Reviewerly/exHarmony huggingface dataset:
A Service-Aware Autoscaling Strategy for Container Orchestration Platforms with Soft Resource Isolation

Container orchestration platforms like Kubernetes (K8s) allow easy deployment and management of cloud native services. When deploying their services, service providers need to specify a proper amount of resources to K8s, so that the desired Quality of Service (QoS) to their users can be maintained. To cope with the varying traffic demand coming from users, they can rely on the K8s Horizontal Pod Autoscaling (HPA) mechanism. To ensure that enough resources are available when needed, the standard HPA mechanism relies on resource overprovisioning. In this way, the required QoS is achieved most of (or all) the time but at the expense of additional resources that are allocated (and charged for), while they may stay idle for significant periods of time. A way to reduce overprovisioning is provided by the soft resource isolation of K8s, which allows services to compensate for a temporary lack of resources with shared resources, i.e., idle resources of the machines where services are running. However, during traffic spikes, these idle resources may not be enough to serve the whole demand, degrading the QoS. The HPA, which is not aware of how much demand could not be served, is not always able to correctly estimate the required additional resources, further degrading the QoS. To overcome this, service providers need to leverage overprovisioning, limiting the use of shared resources. In this paper, we propose a novel mechanism for autoscaling resources in K8s that relies on service-related data to avoid the additional degradation introduced by the HPA. The proposed strategy also offers a way to tune overprovisioning and shared resources. Simulation results show that our approach can reduce idle resources by up to 60% compared with the HPA mechanism.
Collective Decision for Open Set Recognition (Extended Abstract)

In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances are collectively coming in batch. Recognizers in decision either reject or categorize them to some known class using empirically-set threshold. Thus the decision threshold plays a key role. However, the selection for it usually depends on the knowledge of known classes, inevitably incurring risks due to lacking available information from unknown classes. On the other hand, a more realistic OSR system should NOT just rest on a reject decision but should go further, especially for discovering the hidden unknown classes among the reject instances, whereas existing OSR methods do not pay special attention. In this paper, we introduce a novel collective/batch decision strategy with an aim to extend existing OSR for new class discovery while considering correlations among the testing instances. Specifically, a collective decision-based OSR framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet process (HDP). Thanks to HDP, our CD-OSR does not need to define the decision threshold and can implement the open set recognition and new class discovery simultaneously. Finally, extensive experiments on benchmark datasets indicate the validity of CD-OSR.
Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition (Extended Abstract)

Remote sensing (RS) images are frequently observed from multiviews. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle the multiview RS recognition problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Furthermore, we also develop a Multiple Scale version of TCCANet, i.e., MS-TCCANet, to extract enriched representation of the RS data by incorporating all previous convolutional layers. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition.
Context-aware Service Recommendation based on Knowledge Graph Embedding (Extended Abstract)

As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, w.r.t. their context requirements (e.g., invocation time, location, social profiles, connectivity). However, current CASR lacks a rich context modelling and does not allow for multi-relational interactions between users and services in different contexts. We propose a context-sensitive service recommendation, by constructing a contextual service knowledge graph (C-SKG), which we translated into a low-dimensional vector space to facilitate its processing. Dilated Recurrent Neural Networks are applied to allow a context-aware C-SKG embedding, based on the principles of subgraph-aware proximity. A recommendation algorithm, finally, returns the top-rated services w.r.t. the target user’s context and the proximity degrees.
Innovation And Experiential Learning In Academic Libraries: Meeting The Needs Of Today’s Students

"Innovation And Experiential Learning In Academic Libraries: Meeting The Needs Of Today’s Students." Technical Services Quarterly, 40(3), pp. 247–248
Market Analysis System Based on Apriori Algorithm

In this paper, we use the Apriori algorithm as the core idea to establish a highly reusable and high-degree-of-freedom human factor correlation analysis model to better guide the development of the ice and snow sports market. We first normalized the descriptive statistics data, and then constructed a correlation degree calculation method between frequent itemset through the relational structure of the database. Finally, the data mining of key content is carried out by generating significant association rules, and the results of statistical tests prove that the model has strong analysis ability of association degree.
Enhancement of the phase sensitivity with two-mode squeezed coherent state based on a Mach-Zehnder interferometer

