Reviewerly/exHarmony huggingface dataset:
Modeling content-attribute preference for personalized image esthetics assessment

Personalized image esthetics assessment (PIAA) models user's personalized esthetic preference and predicts a unique esthetic score for the user, which has become a hot topic due to its usefulness in social media, album curation, etc. People's esthetic preference is typically determined by a mixture of diversified factors, which makes it extremely challenging to model. Intuitively, people with different personalities have distinct preferences for various photographic contents, while image content and esthetic attributes are always tightly coupled. Motivated by this, this paper presents a content-attribute preference (CAP) framework for PIAA, which models the intricate relationship between user personality and image “content-attribute” coupling features. Specifically, a multi-task learning network is utilized to extract “content-attribute” coupling features, and another parallel network is used to extract personality features. Then, a personalized esthetic prior model is trained to represent the content-attribute preferences of people with different personalities based on the cross attention mechanism. Finally, a PIAA model is obtained by fine-tuning the esthetic prior model using target user data. Extensive experiments on three public databases demonstrate that the proposed CAP model outperforms the state-of-the-arts in terms of both prediction performance and generalization ability.
Blur Classification and Estimation of Motion Blur Parameters Using OLR

The atmospheric conditions impair the quality of underwater pictures. Motion blur induced by the imaging device or the movement of the object is one of the most significant issues in underwater images. With the Artificial Intelligence and Robotics industries skyrocketing and dominating the market lately, researchers have utilized these technologies to explore and research beyond and beneath the surface of the earth and one of them being underwater image analysis and processing. But the process of underwater imaging is very complex with a lot of challenges ahead needed to be tackled. Parameters of the blurred picture must be determined to fix this in post-processing. As a result, the characteristics estimate the point spread function based on the image's spectrum. After measuring the angle and length of motion blurred pictures, the Optimized Linear Regression (OLR) approach is used to increase the parameter estimation accuracy. The data was first gathered and analyzed in real-time settings in Chennai. For underwater pictures, the suggested OLR approach outperforms the current traditional Cepstral, Hough, and Radon transform estimation methods.
A Systematic Study of Nature Inspired Algorithms

The nature-inspired algorithms are a set of algorithms widely used to solve problems that are inspired by natural objects. This paper presents the review of different nature-inspired algorithms like Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Bat Algorithm (BA), Salp Swarm Algorithm, hybrid PSO-WOA algorithm, and hybrid WOA-BA algorithm. The contribution of this paper is two-fold: we provided the details of nature-inspired algorithms and real-world applications; research gaps and comparative study of past research work have also been presented in this survey paper in order to provide a roadmap to the researchers who want to work in the same field.
Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures

Automatic grouping (clustering) involves dividing a set of objects into subsets (groups) so that the objects from one subset are more similar to each other than to the objects from other subsets according to some criterion. Kohonen neural networks are a class of artificial neural networks, the main element of which is a layer of adaptive linear adders, operating on the principle of “winner takes all”. One of the advantages of Kohonen networks is their ability of online clustering. Greedy agglomerative procedures in clustering consistently improve the result in some neighborhood of a known solution, choosing as the next solution the option that provides the least increase in the objective function. Algorithms using the agglomerative greedy heuristics demonstrate precise and stable results for a k-means model. In our study, we propose a greedy agglomerative heuristic algorithm based on a Kohonen neural network with distance measure variations to cluster industrial products. Computational experiments demonstrate the comparative efficiency and accuracy of using the greedy agglomerative heuristic in the problem of grouping of industrial products into homogeneous production batches.
Pengembangan Bahan Ajar Menggunakan Pendekatan Realistic Mathematic Education (RME) Dengan Sistem Flipped Classroom Bagi Siswa Kelas V Sekolah Dasar

The purpose of this research is to develop speed teaching materials with the Flipeed Classroom system using a Realstic Mathematical Education approach. The research method used is the ADDIE model including: analysis, design, development, implementation, and evaluation. The teaching materials developed were analyzed by a team of experts. The research subjects were students of class V. Practically, teaching materials were seen from the content and objectives that were clear, easy to read, looked attractive and could increase the attractiveness/interest of students to learn. The effectiveness of teaching materials is seen from the increase in student learning outcomes with a learning completeness rate of 96%. Based on these results, it can be concluded that speed teaching materials using a realistic mathematical education (RME) approach with the Flipped Classroom system developed for fifth grade elementary school students are declared valid, practical, and effective.
Performance of Domain Adaptation Schemes in Video Action Recognition using Synthetic Data

