded as the earliest forms of knowledge organization system in China, which embed rich academic value and can reflect the knowledge, cultural, and political status at that time. This study adopts the digital humanities approach to investigate the bibliographies in a data-driven manner. We select seven representative traditional Chinese bibliographies and produced structured data by semiautomated methods. Interactive visualization is used as the main technique to provide both quantitative and qualitative view for humanities scholars to discover patterns, trends and anomalies in the change of the classification schemes of the traditional bibliographies. Examples are given to manifest how this interactive visualization can act as a tool to facilitate humanities research and provoke scholars thinking. We discuss the potential of the visualization technique to be used for studying traditional Chinese bibliographies and the embedded sociocultural implications.
Improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>Q</mi> </math>-Learning Method for Multirobot Formation and Path Planning with Concave Obstacles

Aiming at the formation and path planning of multirobot systems in an unknown environment, a path planning method for multirobot formation based on improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>Q</mi> </math> -learning is proposed. Based on the leader-following approach, the leader robot uses an improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>Q</mi> </math> -learning algorithm to plan the path and the follower robot achieves a tracking strategy of gravitational potential field (GPF) by designing a cost function to select actions. Specifically, to improve the Q-learning, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>Q</mi> </math> -value is initialized by environmental guidance of the target’s GPF. Then, the virtual obstacle-filling avoidance strategy is presented to fill non-obstacles which is judged to tend to concave obstacles with virtual obstacles. Besides, the simulated annealing (SA) algorithm whose controlling temperature is adjusted in real time according to the learning situation of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>Q</mi> </math> -learning is applied to improve the action selection strategy. The experimental results show that the improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>Q</mi> </math> -learning algorithm reduces the convergence time by 89.9% and the number of convergence rounds by 63.4% compared with the traditional algorithm. With the help of the method, multiple robots have a clear division of labor and quickly plan a globally optimized formation path in a completely unknown environment.
AGQFL: Communication-efficient Federated Learning via Automatic Gradient Quantization in Edge Heterogeneous Systems

With the widespread use of artificial intelligent (AI) applications and dramatic growth in data volumes from edge devices, there are currently many works that place the training of AI models onto edge devices. The state-of-the-art edge training framework, federated learning (FL), requires to transfer of a large amount of data between edge devices and the central server, which causes heavy communication overhead. To alleviate the communication overhead, gradient compression techniques are widely used. However, the bandwidth of the edge devices is usually different, causing communication heterogeneity. Existing gradient compression techniques usually adopt a fixed compression rate and do not take the straggler problem caused by the communication heterogeneity into account. To address these issues, we propose AGQFL, an automatic gradient quantization method consisting of three modules: quantization indicator module, quantization strategy module and quantization optimizer module. The quantization indicator module automatically determines the adjustment direction of quantization precision by measuring the convergence ability of the current model. Following the indicator and the physical bandwidth of each node, the quantization strategy module adjusts the quantization precision at run-time. Furthermore, the quantization optimizer module designs a new optimizer to reduce the training bias and eliminate the instability during the training process. Experimental results show that AGQFL can greatly speed up the training process in edge AI systems while maintaining or even improving model accuracy.
Forming Linguistic Students’ Research Competence on the Basis of a Practice-Oriented Approach

The paper aims to reveal essence of the notion “linguistic research competence”. The article considers the characteristics of research competence, examines the process of research competence formation among linguistic students on the basis of a practice-oriented approach. Scientific originality of the study lies in the fact that relying on a survey conducted among foreign students studying English as a language of international communication and Russian as a foreign language the authors identify specific features of linguistic research competence, reveal the aspects of research competence formation taking into account its components: motivational, informational, cognitive (communicative), activity-based, reflexive, creative, personal. As a result, it is shown that focus group method allows representing research competence characteristics and provides deeper understanding of research competence formation.
An Influence of User’s Avatar Display on Conversation in Web Conferencing

Due to the COVID-19 epidemic, the use of web conferencing systems has become widespread but many people are not likely to send their own camera images. Muting the camera is usable to hide a participant but other participants can not aware the hided participant’s non-verbal information. The purpose of this study is to investigate how the ease of conversation is affected when the participants are displayed as avatars instead of actual images in a web conferencing system. For the purpose, we investigated whether the fidelity of participant’s image influence on the ease of conversation experimentally. We used four kinds of participant’s image, they were default still image, avatar image, point image, and real image. Experimental result showed the possibility that the avatar facial image reduced the participant’s embarrassment. Some participants felt that the system using avatar or point image improved the ease of conversation compared to the system using default non-moving image or real image.