We theoretically study the phase estimation based on a Mach-Zehnder interferometer (MZI) with a two-mode squeezed coherent state. By maximizing the quantum Fisher information, we find that the quantum Cramér-Rao bounds (QCRB) can reach sub-Heisenberg limit under the phase-matched condition. The optimal phase sensitivity can reach the sub-shot noise limit (SNL) and approach the QCRB by employing the intensity difference detection. Meanwhile, compared with the MZI fed with a coherent plus a single-mode squeezed vacuum state, this scheme can have better performance by adjusting the squeezing parameter and the mean photon number. With the same parameter, our scheme shows more sensitive phase measurement than the SU(1,1) interferometer with a coherent plus a vacuum state. We also show that the phase sensitivity of our proposal can still reach the SNL when the loss of the photon is 36%. This scheme can provide potential applications in optical sensors.
The Role of Institutional Quality in Financial Development: An Approach towards Emerging Economies

This chapter explores the role and importance of institutional quality in promoting financial development in emerging economies. Institutional quality and financial development are important pillars of sustainable economic growth. The quality of these institutions, which serve as the primary foundations of financial development, is one of the key factors influencing their performance. Socioeconomic, cultural, religious, and corrupt factors are all included in the definition of institutional quality, along with political stability, government effectiveness, the voice of accountability and transparency, as well as internal and external conflicts and their resolutions. The dataset puts together 20 years of annual data between 1999 and 2019 for 26 emerging countries. It draws on a number of data sources: The World Bank FinStats (Feyen, Kibuuka, and Sourrouille, 2014), IMF’s Financial Access Survey, World Governance Indicators, and Bank for International Settlement (BIS) debt securities database We used GMM and 2SLS methods to analyze the behavior of the components of institutional quality including political stability, regulatory quality, rule of law, control over corruption, government effectiveness, and voice and accountability in influencing financial development. It is revealed that there is a direct correlation between financial development and institutional quality. Well-functioning institutions have the capability to promote financial development. Improved institutional standards and governance frameworks have contributed to high rates of financial development in several nations. The components of institutional quality can be improved unit for unit, and this can have a direct and significant effect on financial development. This suggests that stable financial growth is a result of robust institutions. The relationship between the quality of institutions and financial development is unparalleled that emerging markets need to address the issues of the different components such as political stability, regulatory quality, rule of law, control over corruption, government effectiveness, and voice and accountability.
An overview of artificial intelligence applications for next-generation gynaecological pathology

With the drive to roll out digital pathology in the UK, implementation of Artificial intelligence (AI) tools for pathology is now a possibility, bringing with it the potential to change how we work as a specialty.AI promises many benefits for working practices such as improved efficiency and consistency, financial and productivity gains and ultimately a better service for our patients.Gynaecological pathology is a diverse specialty with many potential avenues for algorithm development, yet there are relatively few nearing clinical validation compared to other pathology specialties.This article provides a summary of the current landscape of AI in pathology with a focus on applications in gynaecological pathology.We discuss the ways pathologists can be involved in algorithm development and draw on our significant experiences in a nationally-funded programme for AI development and research.Finally we look to what the future might hold.
Usability of Pre-trained Diffusion Models in Generating Novel Datasets and Its Performance Evaluation