research-article Share on Performance of Domain Adaptation Schemes in Video Action Recognition using Synthetic Data Authors: Hana Isoi Ochanomizu University, Japan Ochanomizu University, JapanView Profile , Atsuko Takefusa National Institute of Informatics, Japan National Institute of Informatics, JapanView Profile , Hidemoto Nakada National Institute of Advanced Industrial Science and Technology, Japan National Institute of Advanced Industrial Science and Technology, JapanView Profile , Masato Oguchi Ochanomizu University, Japan Ochanomizu University, JapanView Profile Authors Info & Claims IVSP 2022: 2022 4th International Conference on Image, Video and Signal ProcessingMarch 2022 Pages 70–79https://doi.org/10.1145/3531232.3531242Published:01 June 2022Publication History 0citation24DownloadsMetricsTotal Citations0Total Downloads24Last 12 Months24Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Four Poems from The Blue of Cancellation by Maria Attanasio

Translation of selected poems by the Sicilian woman poet Marie Attanasio, with an emphasis on the presence of African immigrants in contemporary Sicily.
Understanding, Visualizing and Explaining XAI Through Case Studies

Data-driven AI applications require Accountability, Responsibility, and Transparency. At the same time,users' expectations on the accuracy and precision of the predictions are unreasonable. This draws attention to - explainability, an inherent limitation in the traditional intelligent systems that are black box in nature. As Explainable AI(XAI) seeks to bridge this gap through the explanation of AI models, this paper focuses on the study of achieving Responsible AI through XAI. AIX360 is a robust popular tool that is used to provide explainability from various viewpoints. Hence, this work uses AIX 360 to bring explainability in the viewpoint of various stakeholders namely domain experts, service providers and customers. The uniqueness of the paper lies in designing a conceptual model for XAI based predictive analysis. This paper is an attempt to understand, visualize and explain the observations through two data-intensive intelligence systems as case studies HELOC(Home Equity Line Of Credit) and ISIC(International Skin Imaging Collaboration).
Research and Implementation of Recognition Algorithm of Long-distance Runners Based on Deep Learning

The recognition of long-distance runners is mainly used to retrieve specific long-distance runner targets across video equipment in long-distance running events, which helps to improve the efficiency of the management of long-distance running events. At present, with the rapid development of artificial intelligence, lots of scholars utilize deep learning-based Person Re-Identification(Re-ID) technology to achieve long-distance runner recognition tasks. However, in practical applications, problems such as occlusion, noise, brightness changes, and color shifts usually affect the collected images of long-distance runners, thereby reducing the recognition accuracy of the existing Re-ID technology. For this reason, this paper proposes a recognition network for long-distance runners named Ldrr-net based on deep learning.Ldrr-net introduces the IGBN structure into the backbone network called Resnet50, which can reduce the adverse effects caused by the captured images, and has stronger robustness. In addition, we modify the loss, and propose Ldrr-loss to train network parameters, so that the network can better achieve intra-class aggregation and inter-class separation in the case of occlusion and similar features, and further improve the accuracy of long-distance runners' recognition. Experiments show that Ldrr-net has certain advantages in the recognition task of long-distance runners.
Utilization of Infographics for Breast Cancer Awareness: Another Look

Breast cancer is the most frequent malignancy among women worldwide.It affects about one in every eight women at some point in their lives.In the U.S., women younger than forty-five years old account for about nine percent of all affected individuals.Breast cancer in young females is more likely to be inherited than breast cancer in aged females, and if it is diagnosed late, there will be progression of the disease, and it will be more invasive and harder to treat.Several young women are unaware of their breast cancer risk or how to reduce it.For this, more awareness of the disease is needed.Towards this, in this research, utilization of infographics is reviewed.It compares various infographics used for breast cancer awareness.It is also of practical interest to evaluate the parameters and factors that can improve the effectiveness of the breast cancer infographics and know why we need to design a new infographic for breast cancer and what the characteristics should be.Hence, the current study suggested designing an infographic related to the early diagnosis of breast cancer awareness.The early diagnosis-based breast cancer infographic will educate and/or enhance their previous knowledge the women, especially those at higher risk, about examining the symptoms, like lump formation, excessive secretions from the nipples, and pain.The novel infographic should be a combination of motion and static infographics (such as interactive infographics) that will present the breast examination video from time to time in a short video clip with background audio.The pink or blue color scheme, pictures, icons, logos, and fewer words should be used to design a new infographic.As a result, the current study focuses on the potential for increasing health awareness about breast cancer to influence early diagnostic strategies adherence favorably.
Transformer Based End-to-End Speech Recognition with Linear Attention