Even though sophisticated deep learning methods are getting better and better day by day, still they rely on a large number of datasets. But it is not always possible to acquire large datasets for all kinds of problems. Though diffusion models are now popular for their creative applications, it is already proven that they can generate better realistic-looking synthetic images compared to Generative Adversarial Networks (GAN). GANs are a popular option for image synthesis that helps the data sampling process for datasets that have low amounts of data or imbalanced data. In our work, we have experimented with a pre-trained text-to-image generation diffusion model for generating datasets for two different classes of problems. These problems are two common problems that can get benefitted from deep learning-based solutions but the lack of datasets hampers the process. We used the diffusion model to generate synthetic images and used those images as the training and validation data for the problems we tried to solve. Then we tested the models with manually collected real-world data and demonstrated the performance of such a method comparatively. From our experiments, we found that the diffusion model can generate realistic images and is up to 50 times faster in data generation compared to the manual human process. Also, in our testing, we found that the Convolutional Neural Networks trained with these synthetic data can achieve up to 80% and 89% accuracy scores.
Image Defogging Method based on Multi-Scale and Frequency Domain Features

Due to the existence of atmospheric particles in the air (such as dust, colloids, raindrops, etc.), the quality of images taken in outdoor scenes is usually poor, and the visual effect will decline a lot. The phenomena caused by the absorption and refraction of atmospheric light by these particles include fog, smoke, etc. Images of these scenes usually show brightness increase and contrast decrease, which are not conducive to the identification and extraction of image features and increase the difficulty of subsequent image processing. In order to solve this problem, this paper proposes an image defogging method based on a deep neural network, which employs convolutions, multi-scale feature attention mechanism and frequency domain feature extraction to extract and fuse features of different scales. The details of the image are further restored by using the details extracted from the high-frequency components of the image. In order to verify the performance of the proposed method, experiments are conducted on RESIDE datasets. The experiments show that the model is superior to other models in terms of peak signal to noise ratio, structural similarity and subjective performance.
Semantics and Scheduling for Machine Knitting Compilers

Machine knitting is a well-established fabrication technique for complex soft objects, and both companies and researchers have developed tools for generating machine knitting patterns. However, existing representations for machine knitted objects are incomplete (do not cover the complete domain of machine knittable objects) or overly specific (do not account for symmetries and equivalences among knitting instruction sequences). This makes it difficult to define correctness in machine knitting, let alone verify the correctness of a given program or program transformation. The major contribution of this work is a formal semantics for knitout, a low-level Domain Specific Language for knitting machines. We accomplish this by using what we call the fenced tangle , which extends concepts from knot theory to allow for a mathematical definition of knitting program equivalence that matches the intuition behind knit objects. Finally, using this formal representation, we prove the correctness of a sequence of rewrite rules; and demonstrate how these rewrite rules can form the foundation for higher-level tasks such as compiling a program for a specific machine and optimizing for time/reliability, all while provably generating the same knit object under our proposed semantics. By establishing formal definitions of correctness, this work provides a strong foundation for compiling and optimizing knit programs.
Developing a fast, reproducible, and simple protocol for virtual lichen herbarium using barcoding and QR code techniques

In recent days, biological specimens are digitalized and digital images are available in virtual herbarium for teaching and learning process. Now, there is a need to explore possibilities of usage of barcodes and quick response (QR) codes in developing virtual herbarium for quick access as well as study the taxonomy of repository specimens. In order to establish a virtual herbarium for lichens using barcode and QR code techniques, lichen specimens such as Chrysothrix candelaris (L.) J.R.Laundon, Leucodermia leucomelos (L.) Kalb, Heterodernia flabellate (fee) D.D.Awasthi, Parmotrema andinum (Mull.Arg.) Hale, Parmotrema grayanum (Hue) Hale, Parmellinella stuppeum (Taylor) Hale, and Ramalina intermedia (Delise ex Nyl.) Nyl. were collected from the Eastern Ghats and the Western Ghats of Tamil Nadu, India and were identified based on morphological, anatomical and biochemical methods. Moreover, these specimens were preserved in the conventional lichen herbarium as reference materials for future studies. The barcodes and QR codes were generated for all the repository specimens to access the materials as well as to get a complete description of the lichen specimens. The generated barcodes provided the binomial name of lichen specimens along with their accession number. Similarly, the QR codes provided the digital image of lichen specimens along with complete descriptions such as distribution, habit and habitat, growth forms, name of the family, reproductive structure, chemistry, nature of thallus structure and lichen secondary metabolites. From these studies, we standardized a simple, rapid with reproducible protocol to develop a virtual herbarium for lichens to get the digital image and to access the complete descriptions of lichen specimens. This study might be useful for Lichenologists to get information about lichens in digital form and to maintain the lichen wealth for future regenerations without disturbing the lichen biodiversity.
Adversarial Attack-Based Robustness Evaluation for Trustworthy AI

Artificial Intelligence (AI) technology has been extensively researched in various fields, including the field of malware detection. AI models must be trustworthy to introduce AI systems into critical decision-making and resource protection roles. The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI. Although various adversarial attack and defense methods are actively being studied, there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks. An AI model’s robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and recall. Additional evaluation indicators are necessary to evaluate the robustness of AI models against adversarial attacks. In this paper, a Sophisticated Adversarial Robustness Score (SARS) is proposed for AI model robustness evaluation. SARS uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation process. This evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation indicators. Moreover, the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial attacks. This paper proposed using SARS, calculated based on adversarial attacks, to identify data groups with robustness vulnerability and improve robustness through adversarial training. Through SARS, it is possible to evaluate the level of robustness, which can help developers identify areas for improvement. To validate the proposed method, experiments were conducted using a malware dataset. Through adversarial training, it was confirmed that SARS increased by 70.59%, and the recall reduction rate improved by 64.96%. Through SARS, it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data types. In addition, it is expected that improved models can be achieved by improving resistance to adversarial attacks via methods such as adversarial training.
An Innovative Technique for Constructing Highly Non-Linear Components of Block Cipher for Data Security against Cyber Attacks

The rapid advancement of data in web-based communication has created one of the biggest issues concerning the security of data carried over the internet from unauthorized access. To improve data security, modern cryptosystems use substitution-boxes. Nowadays, data privacy has become a key concern for consumers who transfer sensitive data from one place to another. To address these problems, many companies rely on cryptographic techniques to secure data from illegal activities and assaults. Among these cryptographic approaches, AES is a well-known algorithm that transforms plain text into cipher text by employing substitution box (S-box). The S-box disguises the relationship between cipher text and the key to guard against cipher attacks. The security of a cipher using an S-box depends on the cryptographic strength of the respective S-box. Therefore, various researchers have employed different techniques to construct high order non-linear S-box. This paper provides a novel approach for evolving S-boxes using coset graphs for the action of the alternating group over the finite field and the symmetric group . The motivation for this work is to study the symmetric group and coset graphs. The authors have performed various analyses against conventional security criteria such as nonlinearity, differential uniformity, linear probability, the bit independence criterion, and the strict avalanche criterion to determine its high cryptographic strength. To evaluate its image application performance, the proposed S-box is also used to encrypt digital images. The performance and comparison analyses show that the suggested S-box can secure data against cyber-attacks.
Optimizing Region of Interest Selection for Effective Embedding in Video Steganography Based on Genetic Algorithms

With the widespread use of the internet, there is an increasing need to ensure the security and privacy of transmitted data. This has led to an intensified focus on the study of video steganography, which is a technique that hides data within a video cover to avoid detection. The effectiveness of any steganography method depends on its ability to embed data without altering the original video’s quality while maintaining high efficiency. This paper proposes a new method to video steganography, which involves utilizing a Genetic Algorithm (GA) for identifying the Region of Interest (ROI) in the cover video. The ROI is the area in the video that is the most suitable for data embedding. The secret data is encrypted using the Advanced Encryption Standard (AES), which is a widely accepted encryption standard, before being embedded into the cover video, utilizing up to 10% of the cover video. This process ensures the security and confidentiality of the embedded data. The performance metrics for assessing the proposed method are the Peak Signal-to-Noise Ratio (PSNR) and the encoding and decoding time. The results show that the proposed method has a high embedding capacity and efficiency, with a PSNR ranging between 64 and 75 dBs, which indicates that the embedded data is almost indistinguishable from the original video. Additionally, the method can encode and decode data quickly, making it efficient for real-time applications.