Transformer models with dot-product attention have made great progress in speech recognition task in recent years. However, it suffers from the drawback of slow training speed because its memory and computational costs grow quadratically with the length of the input feature. In this paper, we apply the linear transformer to automatic speech recognition task which reduces its memory and computational complexity. To evaluate the effectiveness of linear attention based speech transformer, we conduct comparison experiments on the AISHELL-1 which is an open-source Mandarin speech corpus. Evaluated on AISHELL-1 speech recognition dataset, our best model achieves character error rate (CER) of 11.9%, while the whole training process only takes 21 hours on 1 GPU, significantly faster than Speech-transformer models.
An Analysis of Josa and Eomi in Translating Korean TV Dramas Into English With Artificial Intelligence

The goal of this study is to find out why the English subtitles of Korean TV dramas have frequent errors. It is anticipated that the findings would shed light on innovative ways for machine translation technology to agglutinate languages. To do this, as a first step, Korean-English subtitles were grammatically tagged according to the category part of speech (POS) to find out which POS has the most frequent errors in each language. Thirty-one groups were analyzed and categorized by tagging the part of speech. Then, for the Korean language, the Kokoma Korean morpheme analyzer was run to tag the Korean script according to the category noun, verb, adjective, etc. These were categorized into forty-five groups. This categorization included nine subsets of josa (postposition) and fourteen of eomi (ending), which are the most difficult parts of Korean to translate into English due to differences in linguistic structure. As a next step, the subtitles were scored and graded as the most corrected and the least corrected by Korean-American bilinguals. The results show that the most frequent error of josa is JX (auxiliary particle) among nine groups whereas the frequent error of eomi is EPT (tense prefinal ending).
COPA Use Case: Distributed Secure Joint Computation

Data centers provide good environments for distributed computing as they are easily accessible and may have low-latency communication between nodes [1] ; often, however, performance is limited by network bandwidth. These network bottlenecks drive the need for alternative communication resources to improve performance of large-scale applications. SmartNICs [2] – [4] have been introduced to perform the same tasks of standard NICs, but contain additional resources to allow for network function optimization with additional hardware. Adoption of SmartNICs continues to increase as a means to accelerate network functions and offload packet processing tasks away from CPU resources [5] – [13] .
Compression loss-based spatial-temporal attention module for compressed video quality enhancement

Recently, deep learning technology has achieved remarkable progress in compressed video quality enhancement. However, the existing methods fail to consider the fact that the regions with different compression losses contain varied effective information. To address this issue, this paper proposes a Compression Loss-based Spatial-Temporal Attention (CLSTA) module that predicts the compression loss-based attention of each pixel when restoring target frames. Based on this, the pixels with lower compression losses contribute more to restoring target frames. Meanwhile, the compression-domain information in the compressed video stream is exploited to overcome the difficulty in inferring compression loss-based attention directly from the pixel-domain. With a slight modification, the CLSTA module can be easily integrated into the existing methods for end-to-end training. A series of experiments have been conducted in this study to validate the effectiveness and generality of the CLSTA module for compressed video quality enhancement. By adding the CLSTA module to the state-of-the-art method STDF, the proposed method achieves an average PSNR improvement of 0.96 dB and a BD-rate reduction of 26.85% under the Low-Delay P configuration, which is respectively 0.06 dB and 1.83% better than the baseline STDF. In the best case, the proposed method obtains 0.19 dB higher PSNR than STDF for a single frame.
Karp: a language for NP reductions

In CS theory courses, NP reductions are a notorious source of pain for students and instructors alike. Invariably, students use pen and paper to write down reductions that “work” in many but not all cases. When instructors observe that a student’s reduction deviates from the expected one, they have to manually compute a counterexample that exposes the mistake. In other words, NP reductions are subtle yet, most of the time, unimplemented programs. And for a good reason: there exists no language tailored to NP reductions.
Protection of Critical Infrastructure Using an Integrated Cybersecurity Risk Management (i-CSRM) Framework

Risk management plays a vital role in tackling cyber threats within the cyber-physical system (CPS) for overall system resilience. It enables identifying critical assets, vulnerabilities, and threats and determining suitable proactive control measures to tackle the risks. However, due to the increased complexity of the CPS, cyber-attacks nowadays are more sophisticated and less predictable, which makes risk management task more challenging. This chapter proposes an integrated cyber security risk management (i-CSRM) framework for systematically identifying critical assets through the use of a decision support mechanism built on fuzzy set theory, predicting risk types through machine learning techniques, and assessing the effectiveness of existing controls through the use of comprehensive assessment model (CAM) parameters.
Extending a corpus for assessing the credibility of software practitioner blog articles using meta-knowledge

Practitioner written grey literature, such as blog articles, has value in software engineering research. Such articles provide insight into practice that is often not visible to research. However, a high quantity and varying quality are two major challenges in utilising such material. Quality is defined as an aggregate of a document’s relevance to the consumer and its credibility. Credibility is often assessed through a series of conceptual criteria that are specific to a particular user group. For researchers, previous work has found ‘argumentation’ and ‘evidence’ to be two important criteria.
Extractive Summarization of Scientific Articles

Scientific articles are continuously growing on a daily basis, which makes it difficult to keep up with the latest updates. Hence, this problem can be solved by summarizing these articles. The methodology used aims to summarize single documents and apply the extractive approach to generate the summary using GloVe as an embedding technique. This methodology was applied to the CL-SciSumm 2019 dataset with results of a 0.124465 F-score measure of ROUGE-2.
Public geoscience solutions for diversifying Canada's critical mineral production

Abstract Achieving net-zero carbon emissions goals will increasingly rely on critical mineral resources while simultaneously decreasing the extraction, processing and use of hydrocarbons as the primary provider of energy. Canada is well positioned to contribute to this effort through a series of innovative policy and research initiatives, and it is Canada's goal to be a stable supplier of critical minerals into the future. To this end, Natural Resources Canada and the Geological Survey of Canada invest financial resources into critical mineral research initiatives. This research aims to generate precompetitive baseline geological, geochemical and geophysical data for large, underexplored regions within Canada, whereas targeted studies focus on mineral systems science and improved exploration models for the large variety of critical mineral resources distributed throughout Canada. These research approaches can be combined, digitally, to generate mineral potential models. These ongoing efforts by the Geological Survey of Canada enhance the viability of Canada being (or maintaining its status as) a hub for critical mineral resource development and processing well into the future.
Secure and Policy-Compliant Query Processing on Heterogeneous Computational Storage Architectures

Computation Storage Architectures (CSA) are increasingly adopted in the cloud for near data processing, where the underlying storage devices/servers are now equipped with heterogeneous cores which enable computation offloading near to the data. While CSA is a promising high-performance architecture for the cloud, in general data analytics also presents significant data security and policy compliance (e.g., GDPR) challenges in untrusted cloud environments. In this paper, we present IronSafe, a secure and policy-compliant query processing system for heterogeneous computational storage architectures, while preserving the performance advantages of CSA in untrusted cloud environments. To achieve these design properties in a computing environment with heterogeneous host (x86) and storage system (ARM), we design and implement the entire hardware and software system stack from the ground-up leveraging hardware-assisted Trusted Execution Environments (TEEs): namely, Intel SGX and ARM TrustZone. More specifically, IronSafe builds on three core contributions: (1) a heterogeneous confidential computing framework for shielded execution with x86 and ARM TEEs and associated secure storage system for the untrusted storage medium; (2) a policy compliance monitor to provide a unified service for attestation and policy compliance; and (3) a declarative policy language and associated interpreter for concisely specifying and efficiently evaluating a rich set of polices. Our evaluation using the TPC-H SQL benchmark queries and GDPR anti-pattern use-cases shows that IronSafe is faster, on average by 2.3x than a host-only secure system, while providing strong security and policy-compliance properties.
Sharp behavioral changes in preemptible instance pricing

Alibaba Cloud was the second cloud provider to offer preemptible (spot) instances, and yet their price traces have never been analyzed. We analyzed thousands of price traces collected for over 3 years to find sharp and coordinated behavioral changes in the pricing.
Augmenting Biometric Authentication with Artificial Intelligence

We have already entered into an innovative epoch of most secure and fastest digitalized user authentication. This is achieved with the advent of numerous innovative breakthroughs in many fields. Amongst all the fields, major contributions have been driven from the area of Biometrics and Artificial Intelligence. The unique features and capabilities of Biometrics have already been an advent in the field of robust user authentication worldwide. Empowering biometrics with Artificial Intelligence techniques like Neural Networks, Fuzzy logic makes it really a boon in the area of User Authentication field. This paper gives knowledgeful insights into the field of Biometrics and pioneering contemporary works in the blend of Artificial Intelligence and Biometrics.